# Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025

arXiv: https://arxiv.org/abs/2510.15217 · HTML: https://mlsanity.com/paper/2510.15217/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 41 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 26 of 26)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [Inherent Trade-Offs in the Fair Determination of Risk Scores](https://mlsanity.com/paper/1609.05807/) (2016)
- [Justice or Prejudice? Quantifying Biases in LLM-as-a-Judge](https://mlsanity.com/paper/2410.02736/) (2024)
- [Ethical Machine Learning in Health Care](https://mlsanity.com/paper/2009.10576/) (2020)
- [Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directions](https://mlsanity.com/paper/2404.03264/) (2024)
- [Integrated multimodal artificial intelligence framework for healthcare applications](https://mlsanity.com/paper/2202.12998/) (2022)
- [Causal inference methods for combining randomized trials and observational studies: a review](https://mlsanity.com/paper/2011.08047/) (2020)
- [An Empirical Characterization of Fair Machine Learning For Clinical Risk Prediction](https://mlsanity.com/paper/2007.10306/) (2020)
- [A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Models](https://mlsanity.com/paper/2403.12025/) (2024)
- [A Review of Generalizability and Transportability](https://mlsanity.com/paper/2102.11904/) (2021)
- [Review of multimodal machine learning approaches in healthcare](https://mlsanity.com/paper/2402.02460/) (2024)
- [A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdom](https://mlsanity.com/paper/2306.11963/) (2023)
- [Uncertainty Quantification for Machine Learning in Healthcare: A Survey](https://mlsanity.com/paper/2505.02874/) (2025)
- [Quantifying disparities in intimate partner violence: a machine learning method to correct for underreporting](https://mlsanity.com/paper/2110.04133/) (2021)
- [Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?](https://mlsanity.com/paper/2303.16008/) (2023)
- [On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Model](https://mlsanity.com/paper/2310.05598/) (2023)
- [Using large language models to promote health equity](https://mlsanity.com/paper/2312.14804/) (2023)
- [Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairness](https://mlsanity.com/paper/2506.04193/) (2025)
- [Towards Generalizing Inferences from Trials to Target Populations](https://mlsanity.com/paper/2402.17042/) (2024)
- [Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysis](https://mlsanity.com/paper/2501.16933/) (2025)
- [Assessing Phenotype Definitions for Algorithmic Fairness](https://mlsanity.com/paper/2203.05174/) (2022)
- [A Unified Framework for the Transportability of Population-Level Causal Measures](https://mlsanity.com/paper/2505.13104/) (2025)
- [Learning Disease Progression Models That Capture Health Disparities](https://mlsanity.com/paper/2412.16406/) (2024)

## Built on by (most cited first, 4 of 4)

- [SP-Mind: An Autonomous Reasoning Agent for Spatial Proteomics Analysis](https://mlsanity.com/paper/2606.24235/) (2026)
- [Discriminative Representation Learning for Clinical Prediction](https://mlsanity.com/paper/2603.20921/) (2026)
- [Dense Feature Learning via Linear Structure Preservation in Medical Data](https://mlsanity.com/paper/2602.07706/) (2026)
- [DR-LabStack: Design and Implementation of a Clinician-Facing Web System for Diabetic Retinopathy Prediction](https://mlsanity.com/paper/2609.10796/) (2026)

## Abstract (arXiv)

The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of "Explainability, Interpretability, and Transparency," "Uncertainty, Bias, and Fairness," "Causality," "Domain Adaptation," "Foundation Models," "Learning from Small Medical Data," "Multimodal Methods," and "Scalable, Translational Healthcare Solutions."