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Artificial Intelligence in Healthcare: 2021 Year in Review
Piyush Mathur MD, FCCM, FASA;Shreya Mishra MTech; Raghav Awasthi MSC;Ashish K.
Khanna MD, FCCP, FCCM, FASA; Kamal Maheshwari MD MPH; Francis A. Papay,
MS(BME), MD, FACS,FAAP; Amanda J. Naylor, MA.; Nour Abdallah, MD; Christopher J.
Weight, MD, MS; Anirban Bhattacharyya, MD, MPH; Avneesh Khare, MD,MBA; Gursimran
Singh MD, MS; Sandeep Reddy MBBS, PhD, MSc, FAcadmTM, CHIA; Jungwon Cha, BS.,
MS, PhD; Amit Anand, MD; Hoang Nguyen, MD; Animesh (Aashoo) Tandon, MD, MS;
Chaitanya Mamillapalli, MD; Nikita Pozdeyev, MD; Johnson Thomas, MD, FACE; Ghaith
Habboub, MD; John Lee MD; Srinivas R Mummadi, MD, MBI; Nathan Farrokhian BS, BA;
Andrés M. Bur, MD, FACS; Babu P Mohan MD; Jai Nahar, MD,MBA ; Sumit Sharma, MD;
Manmeet Ahluwalia, MD; B.Scott Segal, MD,MHCM; Shreya Saxena, MD, MPH ; Ty Vachon,
MD; Avirup Guha, MD, MPH, FACC, FICOS; Jacek B. Cywinski MD, FASA
Author Bios
BrainX and BrainX Community, February, 2022.( https://www.brainxai.org )
INTRODUCTION
The purpose of this synopsis is to provide a comprehensive review of publications related to
artificial intelligence (AI) applications in healthcare for the year 2021. We appreciate the work of
all the researchers and authors who have contributed to the advancement of AI in healthcare. Our
methodology has remained consistent over the past four years, which provides an opportunity for
comparative analysis of publications for each medical speciality, year over year.
The quality of publications, and the use of innovative AI technologies, continues to increase
amongst various specialities, although overall there was a decrease in the number of publications
in 2021 compared to 2020. Experts from their respective medical specialties have provided
editorial overview of the referenced publications and highlighted the research trends for the
speciality.
METHODOLOGY
We performed a PubMed search using the terms, “machine learning” or “artificial intelligence”
and “2021”, restricted to English language and human subject research as of December 31, 2021.
This search resulted in an initial pool of 4164 publications. These publications were then
reviewed individually and exclusions were made based on errors in the PubMed search results or
relevance of the publication for this review. A large number of the excluded publications (1982)
were either focused on robotic surgeries which did not have ML/AI context, certain gene studies
with limited clinical impact, non-human studies and short commentaries. 2182 publications were
finally selected, reviewed and categorized into one or more medical specialties. Articles with the
relevance to 2 or more specialties, overall <5%, were referenced in each corresponding
speciality. Majority of the drug discovery related publications and some of the review or editorial
articles were placed into the “General” category.
REVIEW
Compared to 2020, the initial PubMed search yielded a 30% decrease (4164 vs
5885) in the number of publications (Figure 1). After all exclusions, using criteria similar to the
last year, the number of publications remaining in our final review decreased by 32% (2182 vs
3232).We continued to maintain a separate speciality category for COVID-19, which included
134 publications this year.
Figure 1 . Number of publications for artificial intelligence in healthcare per year.
[Total(selected) = Publications selected after exclusions from initial Pubmed search; Excluded = publications
excluded based on exclusion criteria; Total (search results) = Publications based on Pubmed search]
SPECIALITY ABSTRACT INDEX
1
In 2021, there was an overall decrease in the number of publications across most specialities
compared to 2020 (Figure 2, Table 1 ).
Table 1 . Publications related to artificial intelligence in healthcare
[Total(selected) = Publications selected after exclusions from initial Pubmed search; Excluded = publications
excluded based on exclusion criteria; Total (search results) = Publications based on Pubmed search]
SPECIALITY ABSTRACT INDEX
2
Compared to the trends since 2018, most specialities still continue to see an increasing number of
publications with 2020 being an exceptional year ( Figure 3 ).
Figure 3 . Trends in number of AI in healthcare publications for the top 10 most published
specialities (2018 - 2021).
Once again, Oncology and Imaging continued to dominate in the number of publications related
to AI in healthcare. Gastroenterology demonstrated a significant, year over year increase, as
other specialities had a decline in the number of publications. Although, Imaging had a
significant decrease in publications, their publications showcased more mature models and
applications which are solving real world problems. Radiomics, which integrates non-imaging
data with imaging data to address problems associated with workflows, also experienced a
significant increase in the number of FDA approved algorithms. Oncology had a similar number
of publications compared to 2020 and continues with the trend towards increased use of
multimodal data including imaging and genomics. COVID-19 publications on the other hand,
experienced an overall decrease in the number of publications but the focus of publications on
radiology based AI models and reviews stayed strong.
SPECIALITY ABSTRACT INDEX
4
Gastroenterology continues to present advanced modeling using endoscopic image data to
predict the type of lesion or interventions. There is a trend towards expanding use of non-image
data such as text data and data from other sources for prediction modeling. Cardiology literature
also seems to be focused on use of imaging data from Echocardiography, CT scan images and
angiography images for development of machine learning models. Cardiology publications also
described an increasing utilization of multimodal data beyond electrocardiograms such as
genomics, wearable device data and digital biomarkers to create prediction models utilizing a
diverse set of techniques including blockchain.
Surgery as a speciality is seeing many areas of subspecialty application and growth of research.
Abdominal surgeries,head and neck cancer, dental imaging, vascular surgery and urology were
specific areas of significant growth in research and publications. Pathomics, Radiomics, image
analysis are all being researched for better surgical planning and outcomes. Pain management
and hypotension prediction research were the key areas of focus for Anesthesiology.
Management of conditions such as sepsis, delirium, ARDS and mechanical ventilation continued
to be worked upon in Critical Care. Pulmonary medicine focus areas for application of AI were
sleep medicine, obstructive lung disease and pulmonary tuberculosis.
Applications for prediction of Diabetes, management using device data and prediction of
complications continues to be the research focus amongst Endocrinology. Year 2021 also
expanded research in non-diabetes endocrinology disorders such as thyroid conditions, especially
thyroid nodule/malignancy management. Emergency Medicine publications were directed
towards risk stratification and managing challenges with data. Pediatric publications were very
diverse including pediatric neurologic disorders, head injury, neonatal sepsis and allergic
disorders amongst others.
Psychiatry and Behavioral disorder research and publications continue to utilize neural networks
and natural language processing to predict, diagnose and manage diverse conditions such as
depression, schizophrenia amongst other illnesses. Expanded use of AI in measuring treatment
response was a major area of research and demonstrates growing maturity in use of these
technologies in the field of behavioral sciences. Alzheimer's disease, Stroke and Seizure
management continue to be the focus areas for AI application research in Neurology utilizing
imaging data and EEG data.Ophthalmology,interestingly, saw increased focus on diagnosis and
prognostication,especially using autoML.
Pathology with digitization of images has been expanding focus in the areas of histopathology
and cell cytology analysis. There were many review articles which lay the foundation for future
research.
SPECIALITY ABSTRACT INDEX
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We noticed the number of publications decrease in the year 2021 compared to 2020. It might be
related to increased publications related to COVID-19 in journals, challenges with collaboration,
impact of COVID-19 on clinical research including delays in data collection,study
participation,study approvals,decreased resources including both clinician and non-clinician,
delays in submissions, editorial bias and increased review time.
LIMITATIONS
Search was limited to PubMed and with the restrictions mentioned in the methodology section. It
is possible that some significant studies or articles might have been missed. BrainX
Community’s “LEARN” ( https://www.brainxai.org/learn/ ) section provides an extensive
supplement to the review provided here.Also,while the majority of the specialities have specialist
abstracts,there were a few where only references are available.
CONCLUSION
Overall there continues to be a positive trend in increasing number and maturity of publications
related to AI in healthcare.Although there was a decrease in number of publications in the year
2021, possibly related to the ongoing COVID-19 pandemic, overall growth trend is still
preserved.Some specialities such as Oncology,Imaging,Gastroenterology,Cardiology are leading
with AI applications undergoing prospective randomized multicenter trial and FDA approvals.
SPECIALITY ABSTRACT INDEX
Administrative/Quality Improvement ……………………………………………………. 7
Anesthesiology ……………………………………………………………………………13
Cardiovascular Medicine ……………………………………………………………….... 16
COVID -19 ………………………………………………….………………………........ 25
Critical Care ………………………………………………….………………………....... 35
Dermatology ………………………………………………….………………………...... 44
Education ………………………………………………….………………………........... 47
Emergency Medicine ………………………………………………….…………………. 50
Endocrinology ………………………………………………….………………………....51
Gastroenterology ……………………………………………………………………….... 55
General ………………………………………………….……………………….............. 69
Genetics ………………………………………………….………………………............. 98
Head & Neck/Dental …………………………………………………………………….. 104
SPECIALITY ABSTRACT INDEX
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Nephrology ………………………………………………….………………………........ 107
Neurology ………………………………………………….……………………….......... 109
Obstetrics/Gynaecology/Reproductive Medicine …………………………………………116
Oncology ………………………………………………….………………………........... 117
Ophthalmology ………………………………………………….………………………...132
Orthopedics/Rheumatology ……………………………………………………………….138
Pathology ………………………………………………….………………………........... 139
Pediatrics ………………………………………………….………………………........... 144
Physiotherapy/Rehabilitation Medicine …………………………………………………. 148
Psychiatry and Behavioral Sciences ………………………………………………………149
Pulmonary ………………………………………………….……………………….......... 155
Radiology ………………………………………………….………………………........... 158
Surgery …………………………………………………………………………………… 180
SPECIALITY ABSTRACTS
Administrative/Quality Improvement
Avneesh Khare, MD, MBA
The domain of administration/ quality improvement in healthcare continues to see increasingly
diverse applications of Artificial Intelligence (AI) and Machine Learning (ML). The research
focus seems to have progressed beyond individual studies, as evidenced by the fact that the list
of publications for this year contains many review articles.
Apart from generating insight by analyzing volumes of unstructured free-text data related to
patient experience feedback, Natural Language Processing (NLP) has demonstrated useful
applications in automated conversational agents (chatbots) for follow up of patients after
physical health interventions, and automated identification and classification of Social Work
(SW) interventions documented in electronic health records to facilitate managerial decisions
related to SW staffing, resource allocation, and patients' social needs. NLP can be used to extract
data about social determinants of health from narrative clinical notes, which in turn can aid in the
development of screening tools, risk prediction models, and Clinical Decision Support Systems
(CDSSs). Speech-based healthcare solutions employing automatic speech recognition, speech
synthesis (text to speech), and health detection and monitoring using speech signals offer
unprecedented opportunities for transforming the healthcare industry.
A notable theme for this year has been identification of the existing challenges, and exploration
of solutions for enhanced assessment and adoption of technology in real world environment.
SPECIALITY ABSTRACT INDEX
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Focus areas include stakeholders’ views and attitudes towards clinical AI, importance of
reproducibility to ensure reliable clinical use, clinical validation and approval of AI and
ML-based medical devices, etc. Prevalence and nature of clinical expert involvement in the
development, evaluation, and implementation of CDSSs has also been discussed. One of the
articles provides 10 practical tips for success of AI in clinical environment, such as building a
collaborative science team, engaging frequently with end user, presenting a balanced view of
ethical challenges, investing in data science training for health professionals, understanding the
data, using the right algorithms, etc.
Some articles focus on improving patient safety by leveraging AI for prediction, prevention, or
early detection of adverse drug events, decompensation, and diagnostic errors. Few articles are
related to prevention and early detection of patient fall in hospital, prediction of avoidable
readmissions or hospital length of stay, enhanced malnutrition screening in acute care facilities,
and better clinical decision making in multimorbidity by effective use of AI/ ML.
Other areas include AI-enabled patient decision aids, optimisation of workflow by use of
electronic emergency triage and patient priority systems, pandemic preparedness and response
including digital detection surveillance systems, effective modeling and analysis of health care
staff security practices, data anonymization, and ML-based predictive analytic tools focused on
improving cost effectiveness, equity, efficiency, outcomes and quality in healthcare.
Methods used for machine learning models include Bayesian network, K-nearest neighbor,
logistic regression, support vector machines, neural networks, decision trees and random forests.
There seems to be a huge scope for improving administration/ quality in healthcare by use of AI/
ML. The greatest impact is expected in areas where current strategies are not effective, and
integration and complex analysis of novel, unstructured data are necessary to make accurate
predictions.
Article of choice: Wilson A, Saeed H, Pringle C, Eleftheriou I, Bromiley PA, Brass A. Artificial
intelligence projects in healthcare: 10 practical tips for success in a clinical environment. BMJ
Health Care Inform. 2021;28(1)
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Anesthesiology
B.Scott Segal, MD,MHCM
There were 18 anesthesiology related AI/ML publications identified in this year’s review, a sharp
decrease from the 38 identified last year. This is likely an underestimate, as multiple papers on
airway management/prediction, COVID-19, critical care, and image analysis may be classified
elsewhere or missed by the search strategy and thus not listed here. Of the 18, the two most
common focus areas were pain (N=6) and hypotension (N=5). Image analysis, mechanical
ventilation, and general commentary were the other topics of interest. Most of the papers were
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systematic and narrative reviews or commentaries (N=12). Of five studies presenting primary
data, two were pain related and three were hypotension related. Two publications focused on
methods and limitations of ML in anesthesiology or healthcare overall.
The focus on prediction of intraoperative hypotension is understandable given the excitement
triggered by last year’s HYPE RCT of a proprietary algorithm based on arterial pressure contours
which predicted hypotension up to 15 minutes in advance, and which was demonstrated to
reduce the minutes of MAP<65 (Wijnberge et al., JAMA. 2020 Mar 17;323(11):1052-1060).
Other trials last year also found predictive utility of the hypotension prediction index used in the
HYPE trial. Since even brief episodes of hypotension greatly increase perioperative risk
including all cause mortality, the topic is worthy of this and future attention. In 2021 Lee et al
demonstrated further advance in prediction of hypotension by introduction of monitoring
modalities other than arterial pressure waveforms (ECG, pulse oximeter, capnography).
Maheshwari et al. demonstrated acceptable performance of a prediction algorithm using
noninvasive continues BP monitoring. Schenk et al, however, showed that the HPI did not reduce
postoperative hypotension.
Most pain related articles were narrative or systematic reviews. However, Mullin et al.
demonstrated use of K-means and decision tree analysis of pain patient subtypes. Ichesco et al
used a support vector machine approach to evaluate medication response in fibromyalgia, a
challenging disease to treat.
Two articles on methods and limitations in ML and AI are especially worth your attention.
Soussi et al. published in Anesthesiology a “clinician’s overview” or non-expert toolbox of ML
vs. traditional statistics and some of the limitations of the former. Feldman et al. published an
opinion piece in response to the fascinating “forensic disassembly” of the BIS monitor in which
Connor reverse engineered the proprietary algorithm behind AI-based processed EEG monitor.
(Anesth Analg. 2020 Dec;131(6):1923-1933). Feldman argued that there are general lessons to
be learned from this kind of work, broadly applicable to ML/AI. These include the black box
problem in that many ML algorithms demonstrably “work” but are unexplainable to end users
and/or proprietary. There is additionally the problem of identifying a gold standard for the
outcome of interest, overfitting of models derived from hundreds or thousands of data features,
and the need for external validation of derived algorithms. As these techniques become more
widespread and enter clinical use, all readers should educate themselves and exercise vigilance
and caution in their interpretation.
References
1. Awad H, Alcodray G, Raza A, et al. Intraoperative Hypotension-Physiologic Basis and
Future Directions. J Cardiothorac Vasc Anesth. 2021.
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2. Chen X, Owen CA, Huang EC, et al. Artificial Intelligence in Echocardiography for
Anesthesiologists. J Cardiothorac Vasc Anesth. 2021;35(1):251-261.
3. Falla D, Devecchi V, Jiménez-Grande D, Rügamer D, Liew BXW. Machine learning
approaches applied in spinal pain research. Journal of electromyography and kinesiology :
official journal of the International Society of Electrophysiological Kinesiology.
2021;61:102599.
4. Feldman JM, Kuck K, Hemmerling T. Black Box, Gray Box, Clear Box? How Well Must
We Understand Monitoring Devices? Anesth Analg. 2021;132(6):1777-1780.
5. Gallifant J, Zhang J, Del Pilar Arias Lopez M, et al. Artificial intelligence for mechanical
ventilation: systematic review of design, reporting standards, and bias. British journal of
anaesthesia. 2021.
6. Ichesco E, Peltier SJ, Mawla I, et al. Prediction of Differential Pharmacologic Response
in Chronic Pain Using Functional Neuroimaging Biomarkers and a Support Vector Machine
Algorithm: An Exploratory Study. Arthritis Rheumatol. 2021;73(11):2127-2137.
7. Lang VA, Lundh T, Ortiz-Catalan M. Mathematical and Computational Models for Pain:
A Systematic Review. Pain Med. 2021;22(12):2806-2817.
8. Lee S, Lee HC, Chu YS, et al. Deep learning models for the prediction of intraoperative
hypotension. British journal of anaesthesia. 2021;126(4):808-817.
9. Maheshwari K, Buddi S, Jian Z, et al. Performance of the Hypotension Prediction Index
with non-invasive arterial pressure waveforms in non-cardiac surgical patients. J Clin Monit
Comput. 2021;35(1):71-78.
10. Mari T, Henderson J, Maden M, Nevitt S, Duarte R, Fallon N. Systematic Review of the
Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or
Treatment Outcomes Using Electroencephalogram Data. J Pain. 2021.
11. Matsangidou M, Liampas A, Pittara M, Pattichi CS, Zis P. Machine Learning in Pain
Medicine: An Up-To-Date Systematic Review. Pain Ther. 2021;10(2):1067-1084.
12. McKendrick M, Yang S, McLeod GA. The use of artificial intelligence and robotics in
regional anaesthesia. Anaesthesia. 2021;76 Suppl 1:171-181.
13. Mullin S, Zola J, Lee R, et al. Longitudinal K-means approaches to clustering and
analyzing EHR opioid use trajectories for clinical subtypes. J Biomed Inform. 2021;122:103889.
14. Schenk J, Wijnberge M, Maaskant JM, et al. Effect of Hypotension Prediction
Index-guided intraoperative haemodynamic care on depth and duration of postoperative
hypotension: a sub-study of the Hypotension Prediction trial. British journal of anaesthesia.
2021;127(5):681-688.
15. Soussi S, Collins GS, Jüni P, Mebazaa A, Gayat E, Le Manach Y. Evaluation of
Biomarkers in Critical Care and Perioperative Medicine: A Clinician’s Overview of Traditional
Statistical Methods and Machine Learning Algorithms. Anesthesiology. 2021;134(1):15-25.
16. van der Ven WH, Veelo DP, Wijnberge M, van der Ster BJP, Vlaar APJ, Geerts BF. One
of the first validations of an artificial intelligence algorithm for clinical use: The impact on
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intraoperative hypotension prediction and clinical decision-making. Surgery.
2021;169(6):1300-1303.
17. Wingert T, Lee C, Cannesson M. Machine Learning, Deep Learning, and Closed Loop
Devices-Anesthesia Delivery. Anesthesiol Clin. 2021;39(3):565-581.
18. Wu Z, Wang Y. Development of Guidance Techniques for Regional Anesthesia: Past,
Present and Future. J Pain Res. 2021;14:1631-1641.
Cardiovascular
Jai Nahar, MD, MBA
Year 2021 was very productive in terms of research publications related to use of AI/ML in
cardiology. There were 119 publications, which focused on applications in variety of functional
areas as below.
1. Cardiac imaging: such as Echocardiography (Transthoracic, Intravascular), Cardiac CT,
Cardiac MRI, Interventional cardiology, and multimodal imaging
2. Electrophysiology: Arrhythmia prediction, detection, and management
3. Clinical decision support: prediction, risk stratification, diagnosis, and management
4. Precision and personalized medicine: diagnosis and management using pheno-mapping,
phenotyping, multi-omic integration
5. Remote patient monitoring: using AI with smart sensors and Internet of things
6. Congenital heart disease: detection of risk factors, diagnosis and stratification
7. Cardiac Rehabilitation: specific home monitoring and decision support
Amongst the clinical disorders, research focused on atrial fibrillation, heart failure, coronary
artery disease, cardiomyopathy, pulmonary hypertension, peripheral vascular disease and shock.
Computer vision and cardiac Imaging
Deep learning-based computer vision was widely studied in cardiac imaging. In
echocardiography the studies focused on value adding applications of AI/ML at various steps
involved in imaging workflow such as analysis, detection, diagnosis and decision support.
Additionally, Deep learning integrated intravascular ultrasound imaging has been studied in
plaque characterization, which has potential to assist clinicians in recognizing high-risk coronary
lesions.
In advanced imaging, there has been interesting research on Radio genomics and Artificial
Intelligence application to Cardiac Computed Tomography Angiography and Cardiac Magnetic
Resonance to facilitate Precision and personalized Medicine in Coronary Heart Disease.
Electrophysiology
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In Electrophysiology there were multiple studies focused on arrhythmia, specifically atrial
fibrillation detection, and risk stratification.
Beyond arrhythmia detection, AI-enabled EKGs have been studied in identification of patients
with low EF (Yao X et al). Additionally there was a review article on potential use of deep
learning in EKG, for cases of electrolyte imbalance, and sleep apnea (Sun JY et al).
Signal processing and remote patient monitoring
An emerging application of AI which has been studied, is in remote patient monitoring including
smart sensors, for risk prediction, stratification and personalized disease management (e.g.,
hypertension, heart failure) Sun JY et.al
Precision Cardiology
Another high value application of AI which was extensively studied last year was precision
cardiology evaluating integration of multi-omic data, digital biomarkers, for precision risk
prediction, and personalized prescription.
AI-Block chain
An interesting emerging area of research which has been reviewed, highlights the integration of
Artificial Intelligence-powered Block chain for Cardiovascular Medicine with specific
applications in areas such as high-throughput gene sequencing, wearable technologies, and
clinical trials (Krittanawong, et al)
Future Directions: This rich collection of research in 2021 and rapidly evolving landscape of
digital and precision cardiology had paved the way for potential future research in areas such as
multimodal intelligence, intelligent remote patient monitoring, connected intelligence, and
decentralized ML (federated and swarm learning).
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COVID - 19
Raghav Awasthi, MSc.
Despite the remarkable discovery of efficient COVID-19 vaccines in 2020, the virus continued to
spread and mutate throughout 2021. As a response, in order to combat the pandemic, innovation
in AI has continued., In 2021, the number of peer-reviewed research articles (from PubMed
searches) in the field of AI on COVID-19 was 134. COVID-19 diagnosis, detection, epidemic
trends, classification, drug repurposing, and efficient vaccination were the primary areas of AI
study this year.
Vaccine supply for COVID-19 was limited in the early phases. As a result, the basic question is
optimal vaccine distribution. For optimum allocation, a deep learning and reinforcement
learning-based system was created.
Vaccine hesitancy is one of the critical challenges in the vaccination process. one of the best
ways to reach out successfully to those hesitant to get vaccinated. AI-powered chatbots were
developed to understand users' viewpoints and provide instant feedback tailor-made answers that
build trust in COVID-19 vaccines.
Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses in
2020; however, concerns about security and trustworthiness impede the collection of large-scale
representative medical data, posing a significant challenge for training a well-generalized model
in clinical practices. To solve this, the Unified CT-COVID AI Diagnostic Initiative (UCADI) was
SPECIALITY ABSTRACT INDEX
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developed. The artificial intelligence (AI) model may be distributedly trained and separately
implemented at each host institution using a federated learning architecture without data sharing.
COVID-19 appears to confer unique features in the audio produced by infected individuals, and
machine learning-based models have been developed to detect COVID-19 from breath, cough,
and speech audio recordings.
During the COVID-19 pandemic, unprecedented public health interventions were employed to
stop the spread of the SARS-CoV-2 virus. Implementing timely and adequate public health
initiatives is challenging. A decision support tool based on reinforcement learning has been
created to execute public health interventions during COVID-19 and future pandemics.
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Critical Care
Anirban Bhattacharyya, MD, MPH
Artificial Intelligence is being increasingly recognized as a tool to assist decision making by the
ICU providers. AI is being seen as a natural solution to mitigate information overload that
plagues ICU providers and improve work efficiency. Algorithms that addressed common ICU
conditions like Sepsis, ARDS, mechanical ventilation strategy, delirium were shown to predict
the respective disease states with sufficient accuracy. Yet despite promising results, few studies
have shown benefits in terms of patient outcomes. Several reviews in the last year have
addressed the utility and shortcomings that lead to biased algorithms. Bias can arise from data
quality issues because artefacts in data, single center retrospective nature, lack of inadequate
representation of different population limiting generalizability, inconsistent definition of disease
states between different research groups, missing values, improper feature selection, limitations
of various algorithms, lack of interpretability and lack of adequate real-world testing. In fact,
majority of the trials have been reported to be single center retrospective design. These issues are
being increasingly addressed year over year and as witnessed from several publications in 2021.
Some of the solutions suggested by published manuscripts in 2021 was using publicly shared
data like MIMIC and eICU-CRD and sharing of code or multicenter collaborations. Prospective
trials although at a small number have also been performed. Interpretable AI and attempts at
implementing AI as tool to help with precision medicine have been reported. Implementing
digital twin that can serve as a playground for trainees is being viewed as a tool to improve care
and promote patient safety. Also, use of sensors to collect additional information and real time
analysis using AI algorithms, or development of digital biomarkers that can diagnose common
ICU condition are areas of active research. In summary, 2021 continues to report growth in
publications that look beyond prediction algorithms and attempts to address the barriers to
bedside implementation.
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106. Senthilraja M. Application of Artificial Intelligence to Address Issues Related to the
COVID-19 Virus. SLAS Technol. 2021;26(2):123-126.
107. Serafim MSM, Gertrudes JC, Costa DMA, Oliveira PR, Maltarollo VG, Honorio KM.
Knowing and combating the enemy: a brief review on SARS-CoV-2 and computational
approaches applied to the discovery of drug candidates. Biosci Rep. 2021;41(3).
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108. Serena Low WC, Chuah JH, Tee C, et al. An Overview of Deep Learning Techniques on
Chest X-Ray and CT Scan Identification of COVID-19. Computational and mathematical
methods in medicine. 2021;2021:5528144.
109. Shaikh F, Andersen MB, Sohail MR, et al. Current Landscape of Imaging and the
Potential Role for Artificial Intelligence in the Management of COVID-19. Current problems in
diagnostic radiology. 2021;50(3):430-435.
110. Shi F, Wang J, Shi J, et al. Review of Artificial Intelligence Techniques in Imaging Data
Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Rev Biomed Eng. 2021;14:4-15.
111. Singhal L, Garg Y, Yang P, et al. eARDS: A multi-center validation of an interpretable
machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among
critically ill adults with COVID-19. PloS one. 2021;16(9):e0257056.
112. Srivastava B. Did chatbots miss their "Apollo Moment"? Potential, gaps, and lessons
from using collaboration assistants during COVID-19. Patterns (N Y). 2021;2(8):100308.
113. Suri JS, Agarwal S, Gupta SK, et al. A narrative review on characterization of acute
respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
Computers in biology and medicine. 2021;130:104210.
114. Swayamsiddha S, Prashant K, Shaw D, Mohanty C. The prospective of Artificial
Intelligence in COVID-19 Pandemic. Health Technol (Berl). 2021:1-10.
115. Syed M, Syed S, Sexton K, et al. Deep Learning Methods to Predict Mortality in
COVID-19 Patients: A Rapid Scoping Review. Studies in health technology and informatics.
2021;281:799-803.
116. Syeda HB, Syed M, Sexton KW, et al. Role of Machine Learning Techniques to Tackle
the COVID-19 Crisis: Systematic Review. JMIR Med Inform. 2021;9(1):e23811.
117. Taha BA, Al Mashhadany Y, Bachok NN, et al. Detection of COVID-19 Virus on
Surfaces Using Photonics: Challenges and Perspectives. Diagnostics (Basel). 2021;11(6).
118. Tayarani NM. Applications of artificial intelligence in battling against covid-19: A
literature review. Chaos Solitons Fractals. 2021;142:110338.
119. Tchagna Kouanou A, Mih Attia T, Feudjio C, et al. An Overview of Supervised Machine
Learning Methods and Data Analysis for COVID-19 Detection. J Healthc Eng.
2021;2021:4733167.
120. Tilahun B, Gashu KD, Mekonnen ZA, Endehabtu BF, Angaw DA. Mapping the Role of
Digital Health Technologies in Prevention and Control of COVID-19 Pandemic: Review of the
Literature. Yearbook of medical informatics. 2021;30(1):26-37.
121. Tsao SF, Chen H, Tisseverasinghe T, Yang Y, Li L, Butt ZA. What social media told us in
the time of COVID-19: a scoping review. Lancet Digit Health. 2021;3(3):e175-e194.
122. Valizadeh A, Shariatee M. The Progress of Medical Image Semantic Segmentation
Methods for Application in COVID-19 Detection. Computational intelligence and neuroscience.
2021;2021:7265644.
123. Wang L, Zhang Y, Wang D, et al. Artificial Intelligence for COVID-19: A Systematic
Review. Front Med (Lausanne). 2021;8:704256.
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124. Wang LL, Lo K. Text mining approaches for dealing with the rapidly expanding literature
on COVID-19. Brief Bioinform. 2021;22(2):781-799.
125. Wang Q, Su M, Zhang M, Li R. Integrating Digital Technologies and Public Health to
Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital
Healthcare. International journal of environmental research and public health. 2021;18(11).
126. Wang T, Chen Z, Shang Q, Ma C, Chen X, Xiao E. A Promising and Challenging
Approach: Radiologists' Perspective on Deep Learning and Artificial Intelligence for Fighting
COVID-19. Diagnostics (Basel). 2021;11(10).
127. Xu Z, Su C, Xiao Y, Wang F. AI for COVID-19: Battling the pandemic with
computational intelligence. Intell Med. 2021.
128. Yadav AK, Verma D, Kumar A, Kumar P, Solanki PR. The perspectives of
biomarker-based electrochemical immunosensors, artificial intelligence and the Internet of
Medical Things toward COVID-19 diagnosis and management. Mater Today Chem.
2021;20:100443.
129. Yoshikawa Y, Kumazaki H, Kato TA. Future perspectives of robot psychiatry: can
communication robots assist psychiatric evaluation in the COVID-19 pandemic era? Curr Opin
Psychiatry. 2021;34(3):277-286.
130. Younis MC. Evaluation of deep learning approaches for identification of different
corona-virus species and time series prediction. Comput Med Imaging Graph. 2021;90:101921.
131. Zaib S, Rana N, Noor A, Khan I. Machine Intelligence Techniques for the Identification
and Diagnosis of COVID-19. Curr Med Chem. 2021;28(26):5268-5283.
132. Zhang F. Application of machine learning in CT images and X-rays of COVID-19
pneumonia. Medicine. 2021;100(36):e26855.
133. Zhang Q, Gao J, Wu JT, Cao Z, Dajun Zeng D. Data science approaches to confronting
the COVID-19 pandemic: a narrative review. Philos Trans A Math Phys Eng Sci.
2022;380(2214):20210127.
134. Zhu Q, Ye H, Sun L, et al. GACDN: generative adversarial feature completion and
diagnosis network for COVID-19. BMC Med Imaging. 2021;21(1):154.
Dermatology
Francis A. Papay MS(BME), MD, FACS, FAAP
In 2021, the review of machine learning peer reviewed articles revealed 30 journal articles
pertaining to dermatology issues. Needless to say, computer-aided systems for skin lesion
diagnosis and disease monitoring are growing areas of research and new ventures. Recently,
researchers have shown an increasing interest in developing machine vision and machine
learning, cutaneous diagnostic systems. Since the skin is the largest organ of the human body
and that 40% of all cancers are cutaneous there is a high degree of interest in defining what role
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does intelligent algorithms play in assisting in the differential diagnosis of such disease states. In
addition, the skin is externally facing and its exposure is ideal for obtaining and analyzing
photographic images. Its externality also allows several non-invasive means in examining the
surface of cutaneous lesions by spectroscopic methods, ultrasound, autogenous fluorescence, and
optical coherence tomography. All of these methods lend themselves (or in combination) data
sets that can be used to correlate with pre-malignant and malignant cutaneous diseases.
The 2021 dermatology reviewed also shows that melanoma is still the most frequent cutaneous
disease of interest in applying machine learning and image analysis in diagnosis. In these studies
it was also interesting to point out the difficulties in eliminating biases due to skin color and
ethnic diversity in skin cancers. Image prejudice based on gender and race AI prejudice means
that the models and algorithms fail to give optimal results for people of an under-represented
gender or ethnicity. Mostly, skin lesions from light-colored skin can be seen in most current
datasets. Accurate analysis of real world data sets can only happen if the training dataset
contains sufficient images of dark-skinned people. The addition of clinical data such as race, age,
gender, skin type, as inputs for classifiers may help to increase classification accuracy and
decision making by dermatologists.
Several articles were somewhat limited by dataset, if the dataset contains a large number of
images per class, deep learning appears to be better than traditional machine learning. Even with
datasets containing few images, deep learning can overcome this issue by using different
methods of augmentation. Deep learning methods in general show the most promising results
with a higher accuracy of disease detection.
References
1. Benyahia S, Meftah B, Lézoray O. Multi-features extraction based on deep learning for
skin lesion classification. Tissue & cell. 2021;74:101701.
2. Cazzato G, Colagrande A, Cimmino A, et al. Artificial Intelligence in Dermatopathology:
New Insights and Perspectives. Dermatopathology (Basel). 2021;8(3):418-425.
3. Das K, Cockerell CJ, Patil A, et al. Machine Learning and Its Application in Skin Cancer.
International journal of environmental research and public health. 2021;18(24).
4. Dildar M, Akram S, Irfan M, et al. Skin Cancer Detection: A Review Using Deep
Learning Techniques. International journal of environmental research and public health.
2021;18(10).
5. Felmingham CM, Adler NR, Ge Z, Morton RL, Janda M, Mar VJ. The Importance of
Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for
Skin Cancer Diagnosis in the Real World. Am J Clin Dermatol. 2021;22(2):233-242.
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6. Gao W, Li M, Wu R, et al. The design and application of an automated microscope
developed based on deep learning for fungal detection in dermatology. Mycoses.
2021;64(3):245-251.
7. Gupta AK, Ivanova IA, Renaud HJ. How good is artificial intelligence (AI) at solving
hairy problems? A review of AI applications in hair restoration and hair disorders. Dermatol
Ther. 2021;34(2):e14811.
8. Haggenmüller S, Maron RC, Hekler A, et al. Skin cancer classification via convolutional
neural networks: systematic review of studies involving human experts. Eur J Cancer.
2021;156:202-216.
9. Höhn J, Hekler A, Krieghoff-Henning E, et al. Integrating Patient Data Into Skin Cancer
Classification Using Convolutional Neural Networks: Systematic Review. J Med Internet Res.
2021;23(7):e20708.
10. Iacullo J, Barriera-Silvestrini P, Knackstedt TJ. Dermatologic Follow-up and Assessment
of Suspicious Lesions. Clin Plast Surg. 2021;48(4):617-629.
11. Jartarkar SR, Patil A, Wollina U, et al. New diagnostic and imaging technologies in
dermatology. J Cosmet Dermatol. 2021;20(12):3782-3787.
12. Jobson D, Mar V, Freckelton I. Legal and ethical considerations of artificial intelligence
in skin cancer diagnosis. Australas J Dermatol. 2021.
13. Kassem MA, Hosny KM, Damaševičius R, Eltoukhy MM. Machine Learning and Deep
Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review.
Diagnostics (Basel). 2021;11(8).
14. Kim YH, Kobic A, Vidal NY. Distribution of race and Fitzpatrick skin types in data sets
for deep learning in dermatology: A systematic review. Journal of the American Academy of
Dermatology. 2021.
15. Lee EY, Maloney NJ, Cheng K, Bach DQ. Machine learning for precision dermatology:
Advances, opportunities, and outlook. Journal of the American Academy of Dermatology.
2021;84(5):1458-1459.
16. Lim SS, Ohn J, Mun JH. Diagnosis of Onychomycosis: From Conventional Techniques
and Dermoscopy to Artificial Intelligence. Front Med (Lausanne). 2021;8:637216.
17. Ma EZ, Hoegler KM, Zhou AE. Bioinformatic and Machine Learning Applications in
Melanoma Risk Assessment and Prognosis: A Literature Review. Genes (Basel). 2021;12(11).
18. Mehrabi JN, Baugh EG, Fast A, et al. A Clinical Perspective on the Automated Analysis
of Reflectance Confocal Microscopy in Dermatology. Lasers Surg Med. 2021;53(8):1011-1019.
19. Pai VV, Pai RB. Artificial intelligence in dermatology and healthcare: An overview.
Indian J Dermatol Venereol Leprol. 2021;87(4):457-467.
20. Rey-Barroso L, Peña-Gutiérrez S, Yáñez C, Burgos-Fernández FJ, Vilaseca M, Royo S.
Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review. Sensors (Basel,
Switzerland). 2021;21(1).
21. Saba T. Computer vision for microscopic skin cancer diagnosis using handcrafted and
non-handcrafted features. Microscopy research and technique. 2021;84(6):1272-1283.
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22. Salah S, Colomb L, Benize AM, et al. Prediction of treatment effect perception in
cosmetics using machine learning. J Biopharm Stat. 2021;31(1):55-62.
23. Sharma AN, Shwe S, Mesinkovska NA. Current state of machine learning for
non-melanoma skin cancer. Archives of dermatological research. 2021.
24. Shoen E. DermIA: Machine Learning to Improve Skin Cancer Screening. J Digit
Imaging. 2021;34(6):1430-1434.
25. Skudalski L, Waldman R, Kerr PE, Grant-Kels JM. Melanoma: How and When to
Consider Clinical Diagnostic Technologies. Journal of the American Academy of Dermatology.
2021.
26. Sun MD, Halpern AC. Advances in the Etiology, Detection, and Clinical Management of
Seborrheic Keratoses. Dermatology. 2021:1-13.
27. Takiddin A, Schneider J, Yang Y, Abd-Alrazaq A, Househ M. Artificial Intelligence for
Skin Cancer Detection: Scoping Review. J Med Internet Res. 2021;23(11):e22934.
28. Thomsen K, Pihl A, Iversen L, Winther O, Lomholt HB, Thomsen SF. [Artificial
intelligence in diagnosing skin diseases]. Ugeskr Laeger. 2021;183(7).
29. Wan B, Ganier C, Du-Harpur X, et al. Applications and future directions for optical
coherence tomography in dermatology. Br J Dermatol. 2021;184(6):1014-1022.
30. Wen D, Khan SM, Xu AJ, et al. Characteristics of publicly available skin cancer image
datasets: a systematic review. Lancet Digit Health. 2022;4(1):e64-e74.
Education
Amanda J. Naylor, MA.
In this year’s Education section, we have an increased number of articles highlighting the
burgeoning interest in AI/ML by educators, learners, and clinicians alike. Continuing with trends
from last year, many authors have focused on evaluating medical literature with ML methods,
incorporating AI and ML training into formal curricula, and providing frameworks for clinicians
to understand AI/ML-based literature.
Several articles addressed the use of different ML methodologies to improve efficiency of
reviewing large numbers of articles for the purpose of generating systematic reviews and
meta-analyses. ML technology has been increasingly popular for article filtering and selection as
the number of articles published each year expands and the complexity of medical literature
deepens. Traditional methods, such as MeSH indexing or manual review, may not be swift or
adaptable enough for use by review authors as time goes on.
An exciting theme across articles was the study of AI methods in more subspecialty education
programs than seen previously. The specialties of pathology, ophthalmology, surgery, surgical
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oncology, urology, neurology, dentistry, and nursing were all the focus of at least one article this
year. A wider variety of medical and surgical specialties, and even medical professions, are now
investing in AI/ML curricula.
Regardless of specialty, there were shared areas of study including AI-enhanced simulation
training, evaluating student and resident procedural competency using AI methods, and the
utility of specific technologies such as augmented reality, virtual reality, and haptic feedback in
medical education. Authors note how many AI training curricula have been developed over a
short period of time, but that few studies exist that evaluate the implementation and outcomes of
new curricula.
There were also limitations and areas for growth identified by many authors. A gap in current AI
training for medical trainees includes how to utilize AI-based learning methods in their training.
Most studies of AI-based technology and training programs do not currently separate trainees by
expertise level, such as medical student versus resident, and as such the conclusions of these
studies can be limited. Several authors commented on the need to train residents on how they
will utilize and interface with AI-based technology in their clinical practice.
Interest in AI and ML education for medical trainees and clinicians is at an all-time high. There
will be an increased need for formal training programs, evaluation of outcomes of existing AI
curricula, and better frameworks for evaluating AI/ML research in the coming years.
Article of choice: Bilgic E, Gorgy A, Yang A, et al. Exploring the roles of artificial intelligence
in surgical education: A scoping review. Am J Surg. 2021 Nov 30;S0002-9610(21)00682-6. doi:
10.1016/j.amjsurg.2021.11.023.
References
1. Abdelkader W, Navarro T, Parrish R, et al. Machine Learning Approaches to Retrieve
High-Quality, Clinically Relevant Evidence From the Biomedical Literature: Systematic Review.
JMIR Med Inform. 2021;9(9):e30401.
2. Arora A, Arora A. Pathology training in the age of artificial intelligence. Journal of
clinical pathology. 2021;74(2):73-75.
3. Aum S, Choe S. srBERT: automatic article classification model for systematic review
using BERT. Systematic reviews. 2021;10(1):285.
4. Bakshi SK, Lin SR, Ting DSW, Chiang MF, Chodosh J. The era of artificial intelligence
and virtual reality: transforming surgical education in ophthalmology. Br J Ophthalmol.
2021;105(10):1325-1328.
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5. Bilgic E, Gorgy A, Yang A, et al. Exploring the roles of artificial intelligence in surgical
education: A scoping review. American journal of surgery. 2021.
6. Buchanan C, Howitt ML, Wilson R, Booth RG, Risling T, Bamford M. Predicted
Influences of Artificial Intelligence on Nursing Education: Scoping Review. JMIR Nurs.
2021;4(1):e23933.
7. Burns JK, Etherington C, Cheng-Boivin O, Boet S. Using an artificial intelligence tool
can be as accurate as human assessors in level one screening for a systematic review. Health Info
Libr J. 2021.
8. Charow R, Jeyakumar T, Younus S, et al. Artificial Intelligence Education Programs for
Health Care Professionals: Scoping Review. JMIR Med Educ. 2021;7(4):e31043.
9. Collins JW, Marcus HJ, Ghazi A, et al. Ethical implications of AI in robotic surgical
training: A Delphi consensus statement. Eur Urol Focus. 2021.
10. Fei H, Ren Y, Zhang Y, Ji D, Liang X. Enriching contextualized language model from
knowledge graph for biomedical information extraction. Brief Bioinform. 2021;22(3).
11. Gokli A, Dayneka JS, Saul DT, Francavilla ML, Anupindi SA, Reid JR. RADIAL:
leveraging a learning management system to support radiology education. Pediatr Radiol.
2021;51(8):1518-1525.
12. Grunhut J, Wyatt AT, Marques O. Educating Future Physicians in Artificial Intelligence
(AI): An Integrative Review and Proposed Changes. J Med Educ Curric Dev.
2021;8:23821205211036836.
13. Harmon J, Pitt V, Summons P, Inder KJ. Use of artificial intelligence and virtual reality
within clinical simulation for nursing pain education: A scoping review. Nurse Educ Today.
2021;97:104700.
14. Kirubarajan A, Young D, Khan S, Crasto N, Sobel M, Sussman D. Artificial Intelligence
and Surgical Education: A Systematic Scoping Review of Interventions. J Surg Educ. 2021.
15. Lee J, Wu AS, Li D, Kulasegaram KM. Artificial Intelligence in Undergraduate Medical
Education: A Scoping Review. Acad Med. 2021;96(11s):S62-s70.
16. Liu T, Xiao X. A Framework of AI-Based Approaches to Improving eHealth Literacy and
Combating Infodemic. Front Public Health. 2021;9:755808.
17. Lomis K, Jeffries P, Palatta A, et al. Artificial Intelligence for Health Professions
Educators. NAM Perspect. 2021;2021.
18. Richardson ML, Adams SJ, Agarwal A, et al. Review of Artificial Intelligence Training
Tools and Courses for Radiologists. Acad Radiol. 2021;28(9):1238-1252.
19. Saghiri MA, Vakhnovetsky J, Nadershahi N. Scoping review of artificial intelligence and
immersive digital tools in dental education. J Dent Educ. 2021.
20. Schuur F, Rezazade Mehrizi MH, Ranschaert E. Training opportunities of artificial
intelligence (AI) in radiology: a systematic review. European radiology. 2021;31(8):6021-6029.
21. Scott IA. Demystifying machine learning: a primer for physicians. Intern Med J.
2021;51(9):1388-1400.
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22. Vinny PW, Garg R, Padma Srivastava MV, Lal V, Vishnu VY. Critical Appraisal of a
Machine Learning Paper: A Guide for the Neurologist. Ann Indian Acad Neurol.
2021;24(4):481-489.
23. Wang M, Wang M, Yu F, Yang Y, Walker J, Mostafa J. A systematic review of automatic
text summarization for biomedical literature and EHRs. J Am Med Inform Assoc.
2021;28(10):2287-2297.
24. Ward TM, Mascagni P, Madani A, Padoy N, Perretta S, Hashimoto DA. Surgical data
science and artificial intelligence for surgical education. J Surg Oncol. 2021;124(2):221-230.
Emergency Medicine
John Lee MD
Upon reviewing the emergency medicine artificial intelligence articles for 2021, the sources of
the publications were quite varied. In the opinion of this emergency physician, intense work in
AI has not been firmly established in emergency medicine as it has in other fields such as
radiology. Thus, AI publications in emergency medicine are not surprisingly from disparate
sources.
As with last year, there was a strong theme in risk stratification. There are many circumstances in
emergency medicine where there is ambiguity in the risk of the patient in front of us. This is not
surprising in emergency medicine where we see a high variety of patients with incomplete
clinical information while we are simultaneously highly risk averse. Typical of such a clinical
condition is syncope (2), which is a commonly presenting complaint that is often vague and
fraught with potential truly negative outcomes but these outcomes are frustratingly difficult to
identify and infrequently truly require acute intervention. Along these lines, there were several
publications that explored the promise of more widespread, multifunctional risk stratification (4,
5, 6, 7) but much of this work is theoretical with little evidence for widespread actual front line
usage.
There was a smaller theme of attempting to find signal in noisy data. For instance, Blomberg (1)
tried to use ML tools to passively detect and highlight cardiac arrest. Unfortunately, this strategy
did not yield any success. Jalal (3) also advocated for using AI to help radiologists better direct
the simultaneously labor intensive and time sensitive critical work of emergency radiology.
Stewart (9) provided a window on finding focused insights in the often subjective, vague world
of interpreting ultrasounds.
If there is a single unifying theme, it is that we are early in our AI journey. In many ways, all the
publications were setting the stage for what we would like to be doing at the point of care but
there was little practical action. We are setting the foundation and feeling our way. Hopefully,
we will soon be able to connect this foundational work with real world, accessible clinical
outcomes.
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References
1. Blomberg SN, Christensen HC, Lippert F, et al. Effect of Machine Learning on
Dispatcher Recognition of Out-of-Hospital Cardiac Arrest During Calls to Emergency Medical
Services: A Randomized Clinical Trial. JAMA Netw Open. 2021;4(1):e2032320.
2. Dipaola F, Shiffer D, Gatti M, Menè R, Solbiati M, Furlan R. Machine Learning and
Syncope Management in the ED: The Future Is Coming. Medicina (Kaunas). 2021;57(4).
3. Jalal S, Parker W, Ferguson D, Nicolaou S. Exploring the Role of Artificial Intelligence
in an Emergency and Trauma Radiology Department. Canadian Association of Radiologists
journal = Journal l'Association canadienne des radiologistes. 2021;72(1):167-174.
4. Kareemi H, Vaillancourt C, Rosenberg H, Fournier K, Yadav K. Machine Learning
Versus Usual Care for Diagnostic and Prognostic Prediction in the Emergency Department: A
Systematic Review. Acad Emerg Med. 2021;28(2):184-196.
5. Lee S, Lam SH, Hernandes Rocha TA, et al. Machine Learning and Precision Medicine
in Emergency Medicine: The Basics. Cureus. 2021;13(9):e17636.
6. Naemi A, Schmidt T, Mansourvar M, Naghavi-Behzad M, Ebrahimi A, Wiil UK.
Machine learning techniques for mortality prediction in emergency departments: a systematic
review. BMJ open. 2021;11(11):e052663.
7. Sánchez-Salmerón R, Gómez-Urquiza JL, Albendín-García L, et al. Machine learning
methods applied to triage in emergency services: A systematic review. Int Emerg Nurs.
2021;60:101109.
8. Stewart J, Lu J, Goudie A, et al. Applications of machine learning to undifferentiated
chest pain in the emergency department: A systematic review. PloS one. 2021;16(8):e0252612.
9. Stewart JE, Goudie A, Mukherjee A, Dwivedi G. Artificial intelligence-enhanced
echocardiography in the emergency department. Emerg Med Australas. 2021;33(6):1117-1120.
10. Tan TH, Hsu CC, Chen CJ, et al. Predicting outcomes in older ED patients with influenza
in real time using a big data-driven and machine learning approach to the hospital information
system. BMC Geriatr. 2021;21(1):280.
Endocrinology
Chaitanya Mamillapalli,MD, Nikita Pozdeyev,MD, Johnson Thomas, MD, FACE
Diabetes and thyroid disease are two endocrine diseases that get the most attention from
clinical artificial intelligence (AI) researchers and developers.
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51
Several AI algorithms have been developed to predict developing type 2 diabetes
accurately. Machine learning models such as tree-based algorithms achieve higher classification
accuracies in the 80-90% range and are superior to those obtained with logistic regression
methods (65-85% accuracy). Exciting developments were made in artificial pancreas systems
that mimic islet cell physiology in glucose regulation . The first generation of the artificial
pancreas uses hybrid closed-loop systems that require patients to enter carbohydrates intake to
calculate meal insulin dose. Unlike hybrid systems, fully closed-loop systems currently in
development are autonomous and use AI to automatically estimate food intake or exercise and
make insulin dose adjustments.
Machine learning models predict the risk of diabetes complications and identify diabetes
phenotypes to target high-risk groups for more intensive management. mHealth Apps powered
by AI have been developed for gestational diabetes (GDM). Among eleven discrete GDM-based
mHealth apps, only three were reasonably mature, and one (GDM Health) is currently in use in a
clinical setting.
Ethnic and racial bias is recognized as a limitation to the real-world application of AI-based
diabetes clinical decision support tools. A simple screening tool with five questions was
proposed to evaluate the racial and ethnic equity in the algorithm development.
In a study by Wu YT, et al., involving 16,819 pregnant patients, a deep neural network
model and logistic regression model achieved comparable effectiveness in estimating the risk of
GDM early in the pregnancy with an area under the curve of 0.80 and 0.77, respectively. These
tools could be deployed early in the pregnancy to identify patients at risk for GDM and
recommend early screening. Many hypoglycemia prediction algorithms are already being used in
many hospitals.
Machine learning was used to predict adult height based upon the height measurement
before six years of age with excellent generalizability across different population cohorts .
Traditional models to predict adult height are available for later prepubertal ages and need
information regarding skeletal maturation from the X rays.
AI applications are being developed to analyze thyroid ultrasound images to estimate the
risk of malignancy and assist radiologists in detecting high-risk features. The AI model ThyNet
was trained on a large cohort of patients with thyroid nodules and showed promise for reducing
the number of invasive biopsy procedures,in a study by Peng S, et al. AIBx thyroid nodule risk
stratification tool achieved a negative predictive value of 89% on external validation by Swan
KZ, et al . Deep learning and radiomics approaches are being tested for other clinical tasks such
as detecting neck metastatic lymph nodes in thyroid cancer , diagnosing Hashimoto thyroiditis ,
and thyroid digital cytology.In a study by Liu YH, et al., a random forest model was constructed
to predict the post-thyroidectomy quality of life. Computer-assisted diagnosis of non-thyroidal
endocrine malignancies is also being explored.
Artificial Intelligence (AI) is causing a paradigm shift in diabetes and thyroid disease
management through data-driven precision care. Rigorous standards need to be established to
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address the issues of accuracy , reproducibility , bias , safety , and privacy to facilitate clinical
implementation of AI-based and machine learning tools in endocrine practice.
Additional articles selected for highlighting:
1. Wu YT, Zhang CJ, Mol BW, et al. Early Prediction of Gestational Diabetes Mellitus in the
Chinese Population via Advanced Machine Learning. J Clin Endocrinol Metab. Mar 8
2021;106(3):e1191-e1205. doi:10.1210/clinem/dgaa899
2. Shmoish M, German A, Devir N, et al. Prediction of Adult Height by Machine Learning
Technique. J Clin Endocrinol Metab. Jun 16 2021;106(7):e2700-e2710.
doi:10.1210/clinem/dgab093
3. Peng S, Liu Y, Lv W, et al. Deep learning-based artificial intelligence model to assist
thyroid nodule diagnosis and management: a multicentre diagnostic study. Lancet Digit Health.
Apr 2021;3(4):e250-e259. doi:10.1016/s2589-7500(21)00041-8
4. Swan KZ, Thomas J, Nielsen VE, Jespersen ML, Bonnema SJ. External validation of
AIBx, an artificial intelligence model for risk stratification, in thyroid nodules. Eur Thyroid J.
Feb 1, 2022;doi:10.1530/etj-21-0129
5. Li J, Wu X, Mao N, et al. Computed tomography-based Radiomics Model to Predict
Central Cervical Lymph Node Metastases in Papillary Thyroid Carcinoma: A Multicenter Study.
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6. Zhao W, Kang Q, Qian F, Li K, Zhu J, Ma B. Convolutional Neural Network-Based
Computer-Assisted Diagnosis of Hashimoto's Thyroiditis on Ultrasound. J Clin Endocrinol
Metab. Dec 15 2021;doi:10.1210/clinem/dgab870
7. Böhland M, Tharun L, Scherr T, et al. Machine learning methods for automated
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2. Daley BJ, Ni'Man M, Neves MR, et al. mHealth apps for gestational diabetes mellitus
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5. Gautier T, Ziegler LB, Gerber MS, Campos-Náñez E, Patek SD. Artificial intelligence
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Gastroenterology
Babu P Mohan MD, Gursimran S Kochhar MD
Last year has been inspiring for the field of gastroenterology (GI) on the emerging use of
artificial intelligence (AI) based computer-aided detection (CADe) and/ or computer-aided
diagnosis (CADx). GI continues to lead the way in performing real-world clinical studies on AI
in clinical practice. Convolutional neural network (CNN) based machine learning algorithms
have been successfully deployed commercially, and the AI tool ‘GI-Genius’ (Medtronic U.S.A)
bagged FDA approval in 2021. It's one of the first such clinical application tools to do so. This
application is now in actual clinical use across the country.
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55
The majority of the machine learning interest currently explores the use of computer vision on
endoscopic images to detect and/or diagnose lesions on endoscopic images. Studies have
reported on the emerging use of such AI tools in capsule endoscopy images, endoscopic
ultrasound (EUS) images of the pancreas, fibroscan assessment of liver, cholangioscopic
assessment of cholangiocarcinoma, and on endoscopic evaluation of upper GI premalignant and
malignant lesions like Barrett’s esophagus and gastric/ esophageal malignancy. The ability of
such machine learning algorithms to detect the depth of lesions is an exciting area, as minimally
invasive early cancers (such as T1a, T1b tumors) of the stomach and/ or esophagus are amenable
to successful endoscopic curative therapies.
In a study from Japan, an AI diagnostic system correctly estimated the depth of esophageal
squamous cell carcinoma with a sensitivity of 84.1% and accuracy of 80.9% in 6 seconds! [1] In
a notable study published in the journal Gut , a machine learning model predicted survival in
colorectal cancer with an accuracy of 0.83 (+/-0.04). [2] In another interesting study, a deep
learning model predicted disease severity in ulcerative colitis from the assessment of full-length
endoscopy videos with an excellent agreement to physicians (quadratic weighted kappa of 0.84,
95% CI 0.79-0.9). [3] In a retrospective analysis, AI demonstrated non-inferior performance in
diagnosing gastric cancer compared to expert endoscopists (100% in the AI group vs. 94% in the
expert endoscopists group).[4]
Amidst the growth of image-based assessment of machine learning, it is essential not to forget
the clinical potential of machine learning utilizing natural language processing (NLP) and
biomedical text analysis. Interesting possibilities exist on the use of NLP in clinical practice. The
use of NLP in improving clinical documentation through voice commands and voice recognition
is an exciting avenue. According to a news release in 2021, GI-focused software companies like
Provation medical (Minnesota, USA) have partnered to link Provation’s GI documentation
software with Iterative Scopes (Boston, MA, USA) inflammatory bowel disease data.[5]
Although machine learning models on endoscopic image analysis are exciting, data is emerging
on the important limitation of ‘machine-learning’ bias. Increased data for training and
improvements in algorithm features to improve or limit the concept of ‘overfitting’ are expected
to deliver robust performances. Nevertheless, real-life controlled clinical studies are the only way
to reliably assess AI's actual performance and/ or utility.
A multi-linked data analysis of endoscopy images, NLP data, and individual patient-based data
tools (collectively called the internet of things or ‘IoT’) can improve patient experience and
physician performance multifold in rather unimaginable ways. Indeed, the future is open for
everyone to contribute, and these are exciting times to be a gastroenterologist.
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56
Notable articles:
1: Bibault JE, Chang DT, Xing L. Development and validation of a model to predict survival in
colorectal cancer using a gradient-boosted machine. Gut. 2021;70(5):884-889.
2: Gottlieb K, Requa J, Karnes W, et al. Central Reading of Ulcerative Colitis Clinical Trial
Videos Using Neural Networks. Gastroenterology. 2021;160(3):710-719.e712.
3: Niikura R, Aoki T, Shichijo S, et al. Artificial intelligence versus expert endoscopists for
diagnosis of gastric cancer in patients who underwent upper gastrointestinal endoscopy.
Endoscopy. 2021.
4: Stankovic B, Kotur N, Nikcevic G, Gasic V, Zukic B, Pavlovic S. Machine Learning Modeling
from Omics Data as Prospective Tool for Improvement of Inflammatory Bowel Disease
Diagnosis and Clinical Classifications. Genes (Basel). 2021;12(9).
References:
1. Tokai, Y., Yoshio, T., Aoyama, K. et al. Application of artificial intelligence using
convolutional neural networks in determining the invasion depth of esophageal squamous cell
carcinoma. Esophagus 17, 250–256 (2020). https://doi.org/10.1007/s10388-020-00716-x
2. Bibault JE, Chang DT, Xing L. Development and validation of a model to predict survival in
colorectal cancer using a gradient-boosted machine. Gut. 2021;70(5):884-889.
3. Gottlieb K, Requa J, Karnes W, et al. Central Reading of Ulcerative Colitis Clinical Trial
Videos Using Neural Networks. Gastroenterology. 2021;160(3):710-719.e712.
4. Niikura R, Aoki T, Shichijo S, et al. Artificial intelligence versus expert endoscopists for
diagnosis of gastric cancer in patients who underwent upper gastrointestinal endoscopy.
Endoscopy. 2021.
5. https://www.provationmedical.com/press-release/provation-iterative-scopes-partnership/
SPECIALITY ABSTRACT INDEX
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References:
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New Frontier in Diagnosis and Treatment. Clin Liver Dis (Hoboken). 2021;17(6):392-397.
2. Ahmad OF, Stassen P, Webster GJ. Artificial intelligence in biliopancreatic endoscopy: Is
there any role? Best Pract Res Clin Gastroenterol. 2021;52-53:101724.
3. Ahn JC, Connell A, Simonetto DA, Hughes C, Shah VH. Application of Artificial
Intelligence for the Diagnosis and Treatment of Liver Diseases. Hepatology.
2021;73(6):2546-2563.
4. Andrade Cruz I, Chuenchart W, Long F, et al. Application of machine learning in
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5. Anteby R, Klang E, Horesh N, et al. Deep learning for noninvasive liver fibrosis
classification: A systematic review. Liver Int. 2021;41(10):2269-2278.
6. Antonelli G, Badalamenti M, Hassan C, Repici A. Impact of artificial intelligence on
colorectal polyp detection. Best Pract Res Clin Gastroenterol. 2021;52-53:101713.
7. Arribas Anta J, Dinis-Ribeiro M. Early gastric cancer and Artificial Intelligence: Is it
time for population screening? Best Pract Res Clin Gastroenterol. 2021;52-53:101710.
8. Ashat M, Klair JS, Singh D, Murali AR, Krishnamoorthi R. Impact of real-time use of
artificial intelligence in improving adenoma detection during colonoscopy: A systematic review
and meta-analysis. Endosc Int Open. 2021;9(4):E513-e521.
9. Balsano C, Alisi A, Brunetto MR, Invernizzi P, Burra P, Piscaglia F. The application of
artificial intelligence in hepatology: A systematic review. Dig Liver Dis. 2021.
10. Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Gastrointestinal Ulcer and
Hemorrhage Using Wireless Capsule Endoscopy: Systematic Review and Diagnostic Test
Accuracy Meta-analysis. J Med Internet Res. 2021;23(12):e33267.
11. Bang CS, Lee JJ, Baik GH. Computer-aided diagnosis of esophageal cancer and
neoplasms in endoscopic images: a systematic review and meta-analysis of diagnostic test
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12. Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Diminutive Colorectal Polyps
in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy. J
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13. Barua I, Mori Y, Bretthauer M. Colorectal polyp characterization with endocytoscopy:
Ready for widespread implementation with artificial intelligence? Best Pract Res Clin
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14. Barua I, Vinsard DG, Jodal HC, et al. Artificial intelligence for polyp detection during
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15. Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of
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58
16. Bibault JE, Chang DT, Xing L. Development and validation of a model to predict
survival in colorectal cancer using a gradient-boosted machine. Gut. 2021;70(5):884-889.
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challenges and perspectives in the era of artificial intelligence. World journal of
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19. Calderaro J, Kather JN. Artificial intelligence-based pathology for gastrointestinal and
hepatobiliary cancers. Gut. 2021;70(6):1183-1193.
20. Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural
network from the perspective of gastroenterologists. World journal of gastroenterology.
2021;27(21):2681-2709.
21. Cao JS, Lu ZY, Chen MY, et al. Artificial intelligence in gastroenterology and
hepatology: Status and challenges. World journal of gastroenterology. 2021;27(16):1664-1690.
22. Castañé H, Baiges-Gaya G, Hernández-Aguilera A, et al. Coupling Machine Learning
and Lipidomics as a Tool to Investigate Metabolic Dysfunction-Associated Fatty Liver Disease.
A General Overview. Biomolecules. 2021;11(3).
23. Cetin-Atalay R, Kahraman DC, Nalbat E, et al. Data Centric Molecular Analysis and
Evaluation of Hepatocellular Carcinoma Therapeutics Using Machine Intelligence-Based Tools.
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24. Chen G, Shen J. Artificial Intelligence Enhances Studies on Inflammatory Bowel
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25. Chen PT, Chang D, Wu T, Wu MS, Wang W, Liao WC. Applications of artificial
intelligence in pancreatic and biliary diseases. J Gastroenterol Hepatol. 2021;36(2):286-294.
26. Cheng CH, Wong KL, Chin JW, Chan TT, So RHY. Deep Learning Methods for Remote
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27. Cheung H, Yu J. Machine learning on microbiome research in gastrointestinal cancer. J
Gastroenterol Hepatol. 2021;36(4):817-822.
28. Christou CD, Tsoulfas G. Challenges and opportunities in the application of artificial
intelligence in gastroenterology and hepatology. World journal of gastroenterology.
2021;27(37):6191-6223.
29. Correia FP, Lourenço LC. Artificial intelligence application in diagnostic gastrointestinal
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SPECIALITY ABSTRACT INDEX
59
31. Decharatanachart P, Chaiteerakij R, Tiyarattanachai T, Treeprasertsuk S. Application of
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32. Deliwala SS, Hamid K, Barbarawi M, et al. Artificial intelligence (AI) real-time detection
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33. Dinani AM, Kowdley KV, Noureddin M. Application of Artificial Intelligence for
Diagnosis and Risk Stratification in NAFLD and NASH: The State of the Art. Hepatology.
2021;74(4):2233-2240.
34. Ebigbo A, Palm C, Messmann H. Barrett esophagus: What to expect from Artificial
Intelligence? Best Pract Res Clin Gastroenterol. 2021;52-53:101726.
35. Feng B, Ma XH, Wang S, Cai W, Liu XB, Zhao XM. Application of artificial intelligence
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World journal of gastroenterology. 2021;27(32):5341-5350.
36. Feng G, Zheng KI, Li YY, et al. Machine learning algorithm outperforms fibrosis
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41. Gottlieb K, Requa J, Karnes W, et al. Central Reading of Ulcerative Colitis Clinical Trial
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General
Sandeep Reddy, MBBS MSc PhD FAIDH CHIA
Continuing the trend of 2020, there is a noticeable growth in the number of papers/studies that
emphasize the translational aspects of Artificial Intelligence (AI) in healthcare. This trend is to
be seen as positive, as it means the phase of exploration of AI application has now shifted to the
gritty reality of integrating AI into clinical workflows and realizing patient outcomes. In this
vein, last year, my colleagues and I published a paper in BMJ Health & Care Informatics
outlining in detail an evaluation framework that supported the translational deployment of AI
applications in healthcare (Reddy, et al). The reception within the journal (editor’s choice) and
by readers (one of the most read articles in the journal) and listing the article in a WHO/ITU
working paper indicate interest in the paper and the translational process of AI in healthcare.
While many of the listed papers in the section are reviews (systematic and scoping reviews),
noticeable papers outline the implementation aspects of AI in healthcare and, in some instances,
lament the gaps in the hype of AI in healthcare and its impact on clinical outcomes.
In addition to the reviews and translational discourse-related papers in this section, there were
some nuggets of novelty, including the following four articles listed to be highlighted:
1. Ahmedt-Aristizabal D, Armin MA, Denman S, Fookes C, Petersson L. Graph-Based Deep
Learning for Medical Diagnosis and Analysis: Past, Present and Future. Sensors (Basel,
Switzerland). 2021;21(14).
In this paper, the authors put forward the case for use of graph neural networks (GNNs),
which are better suited in certain cases to analyse non-harmonious and irregular medical
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data. GNNs are more capable of exploiting implicit information that reside in biological
systems, the authors state.
2. De Chiara F, Ferret-Miñana A, Ramón-Azcón J. The Synergy between Organ-on-a-Chip
and Artificial Intelligence for the Study of NAFLD: From Basic Science to Clinical
Research. Biomedicines. 2021;9(3).
The authors of this paper discuss a novel process, which involves use of biological tissue
in the lab (Organ-on-a-chip) to trial efficacy of drugs and how machine learning is
significantly suited to aid and even enhance the analytical process.
3. Tong X, Liu X, Tan X, et al. Generative Models for De Novo Drug Design. J Med Chem.
2021;64(19):14011-14027.
Generative algorithms have been used previously to synthesise medical images. In this
paper, the authors discuss the possibility of using generative models for creating drug
compounds. They also outline performance metrics and benchmarks for this process.
4. Reddy, S et al. (2021). Evaluation framework to guide implementation of AI systems into
healthcare settings. BMJ Health & Care Informatics 2021;28:e100444
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