Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges.

Olawade, David; Plabon, Shamiul Bashir; Ojo, Adeyinka; Ogunbona, Muyiwa Ademola; Makanjuola, Babajide David; Olasilola, Omobolaji Rosemary

Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges.

Olawade, David ORCID: https://orcid.org/0000-0003-0188-9836, Plabon, Shamiul Bashir, Ojo, Adeyinka, Ogunbona, Muyiwa Ademola, Makanjuola, Babajide David and Olasilola, Omobolaji Rosemary (2026) Human in the loop artificial intelligence in healthcare: applications, outcomes, and implementation challenges. International Journal of Medical Informatics, 213. p. 106362.

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Official URL: https://doi.org/10.1016/j.ijmedinf.2026.106362

Abstract

The integration of artificial intelligence in healthcare has transformed clinical practice and research methodologies. However, concerns regarding algorithmic accountability, interpretability, and safety have necessitated human oversight in AI systems. Human in the loop artificial intelligence represents a collaborative paradigm where human expertise and machine intelligence converge to enhance decision making while maintaining ethical standards and clinical safety. This review synthesizes current evidence on human in the loop AI in healthcare delivery and research, examining implementation frameworks, clinical outcomes, comparative advantages over fully automated and clinician-only approaches, and challenges. A comprehensive narrative review was conducted using PubMed, Scopus, Web of Science, and IEEE Xplore databases covering studies from 2018 to 2025. Data were thematically synthesized to identify patterns, frameworks, and outcomes. This narrative approach enables comprehensive conceptual synthesis across diverse HITL-AI applications and contexts. Human in the loop AI demonstrates significant applications across diagnostic imaging, clinical decision support, patient monitoring, drug discovery, and research data analysis. Evidence indicates improved diagnostic accuracy, reduced medical errors, enhanced patient safety, and increased clinician trust compared to both automated AI and traditional approaches. Implementation requires EHR interoperability, clear liability frameworks, adaptive training protocols, and quantum-safe cryptographic security. Challenges include workflow integration, regulatory gaps for adaptive systems, and sustainability concerns. This review advances the field by synthesizing cross-domain implementation patterns, mapping collaboration models to risk-stratified contexts, identifying regulatory gaps for adaptive systems, and proposing future directions including post-quantum cryptographic integration, AI-driven adaptive architectures, and multi-center scalability frameworks for optimizing human-machine collaboration in healthcare. [Abstract copyright: Copyright © 2026 The Author(s). Published by Elsevier B.V. All rights reserved.]