# Lessons Learned from Designing an AI-Enabled Diagnosis Tool for Pathologists

arXiv: https://arxiv.org/abs/2006.12695 · HTML: https://mlsanity.com/paper/2006.12695/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 9 of 9)

- [The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice](https://mlsanity.com/paper/2310.00907/) (2023)
- [A Systematic Literature Review of Human-Centered, Ethical, and Responsible AI](https://mlsanity.com/paper/2302.05284/) (2023)
- [GANravel: User-Driven Direction Disentanglement in Generative Adversarial Networks](https://mlsanity.com/paper/2302.00079/) (2023)
- [Factors that influence the adoption of human-AI collaboration in clinical decision-making](https://mlsanity.com/paper/2204.09082/) (2022)
- [Unpacking Human-AI Interaction in Safety-Critical Industries: A Systematic Literature Review](https://mlsanity.com/paper/2310.03392/) (2023)
- [Supporting Experts with a Multimodal Machine-Learning-Based Tool for Human Behavior Analysis of Conversational Videos](https://mlsanity.com/paper/2402.11145/) (2024)
- [The AI-DEC: A Card-based Design Method for User-centered AI Explanations](https://mlsanity.com/paper/2405.16711/) (2024)
- [Hierarchical Decision Ensembles- An inferential framework for uncertain Human-AI collaboration in forensic examinations](https://mlsanity.com/paper/2111.01131/) (2021)
- ["It depends": Configuring AI to Improve Clinical Usefulness Across Contexts](https://mlsanity.com/paper/2407.11978/) (2024)

## Abstract (arXiv)

Despite the promises of data-driven artificial intelligence (AI), little is known about how we can bridge the gulf between traditional physician-driven diagnosis and a plausible future of medicine automated by AI. Specifically, how can we involve AI usefully in physicians' diagnosis workflow given that most AI is still nascent and error-prone (e.g., in digital pathology)? To explore this question, we first propose a series of collaborative techniques to engage human pathologists with AI given AI's capabilities and limitations, based on which we prototype Impetus - a tool where an AI takes various degrees of initiatives to provide various forms of assistance to a pathologist in detecting tumors from histological slides. We summarize observations and lessons learned from a study with eight pathologists and discuss recommendations for future work on human-centered medical AI systems.