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2025 review

Transforming Healthcare through Generative and Agentic AI: A Systematic Review

5Citations signalées — pas une note de qualité
1Institutions déclarées
1Pays d’affiliation déclarés

Résumé fourni par la source

Generative AI (GenAI) and Agentic AI are considered the most important state-of-the-art approaches that are shaping the future of healthcare solutions. This systematic literature review (SLR) examines the capabilities & applications as well as clinical benefits for both Generative AI (GenAI) and the Agentic AI in healthcare. This review started with the identification of 87 works from Scopus and PubMed databases. After applying eligibility criteria, duplicates removal, and full-text screening, a total of 18 records published between 2024 and 2025 were included for the final study. The review revealed how Generative AI and Agentic AI are being applied across key areas in healthcare. GenAI is mainly used for diagnostics, automated documentation, and simulation-based training, while Agentic AI supports autonomous tasks and interactive multilingual tools. The findings show that successful AI adoption depends not just on technology, but also on thoughtful workflow design, clinician expertise, organizational readiness, and system integration. Thematic opportunities emerged in workflow optimization, clinical decision support, training and knowledge management, and patient engagement. Although research on complex and goal-directed Agentic AI applications is still limited, these AI approaches offer significant promise to enhance healthcare delivery and guide practical, responsible implementation.

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Contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Transforming Healthcare through Generative and Agentic AI: A Systematic Review
Date Crossref
06/11/2025
Éditeur
IEEE
Type
proceedings-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude et ne compte pas comme une seconde source scientifique indépendante.

Institutions déclarées

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Sujets associés

Artificial Intelligence in Healthcare and EducationMachine Learning in HealthcareElectronic Health Records Systems

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