Unveiling the Future of Healthcare: A 25-Year Deep Dive into AI and Machine Learning Trends
A groundbreaking bibliometric analysis offers an unparalleled global and historical perspective on the integration of Artificial Intelligence (AI) and Machine Learning (ML) in health and medicine. This comprehensive study, spanning 25 years (2000-2024), reveals the transformative potential of AI and ML in improving global health outcomes.
Key Insights from the Research:
- Explosive Growth in Research Activity: The study identified a total of 22,113 research articles, demonstrating a notable surge in research activity in recent years, with a steady annual growth rate of 24.8%. Citation trends show substantial growth, with total citations reaching 546,819, indicating widespread recognition of AI’s and ML’s impact in health and medicine.
- Global Leaders in AI/ML Healthcare Research:
- The United States leads in research contributions with 4752 articles, exhibiting extensive collaborative efforts and a significant partnership with India.
- China and India follow closely as major contributors.
- Core Institutions driving innovation include Harvard Medical School and the Ministry of Education of the People’s Republic of China.
- Core Journals for this field are Scientific Reports and IEEE Access.
- Key Research Hotspots and Dominant Themes: Analysis of author keywords revealed critical areas of focus:
- Technological Themes: “Deep learning,” “convolutional neural network,” and “classification” are identified as dominant research themes. Other significant topics include “artificial intelligence,” “machine learning,” “prediction and modeling,” “support vector machine,” and “natural language processing”.
- Application Areas: Research has heavily concentrated on AI/ML in medical imaging, disease prediction and diagnosis, electronic health record (EHR) analysis, and personalized medicine.
- Disease-Specific Research: Frequent author keywords highlight a strong focus on specific diseases such as Alzheimer’s disease, Parkinson’s diseases, COVID-19, and diabetes.
- Emerging Research Topics: The latest publications point to new frontiers, including explainable AI (XAI) in healthcare, blockchain and cybersecurity in healthcare, IoT and edge AI in medical applications, and AI in precision medicine and biomarker discovery. These areas underscore the ongoing evolution and diversification of AI applications in healthcare.
- Thematic Evolution: The field has transitioned from basic neural networks and decision trees to advanced AI models like deep learning and reinforcement learning, with significant shifts aligning with technological advancements in robotics and AI-driven healthcare solutions.
This study provides valuable insights for researchers, policymakers, and funding agencies, emphasizing the importance of interdisciplinary collaboration, clinical validation, and addressing ethical considerations to ensure that AI-driven innovations continue to advance global health outcomes.
Reference for the Article:
Dalky, A., Altawalbih, M., Alshanik, F., Khasawneh, R. A., Tawalbeh, R., Al-Dekah, A. M., Alrawashdeh, A., Quran, T. O., & ALBashtawy, M. (2025). Global Research Trends, Hotspots, Impacts, and Emergence of Artificial Intelligence and Machine Learning in Health and Medicine: A 25-Year Bibliometric Analysis. Healthcare, 13, Article 892. https://doi.org/10.3390/healthcare13080892
