Evidence-based strategies for safe and effective AI integration in clinical practice: a review

Deekshith R. Prabhu; Dasharathraj K. Shetty; Manu Sudhi; Sharanya Hegde; Namesh Malarout; Ishaan Raina

doi:10.1201/9781003773504-85

Evidence-based strategies for safe and effective AI integration in clinical practice: a review

Deekshith R. Prabhu
, Dasharathraj K. Shetty
, Manu Sudhi
, Sharanya Hegde
, Namesh Malarout
, Ishaan Raina

School of Computer Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter

Abstract

The integration of artificial intelligence (AI) into clinical practice offers the potential to transform healthcare delivery, improving diagnosis and patient outcomes. However, its implementation comes with significant risks, including diagnostic errors and ethical challenges. This review aims to explore evidence-based strategies for the safe and effective integration of AI in clinical practice. The key strategies discussed include performance benchmarking, human-AI synergy, clinician education, and cognitive impact management. Global and Indian case studies provide practical insights into AI applications in diagnostics, decision support, and patient care. This paper concludes with recommendations for policy and future practice, emphasizing the need for balanced integration of AI technologies in clinical environments.

Original language	English
Title of host publication	Coresource 4
Publisher	CRC Press
Pages	499-504
Number of pages	6
ISBN (Electronic)	9781003773504
ISBN (Print)	9781041299028, 9781041302339
DOIs	https://doi.org/10.1201/9781003773504-85
Publication status	Published - 2026

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

General Computer Science
General Arts and Humanities
General Social Sciences
General Energy
General Engineering

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10.1201/9781003773504-85

Cite this

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AU - Malarout, Namesh

AU - Raina, Ishaan

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ER -

Evidence-based strategies for safe and effective AI integration in clinical practice: a review

Abstract

UN SDGs

All Science Journal Classification (ASJC) codes

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