Framework for developing and evaluating ethical collaboration between expert and machine
This addresses the problem of integrating AI into medical applications for clinicians and patients, but it appears incremental as it builds on existing co-design approaches.
The paper tackles challenges in AI adoption for precision medicine, such as poor generalizability and lack of trust, by proposing a framework for developing and ethically evaluating expert-guided multi-modal AI, illustrated with a case study on insulin management for Type 1 diabetes.
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.