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Recent Advances in AI and GenAI for Health Informatics.

Sio Iong Ao1,2, Vasile Palade3, Chris Holt2

  • 1International Association of Engineers, Unit 1, 1/F, Hung To Road, Hong Kong.

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|February 27, 2026
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Summary
This summary is machine-generated.

Large language models (LLMs) and generative artificial intelligence (GenAI) are transforming health informatics. This review synthesizes common concerns and opportunities in AI health informatics applications, covering clinical decision support to patient care.

Keywords:
artificial intelligenceclinical decision supportelectronic health recordsgenerative AIhealth analyticshealth informaticshospital managementlarge language modelspatient careremote patient monitoring

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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.