mlsanityData to 14 Oct 2026

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

arXiv 2510.15217 ↗First posted 17 Oct 2025cs.LGcited by 4

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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."

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

26 of 41 references.

References · 26

  1. 2017A Unified Approach to Interpreting Model Predictionscited by 6,555
  2. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  3. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  4. 2016Fair prediction with disparate impact: A study of bias in recidivism prediction instrumentscited by 672
  5. 2016Inherent Trade-Offs in the Fair Determination of Risk Scorescited by 586
  6. 2024Justice or Prejudice? Quantifying Biases in LLM-as-a-Judgecited by 368
  7. 2020Ethical Machine Learning in Health Carecited by 113
  8. 2024Foundation Model for Advancing Healthcare: Challenges, Opportunities, and Future Directionscited by 77
  9. 2022Integrated multimodal artificial intelligence framework for healthcare applicationscited by 71
  10. 2020Causal inference methods for combining randomized trials and observational studies: a reviewcited by 51
  11. 2020An Empirical Characterization of Fair Machine Learning For Clinical Risk Predictioncited by 45
  12. 2024A Toolbox for Surfacing Health Equity Harms and Biases in Large Language Modelscited by 45
14 more
  1. 2021A Review of Generalizability and Transportabilitycited by 37
  2. 2024Review of multimodal machine learning approaches in healthcarecited by 31
  3. 2023A Survey of Multimodal Information Fusion for Smart Healthcare: Mapping the Journey from Data to Wisdomcited by 25
  4. 2025Uncertainty Quantification for Machine Learning in Healthcare: A Surveycited by 14
  5. 2021Quantifying disparities in intimate partner violence: a machine learning method to correct for underreportingcited by 8
  6. 2023Risk ratio, odds ratio, risk difference... Which causal measure is easier to generalize?cited by 8
  7. 2023On Prediction-Modelers and Decision-Makers: Why Fairness Requires More Than a Fair Prediction Modelcited by 7
  8. 2023Using large language models to promote health equitycited by 6
  9. 2025Understanding challenges to the interpretation of disaggregated evaluations of algorithmic fairnesscited by 6
  10. 2024Towards Generalizing Inferences from Trials to Target Populationscited by 4
  11. 2025Rethinking the Win Ratio: A Causal Framework for Hierarchical Outcome Analysiscited by 4
  12. 2022Assessing Phenotype Definitions for Algorithmic Fairnesscited by 2
  13. 2025A Unified Framework for the Transportability of Population-Level Causal Measurescited by 2
  14. 2024Learning Disease Progression Models That Capture Health Disparitiescited by 1

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Citing papers per year: 2026: 4.

4 papers.