Table 1 - uploaded by Piyush Mathur
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The purpose of this review is to provide a comprehensive review of publications related to artificial intelligence (AI) applications in healthcare for the year 2022. With an exponentially increasing number of publications related to AI in healthcare,there is a need to have a curated,timely and data driven review.
This year's review provides a compr...
Citations
... This search resulted in an initial pool of 23,306 publications. Our methodology has remained consistent over the past four years, which allows for comparative analysis of publications for each medical speciality, year over year [7,9]. We performed qualitative evaluation of the publications' maturity with additional details related to the type of data used and type of models developed across the healthcare spectrum. ...
... In the previous years, the total number of articles progressively increased from 3351 in 2019 to 5885 in 2020, marking a 75.59% jump, then slightly decreased to 4164 in 2021, resulting in a 29.24% decrease, and further rose to 9974 in 2022, demonstrating a significant 139.55% increase [7,9]. Also, in 2023, again a significant increase in the number of articles identified, reaching 23306. ...
... This represents a remarkable 133.7% jump (Figure 2A). Also, in previous years there were 99, 250, 36, 600 mature articles in the year 2019, 2020, 2021, 2022 respectively and 2023 there were 1612 mature articles [7,9] (Figure 2B). ...
Background:
The infodemic we are experiencing with AI related publications in healthcare is unparalleled. The excitement and fear surrounding the adoption of rapidly evolving AI in healthcare applications pose a real challenge. Collaborative learning from published research is one of the best ways to understand the associated opportunities and challenges in the field. To gain a deep understanding of recent developments in this field, we have conducted a quantitative and qualitative review of AI in healthcare research articles published in 2023.
Methods:
We performed a PubMed search using the terms, machine learning or artificial intelligence
and 2023, restricted to English language and human subject research as of December 31, 2023 on January 1, 2024. Utilizing a Deep Learning-based approach, we assessed the maturity of publications. Following this, we manually annotated the healthcare specialty, data utilized, and models employed for the identified mature articles. Subsequently, empirical data analysis was performed to elucidate trends and statistics.Similarly, we performed a search for Large Language Model(LLM) based publications for the year 2023.
Results:
Our PubMed search yielded 23,306 articles, of which 1,612 were classified as mature. Following exclusions, 1,226 articles were selected for final analysis. Among these, the highest number of articles originated from the Imaging specialty (483), followed by Gastroenterology (86), and Ophthalmology (78). Analysis of data types revealed that image data was predominant, utilized in 75.2% of publications, followed by tabular data (12.9%) and text data (11.6%). Deep Learning models were extensively employed, constituting 59.8% of the models used. For the LLM related publications,after exclusions, 584 publications were finally classified into the 26 different healthcare specialties and used for further analysis. The utilization of Large Language Models (LLMs), is highest in general healthcare specialties, at 20.1%, followed by surgery at 8.5%.
Conclusion:
Image based healthcare specialities such as Radiology, Gastroenterology and Cardiology have dominated the landscape of AI in healthcare research for years. In the future, we are likely to see other healthcare specialties including the education and administrative areas of healthcare be driven by the LLMs and possibly multimodal models in the next era of AI in healthcare research and publications.
... A review of PubMed research studies in 2022 showed a significant increase in publications exploring the use of AI in health care. [17] There was an exponential increase in publications across various medical specialities. This indicates the widespread interest and adoption of AI in the field. ...
