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Framework for developing and evaluating ethical collaboration between expert and machine

Ayan Banerjee, Payal Kamboj, Sandeep Gupta

TL;DR

A framework to develop and ethically evaluate expert-guided multi-modal AI, addressing challenges in AI integration within precision medicine is proposed and illustrated with case study on insulin management for T1D.

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.

Framework for developing and evaluating ethical collaboration between expert and machine

TL;DR

A framework to develop and ethically evaluate expert-guided multi-modal AI, addressing challenges in AI integration within precision medicine is proposed and illustrated with case study on insulin management for T1D.

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.

Paper Structure

This paper contains 8 sections, 3 figures.

Table of Contents

  1. Introduction
  2. Biomedical System Level Challenge
  3. Method
  4. Why multi-modal AI integrated with expert knowledge may improve generalizability, explainability and bioethics?
  5. Personalization of Automated Insulin Delivery for individuals with T1D
  6. Ethical statement
  7. Conclusions
  8. Acknowledgement

Figures (3)

  • Figure 1: Figure 1: Precision medicine approach with integration of digital twin and multimodal AI guided by expert knowledge.
  • Figure 2: . LLM planner for the endocrine system modeling challenge: LLMs finetuned with multi-modal data for generating safe usage plans under dynamically changing user contexts.
  • Figure 3: Task Organization for the Automated Insulin Delivery biomedical challenge. This project is a collaboration between ASU and Mayo clinic, Rochester.