Framework for developing and evaluating ethical collaboration between expert and machine

TL;DR

This paper presents a framework for ethically developing and evaluating AI systems in precision medicine, emphasizing clinician collaboration and addressing challenges like trust, explainability, and generalizability.

Contribution

It introduces a co-designed framework for ethical AI integration in precision medicine, demonstrated through a case study on insulin management for T1D.

Findings

Framework promotes clinician-AI collaboration for ethical decision-making

Addresses challenges of trust, explainability, and generalizability in AI

Case study shows practical application in T1D insulin management

Abstract

Precision medicine is a promising approach for accessible disease diagnosis and personalized intervention planning in high-mortality diseases such as coronary artery disease (CAD), drug-resistant epilepsy (DRE), and chronic illnesses like Type 1 diabetes (T1D). By leveraging artificial intelligence (AI), precision medicine tailors diagnosis and treatment solutions to individual patients by explicitly modeling variance in pathophysiology. However, the adoption of AI in medical applications faces significant challenges, including poor generalizability across centers, demographics, and comorbidities, limited explainability in clinical terms, and a lack of trust in ethical decision-making. This paper proposes a framework to develop and ethically evaluate expert-guided multi-modal AI, addressing these challenges in AI integration within precision medicine. We illustrate this framework with case study on insulin management for T1D. To ensure ethical considerations and clinician engagement, we adopt a co-design approach where AI serves an assistive role, with final diagnoses or treatment plans emerging from collaboration between clinicians and AI.