Analyzing the impact of AI in healthcare: Trends, myths, challenges, and future directions

Anshika Jangra; Maidul Islam; Kuldeep Gurjar; Swati Sharma; Anshika Jangra; Maidul Islam; Kuldeep Gurjar; Swati Sharma

doi:10.3934/publichealth.2026050

AIMS Public Health

2026, Volume 13, Issue 4: 947-975. doi: 10.3934/publichealth.2026050

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Review

Analyzing the impact of AI in healthcare: Trends, myths, challenges, and future directions

1.
Department of Global Talent, The University of Suwon, Hwaseong-si, South Korea
2.
Department of International Business, Keimyung University, Daegu, South Korea
3.
School of Pharmacy, Sungkyunkwan University, South Korea

Received: 28 February 2026 Revised: 17 May 2026 Accepted: 10 June 2026 Published: 28 September 2026

The integration of artificial intelligence (AI) is significantly transforming healthcare, offering substantial improvements in efficiency, diagnostics, and patient care. This structured narrative review synthesizes peer-reviewed evidence retrieved from PubMed, Scopus, Web of Science, and Google Scholar (January 2017–October 2025) and applies an explicit eligibility protocol to map current applications, trends, and limitations of AI in healthcare. We explicitly distinguish three families of AI relevant to clinical practice—predictive AI (discriminative machine-learning models for risk stratification and diagnosis), generative AI (large language models and generative adversarial networks producing synthetic content), and agentic AI (goal-directed autonomous systems)—and analyze their distinct decision-making loci, adoption pathways, and complementarities with healthcare systems. Beyond technological advancements, this paper addresses prevalent misconceptions and myths surrounding AI in healthcare. These common misunderstandings often involve concerns about AI replacing human professionals, its impact on costs, and the absolute reliability of AI-driven decisions. Dispelling these myths is crucial for fostering informed understanding and professional acceptance. Furthermore, we provide an in-depth analysis of the multifaceted challenges that hinder AI's effective integration. These encompass technical hurdles like data quality and interpretability, alongside critical ethical, social, economic, and legal considerations. By synthesizing current developments and critically evaluating both potential benefits and obstacles, this review offers a balanced perspective on the present state and future direction of AI in healthcare. It emphasizes the need for strategic planning, ethical governance, and collaborative efforts to navigate these challenges responsibly and harness AI's transformative capacity for improved global health outcomes.
- artificial intelligence,
- digital healthcare,
- AI in healthcare trends,
- challenges of AI in healthcare,
- societal impact of AI in healthcare,
- predictive AI,
- generative AI,
- agentic AI,
- structured narrative review
Citation: Anshika Jangra, Maidul Islam, Kuldeep Gurjar, Swati Sharma. Analyzing the impact of AI in healthcare: Trends, myths, challenges, and future directions[J]. AIMS Public Health, 2026, 13(4): 947-975. doi: 10.3934/publichealth.2026050

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Abstract

The integration of artificial intelligence (AI) is significantly transforming healthcare, offering substantial improvements in efficiency, diagnostics, and patient care. This structured narrative review synthesizes peer-reviewed evidence retrieved from PubMed, Scopus, Web of Science, and Google Scholar (January 2017–October 2025) and applies an explicit eligibility protocol to map current applications, trends, and limitations of AI in healthcare. We explicitly distinguish three families of AI relevant to clinical practice—predictive AI (discriminative machine-learning models for risk stratification and diagnosis), generative AI (large language models and generative adversarial networks producing synthetic content), and agentic AI (goal-directed autonomous systems)—and analyze their distinct decision-making loci, adoption pathways, and complementarities with healthcare systems. Beyond technological advancements, this paper addresses prevalent misconceptions and myths surrounding AI in healthcare. These common misunderstandings often involve concerns about AI replacing human professionals, its impact on costs, and the absolute reliability of AI-driven decisions. Dispelling these myths is crucial for fostering informed understanding and professional acceptance. Furthermore, we provide an in-depth analysis of the multifaceted challenges that hinder AI's effective integration. These encompass technical hurdles like data quality and interpretability, alongside critical ethical, social, economic, and legal considerations. By synthesizing current developments and critically evaluating both potential benefits and obstacles, this review offers a balanced perspective on the present state and future direction of AI in healthcare. It emphasizes the need for strategic planning, ethical governance, and collaborative efforts to navigate these challenges responsibly and harness AI's transformative capacity for improved global health outcomes.

Authors' contribution

Conceptualization, A.J., K.G., M.I.; methodology, A.J., S.S; formal analysis, M.I., K.G, and A.J.; investigation, A.J., K.G,; writing—original draft preparation, A.J., K.G., M.I.; writing—review and editing, A.J., K.G., M.I. and S.S.; visualization, A.J., K.G., M.I.; All authors have read and agreed to the published version of the manuscript.

Conflict of interest

All the authors declare that there is no conflict of interest.

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