Figure - available from: Frontiers in Oncology
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Top countries and journals in the field of AI-based on tumor researches. (A) The number of publications and IF in top 10 countries. MCP, multiple countries cooperation; SCP, single country cooperation; IF, impact factor. (B) Venn diagram between top 200 journal publication and top 200 journal citation. (C) The details for merged 17 journals with high publication and citation. (D) Sankey chart between top countries and journals.
Source publication
Background
Artificial intelligence (AI) is widely applied in cancer field nowadays. The aim of this study is to explore the hotspots and trends of AI in cancer research.
Methods
The retrieval term includes four topic words (“tumor,” “cancer,” “carcinoma,” and “artificial intelligence”), which were searched in the database of Web of Science from Ja...
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Purpose:
To examine a series of papers from top ranked orthopaedic journals with respect to the number of citations over a 10-year observation period to identify factors that lead to high citation rates.
Methods:
The Web of Science database was consulted to identify all published papers from the first-year term of 2010 (January-May) from four to...
Citations
... Although individually uncommon, rare cancers collectively account for~24% of all new cancer cases 7 and present substantial challenges due to the lack of corresponding widespread clinical expertise in managing these conditions. Thus, improving the quality of artificial intelligence (AI)-based solutions for rare cancer diseases will contribute significantly to public health [8][9][10] . ...
Diagnosing rare diseases remains a critical challenge in clinical practice, often requiring specialist expertise. Despite the promising potential of machine learning, the scarcity of data on rare diseases and the need for interpretable, reliable artificial intelligence (AI) models complicates development. This study introduces a multimodal concept-based interpretable model tailored to distinguish uveal melanoma (0.4-0.6 per million in Asians) from hemangioma and metastatic carcinoma following the clinical practice. We collected a comprehensive dataset on Asians to date on choroid neoplasm imaging with radiological reports, encompassing over 750 patients from 2013 to 2019. Our model integrates domain expert insights from radiological reports and differentiates between three types of choroidal tumors, achieving an F1 score of 0.91. This performance not only matches senior ophthalmologists but also improves the diagnostic accuracy of less experienced clinicians by 42%. The results underscore the potential of interpretable AI to enhance rare disease diagnosis and pave the way for future advancements in medical AI.
Background: Cancer is progressively becoming the most prevalent disease worldwide, accompanied by significantly increasing investments in research to improve its prevention, early detection, diagnosis, prognosis and treatment. Predictive analytics are showing promising performance when applied to these tasks, with recent reporting guidelines supporting unbiased data analytics whose outcomes demonstrate a clinical benefit. Methods: A systematic review has been conducted to analyse statistical-and ML-based prediction model studies on cancer research from 2010 to 2020. The PRISMA and PROBAST methodologies have been adopted. Findings: Statistical analysis (46.4 %) and linear ML-based methods (36.4 %) predominate over non-linear ML-based methods (17.2 %) among the examined studies. Only 11 % of the studies are associated with a low risk of bias (ROB), whereas the majority of studies (69 %) has been judged as unclear ROB, an aftereffect of the incompleteness (non-transparency) in their reporting. Lastly, 81.6 % of the investigated studies do not report any data quality assessment procedure. A qualitative analysis of the studies from 2021 to 2023 shows a shift to combining data-driven and systems biology computational approaches. Interpretation: The alignment with systematic procedures for reporting and assessing prediction model studies is a prerequisite towards responsible research. These procedures will enable ML-based interventions in the field of cancer research, demonstrating the clinical value of their findings.
Millions of people’s health is at risk because of several factors and multiple overlapping crises, all of which hit the vulnerable the most. These challenges are dynamic and evolve in response to emerging health challenges and concerns, which need effective collaboration among countries working toward achieving Sustainable Development Goals (SDGs) and securing global health. Mental Health, the Impact of climate change, cardiovascular diseases (CVDs), diabetes, Infectious diseases, health system, and population aging are examples of challenges known to pose a vast burden worldwide. We are at a point known as the “digital revolution,” characterized by the expansion of artificial intelligence (AI) and a fusion of technology types. AI has emerged as a powerful tool for addressing various health challenges, and the last ten years have been influential due to the rapid expansion in the production and accessibility of health-related data. The computational models and algorithms can understand complicated health and medical data to perform various functions and deep-learning strategies. This narrative mini-review summarizes the most current AI applications to address the leading global health challenges. Harnessing its capabilities can ultimately mitigate the Impact of these challenges and revolutionize the field. It has the ability to strengthen global health through personalized health care and improved preparedness and response to future challenges. However, ethical and legal concerns about individual or community privacy and autonomy must be addressed for effective implementation.
