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The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric
Chuheng Chang1, Wen Shi2, Youyang Wang3
1Department of General Practice (General Internal Medicine), Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China; 4+4 Medical Doctor Program, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Artificial intelligence (AI) in medical diagnostics is rapidly advancing, evolving from task-specific to general-purpose applications. Key factors like data quality and algorithms drive progress, while explainability and robustness remain challenges.
Area of Science:
- Medical Informatics
- Computer Science
- Artificial Intelligence
Background:
- Artificial intelligence (AI) shows significant potential in healthcare diagnostics.
- AI leverages diverse data (imaging, lab tests, records) for medical applications.
- Understanding AI's development in diagnostics is crucial for clinical integration.
Purpose of the Study:
- To conduct a bibliometric analysis of AI in medical diagnostics.
- To explore the evolution from task-specific to general-purpose AI.
- To identify development factors and collaboration trends.
Main Methods:
- Bibliometric analysis of Web of Science articles (2010-2023).
- Utilized VOSviewer, R package Bibliometrix, and CiteSpace for analysis.
- Examined collaborative networks and keywords.
Main Results:
- Rapid growth in AI for medical diagnostics, focusing on image analysis, disease prediction, and decision support.
- Observed global collaborative networks among researchers and institutions.
- Identified data quality, algorithm design, and computational power as key development factors.
Conclusions:
- AI in medical diagnostics is progressing towards general-purpose, multimodal applications.
- Challenges include model explainability, robustness, and equality.
- Interdisciplinary collaboration is essential to overcome challenges and advance AI in diagnostics.
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