# Framework for developing and evaluating ethical collaboration between expert and machine

arXiv: https://arxiv.org/abs/2411.10983 · HTML: https://mlsanity.com/paper/2411.10983/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 31 references that are in the map.
- First posted on arXiv 17 Nov 2024.

## Builds on (most cited first, 4 of 4)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Gemini: A Family of Highly Capable Multimodal Models](https://mlsanity.com/paper/2312.11805/) (2023)
- [Merging Deep Learning with Expert Knowledge for Seizure Onset Zone localization from rs-fMRI in Pediatric Pharmaco Resistant Epilepsy](https://mlsanity.com/paper/2306.05572/) (2023)
- [CPS-LLM: Large Language Model based Safe Usage Plan Generator for Human-in-the-Loop Human-in-the-Plant Cyber-Physical System](https://mlsanity.com/paper/2405.11458/) (2024)

## Built on by (most cited first, 3 of 3)

- [Recovering implicit physics model under real-world constraints](https://mlsanity.com/paper/2412.02215/) (2024)
- [STORM: Strategic Orchestration of Modalities for Rare Event Classification](https://mlsanity.com/paper/2412.02805/) (2024)
- [Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM](https://mlsanity.com/paper/2501.16481/) (2025)

## Abstract (arXiv)

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.