Next-generation agentic AI for transforming healthcare

Artificial Intelligence (AI) is transforming the healthcare landscape, yet many current applications remain narrowly task-specific, constrained by data complexity and inherent biases. This paper explores the emergence of next generation "agentic AI" systems, characterized by advanced autonomy, adaptability, scalability, and probabilistic reasoning, which address critical challenges in medical management. These systems enhance various aspects of healthcare, including diagnostics, clinical decision support, treatment planning, patient monitoring, administrative operations, drug discovery, and robotic-assisted surgery. Powered by multimodal AI, agentic systems integrate diverse data sources, iteratively refine outputs, and leverage vast knowledge bases to deliver context-aware, patient-centric care with heightened precision and reduced error rates. These advancements promise to enhance patient outcomes, optimize clinical workflows, and expand the reach of AI-driven solutions. However, their deployment introduces ethical, privacy, and regulatory challenges, emphasizing the need for robust governance frameworks and interdisciplinary collaboration. Agentic AI has the potential to redefine healthcare, driving personalized, efficient, and scalable services while extending its impact beyond clinical settings to global public health initiatives. By addressing disparities and enhancing care delivery in resource-limited environments, this technology could significantly advance equitable healthcare. Realizing the full potential of agentic AI will require sustained research, innovation, and cross-disciplinary partnerships to ensure its responsible and transformative integration into healthcare systems worldwide. • Agentic AI offers autonomy and scalability for key challenges in medical and healthcare innovation. • Agentic AI enhances diagnostics, decision support, patient care, treatment planning, and robotic surgery. • Multimodal AI enables precise, context-aware, patient-centric care with iterative refinement. • Unlocking agentic AI’s potential requires ethical, privacy, and governance collaboration.

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Publication Details

Journal
Informatics and Health
Published
2025-04-08
DOI
https://doi.org/10.1016/j.infoh.2025.03.001
Citations
65
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
161.26

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article

Next-generation agentic AI for transforming healthcare

Nalan Karunanayake
65 citations
Informatics and Health
Machine Learning in Healthcare
161.26
article

Next-generation agentic AI for transforming healthcare

Nalan Karunanayake
article en
65 citations

Abstract

Artificial Intelligence (AI) is transforming the healthcare landscape, yet many current applications remain narrowly task-specific, constrained by data complexity and inherent biases. This paper explores the emergence of next generation "agentic AI" systems, characterized by advanced autonomy, adaptability, scalability, and probabilistic reasoning, which address critical challenges in medical management. These systems enhance various aspects of healthcare, including diagnostics, clinical decision support, treatment planning, patient monitoring, administrative operations, drug discovery, and robotic-assisted surgery. Powered by multimodal AI, agentic systems integrate diverse data sources, iteratively refine outputs, and leverage vast knowledge bases to deliver context-aware, patient-centric care with heightened precision and reduced error rates. These advancements promise to enhance patient outcomes, optimize clinical workflows, and expand the reach of AI-driven solutions. However, their deployment introduces ethical, privacy, and regulatory challenges, emphasizing the need for robust governance frameworks and interdisciplinary collaboration. Agentic AI has the potential to redefine healthcare, driving personalized, efficient, and scalable services while extending its impact beyond clinical settings to global public health initiatives. By addressing disparities and enhancing care delivery in resource-limited environments, this technology could significantly advance equitable healthcare. Realizing the full potential of agentic AI will require sustained research, innovation, and cross-disciplinary partnerships to ensure its responsible and transformative integration into healthcare systems worldwide. • Agentic AI offers autonomy and scalability for key challenges in medical and healthcare innovation. • Agentic AI enhances diagnostics, decision support, patient care, treatment planning, and robotic surgery. • Multimodal AI enables precise, context-aware, patient-centric care with iterative refinement. • Unlocking agentic AI’s potential requires ethical, privacy, and governance collaboration.

Informatics and HealthVol. 2(2)
Memorial Sloan Kettering Cancer Center (US)
National Institutes of Health, National Cancer Institute
Openalex Percentile: Top 0%
Machine Learning in Healthcare
161.26
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