Important Ethical, Technical, and Epidemiological Considerations in an AI Tool Production (ETEPAI): Scoping Review

TL;DR

This paper introduces ETEPAI, a framework that combines ethical, technical, and epidemiological considerations for developing high-quality AI tools in healthcare.

Contribution

ETEPAI is a new, comprehensive framework that integrates ethical, technical, and epidemiological guidance across AI tool development stages.

Findings

ETEPAI synthesizes 30 existing frameworks into four stages and three domains for AI tool development.

The framework includes probing questions, key indicators, and common pitfalls to guide implementation.

ETEPAI aligns with EU trustworthiness standards and provides a research proposal template for adoption.

Abstract

Artificial intelligence (AI) tools are being developed in a rapidly evolving technology. The convergence of ethical, technical, and research methods’ considerations is crucial for multidisciplinary teams aiming to produce effective AI tools. The success of these tools postdeployment hinges on the intricate interplay between the AI system’s development on its output through rigorous decision-making processes and stakeholders’ capacity to act on the AI’s recommendations.

This paper synthesizes ethical, technical, and epidemiological considerations for all involved in artificial intelligence tool production (ETEPAI), based on established guidelines, checklists, and frameworks.

Relevant guidelines, checklists, frameworks, and expert recommendations were systematically identified and synthesized into ETEPAI, an ethical, technical, and epidemiological framework for AI tool development in health care.

From 30 reviewed frameworks, ETEPAI integrates critical considerations across 4 stages (design, development, deployment, and postdeployment) and 3 domains (ethics, technical, and epidemiological), providing a compact yet comprehensive guide. It includes probing questions, key indicators, and common pitfalls to support high-quality, ethically sound, and clinically relevant AI tools. ETEPAI aligns with European Union trustworthiness standards and is supported by a research proposal template and supplementary references to aid implementation and adoption. We present probing questions and critical pointers across 4 stages from the design, development, deployment, and postdeployment, highlighting their relevance in health care settings. The designing stage aligns with epidemiologic research methodologies, while the development stage emphasizes transparent project execution. Deployment and postdeployment stages focus on real-world implementation. Additionally included are common pitfalls and challenges to emphasize the importance of due attention to the importance of ETEPAI considerations to avoid serious consequences.

Applying ETEPAI ensures comprehensive, complete, compact, and crisp consideration from conception to execution, promoting high-quality, ethically sound, and clinically relevant AI tools. The brevity and conciseness of ETEPAI might be adequate for trained personnel and serve as clear signposts to unprepared stakeholders.

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