Recent Advances in AI and GenAI for Health Informatics

Area of Science:

• Health Informatics
• Artificial Intelligence
• Machine Learning

Background:

• The integration of Artificial Intelligence (AI) into health informatics is rapidly expanding.
• Large Language Models (LLMs) and Generative Artificial Intelligence (GenAI) represent significant advancements in this field.
• Numerous AI applications in health informatics have emerged in recent years.

Purpose of the Study:

• To synthesize common concerns and opportunities from recent reviews on AI in health informatics.
• To provide broad coverage of AI applications across key healthcare topics.
• To identify AI tools, challenges, and future directions in AI health informatics.

Main Methods:

• Literature search conducted through the Scopus academic database.
• Analysis of popular reviews on AI and health informatics, measured by citation count.
• In-depth analysis of identified reviews by human experts.

Main Results:

• Key healthcare topics include clinical decision support, patient care, electronic health records, hospital management, and remote patient monitoring.
• Common concerns identified are patient privacy, cybersecurity, ethics, clinical accountability, professional engagement, standardization, and explainability.
• Identified AI tools, challenges, and future directions are based on expert analysis of leading reviews.

Conclusions:

• AI, particularly LLMs and GenAI, presents both opportunities and challenges in health informatics.
• A broad, comprehensive approach to reviewing AI health informatics applications is beneficial for practitioners.
• Addressing concerns like privacy, ethics, and explainability is crucial for the responsible adoption of AI in healthcare.