ML4H 2025 invites submissions describing innovative research that lies in the broad purview of Machine Learning for Health. Authors are invited to submit work on relevant problems in a variety of health-related disciplines including healthcare, biomedicine, and public health. This year, ML4H 2025 will accept submissions for two distinct tracks: the Proceedings track, for formal archival publications, and the non-archival Findings track.

In response to the growing ML4H community, ML4H has transitioned into a standalone symposium rather than a NeurIPS-affiliated workshop. This event represents a continuation of prior ML4H workshops/symposiums (2016, 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024) and will continue to be held in December directly before NeurIPS. ML4H 2025 will feature:

Submission Tracks
ML4H 2025 will feature two main symposium submission tracks.

Submissions to the main tracks will undergo double-blind peer review. Accepted submissions to all tracks will be featured at the event’s poster session. Accepted works for all tracks will be chosen based on their technical merit and contribution to the event. More details on how to write an excellent ML4H full paper or findings paper can be found here. The salient differences between these tracks are described below.

(A) Proceedings Track
Excellent ML4H Proceedings papers should be compelling, cohesive works with a high degree of technical sophistication as well as clear and high-impact relevance to health. Accepted proceedings papers will be published in the Proceedings for Machine Learning Research (PMLR). Full proceedings papers can be up to 8 pages at submission (excluding references and appendices). If your submission is accepted, you will be allowed 1 additional content page for the camera-ready version.

Dual submission policy: Papers that are submitted to the ML4H proceedings track cannot be already published or under review in any other archival venue. Similarly, papers published to the ML4H proceedings may not be published again later at any other venue.

(B) Findings Track
An excellent findings paper is one that leads to insights at the event through interaction with other attendees. This can be through presenting new ideas/ways of thinking, leading to insightful discussion and feedback, dissemination of new valuable resources, or enabling new opportunities for collaborations. We also especially solicit “non-traditional research artifacts” as submissions to the findings track, such as papers highlighting novel datasets, insightful negative results, exciting preliminary results that warrant rapid dissemination, reproducibility studies, and opinion pieces or critiques.

Findings papers can be up to 4 pages at submission (excluding references and appendices), though additional information not critical for understanding the work can be included in an appendix without penalty (reviewers will review the work based predominantly on the main text). Findings papers will not appear in the ML4H proceedings, but upon acceptance, we invite (but do not require) authors to submit their findings (no page limit) to the ML4H arxiv.org index.

Authors of accepted findings papers (non-archival submissions) retain full copyright of their work, and acceptance of such a submission to ML4H 2025 does not preclude publication of the same material in another archival venue (e.g. journal or conference). Furthermore, findings submissions that are under review or have been recently published in a conference or a journal are allowed; if this is the case, authors should clearly state any overlapping published or submitted work at the time of submission (in the confidential comments), and must ensure that they are not violating any other venue’s dual submission policies.

Track Switching
Proceedings submissions that are not accepted will automatically be considered for Findings. Findings track submissions cannot be considered for the Proceedings track. Decisions for both tracks will be released simultaneously.

Reciprocal Reviewing Requirement
To support a high-quality and equitable review process, AHLI is introducing a new author reviewing policy based on submission volume.

Every submission must include at least one author registered to review a minimum of three (3) papers. A qualified reviewer will have at least one prior archival publication at a comparable peer-reviewed venue (for example, a ML for health conference, or a health-focused paper at an ML venue).

If none of the authors meet this qualification, the submission is exempt from this requirement. We welcome and encourage submissions from first-time contributors at ML4H. Authors serving as Area Chairs, Senior Area Chairs, or in other organizing roles for ML4H 2025 are exempt from this requirement.

Authors of each submission must nominate at least one reciprocal reviewer at the time of submission. If an author is the nominated reciprocal reviewer for several papers, their reviewing load may increase accordingly.

If no author is registered as a reviewer by the specified deadline, the submission may be desk rejected. Failure to adequately complete assigned reviews by the rebuttal deadline may result in desk rejection of all associated submissions. Exceptions may be granted at the discretion of the Program Chairs.

Submission Areas
Submitted papers should describe innovative machine learning research focused on relevant problems in health-related disciplines. Past works have spanned data integration, temporal models, deep learning, semi-supervised learning, reinforcement learning, transfer learning, few/zero shot learning, learning from missing or biased data, learning from non-stationary data, causality, model biases, model evaluation, model criticism, model interpretability, model deployment, human-computer interaction, privacy/security, and many more topics. General areas of interest include but are not limited to the following. (Note: these areas are non-exhaustive.)

Area 1: Models and Methods: Algorithms, Inference, and Estimation
Description
Advances in machine learning are critical for a better understanding of health. This track seeks technical contributions in modeling, inference, and estimation in health-focused or health-inspired settings. We welcome submissions that develop novel methods and algorithms, introduce relevant machine learning tasks, identify challenges with prevalent approaches, or learn from multiple sources of data (e.g. non-clinical and clinical data).

Our focus on health is broadly construed, including clinical healthcare, public health, and population health. While submissions should be primarily motivated by problems relevant to health, the contributions themselves are not required to be directly applied to real health data. For example, authors may use synthetic datasets to demonstrate properties of their proposed algorithms.

We welcome submissions from many perspectives, including but not limited to supervised learning, unsupervised learning, reinforcement learning, causal inference, representation learning, survival analysis, domain adaptation or generalization, interpretability, robustness, and algorithmic fairness. All kinds of health-relevant data types are in scope, including tabular health records, time series, text, images, videos, knowledge graphs, and more. We welcome all kinds of methodologies, from deep learning to probabilistic modeling to rigorous theory and beyond.

Example Papers
Kim V, Schneider L, Kalaie S, O’Regan D, Bender C. “HeartMAE: Advancing Cardiac MRI Analysis through Optical Flow Guided Masked Autoencoding.” Proceedings of the 4th Machine Learning for Health Symposium (ML4H), 2024.

Khanna, S., Michael, D., Zitnik, M., Rajpurkar, P. “Learning Generalized Medical Image Representations Through Image-Graph Contrastive Pretraining.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Mao, H., Liu, H., Dou, J. X., Benos, P. V. “Towards Cross-Modal Causal Structure and Representation Learning.” Proceedings of the 2nd Machine Learning for Health Symposium (ML4H), 2022.

Moor, M., Huang, Q., Wu, S., Yasunaga, M., Dalmia, Y., Leskovec, J., Zakka, C., Reis, E. P., Rajpurkar, P. “Med-Flamingo: A Multimodal Medical Few-Shot Learner.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Wang, K. A., Fox, E. B. “Interpretable Mechanistic Representations for Meal-Level Glycemic Control in the Wild.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Tang, S., Dunnmon, J. A., Qu, L., Saab, K. K., Baykaner, T., Lee-Messer, C., Rubin, D. L. “Modeling Multivariate Biosignals With Graph Neural Networks and Structured State Space Models.” Proceedings of the Conference on Health, Inference, and Learning (CHIL), 2023.

Shalit, U., Johansson, F.D., Sontag, D. “Estimating individual treatment effect: generalization bounds and algorithms.” Proceedings of the 34th International Conference on Machine Learning (ICML), 2017.

Area 2: Applications and Practice: Investigation, Evaluation, Interpretation, and Deployment
Description
The goal of this track is to highlight works applying robust methods, models, or practices to identify, characterize, audit, evaluate, or benchmark ML approaches to healthcare problems. Additionally, we welcome unique deployments and datasets used to empirically evaluate these systems are necessary and important to advancing practice. Whereas the goal of Track 1 is to select papers that show significant algorithmic novelty, submit your work here if the contribution is describing an emerging or established innovative application of ML in healthcare. Areas of interest include but are not limited to:

Datasets and simulation frameworks for addressing gaps in ML healthcare applications
Tools and platforms that facilitate integration of AI algorithms and deployment for healthcare applications
Innovative ML-based approaches to solving a practical problems grounded in a healthcare application
Surveys, benchmarks, evaluations and best practices of using ML in healthcare
Emerging applications of AI in healthcare
Introducing a new method is not prohibited by any means for this track, but the focus should be on the extent of how the proposed ideas contribute to addressing a practical limitation (e.g., robustness, computational scalability, improved performance). We encourage submissions in both more traditional clinical areas (e.g., electronic health records (EHR), medical image analysis), as well as in emerging fields (e.g., remote and telehealth medicine, integration of omics).

Example Papers
Gupta A, Kocielnik R, Wang J, Nasriddinov F, Yang C, Wong E, Anandkumar A, Hung A. “Multi-Modal Self-Supervised Learning for Surgical Feedback Effectiveness Assessment.” Proceedings of the 4th Machine Learning for Health Symposium (ML4H), 2024.

Goel, A., Gueta, A., Gilon, O., Liu, C., Erell, S., Nguyen, L. H., Hao, X., et al. “LLMs Accelerate Annotation for Medical Information Extraction.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Gröger, F., Lionetti, S., Gottfrois, P., Gonzalez-Jimenez, A., Groh, M., Daneshjou, R., Labelling Consortium, Navarini, A. A., Pouly, M. “Towards Reliable Dermatology Evaluation Benchmarks.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Kocielnik, R., Wong, E. Y., Chu, T. N., Lin, L., Huang, D.-A., Wang, J., Anandkumar, A., Hung, A. J. “Deep Multimodal Fusion for Surgical Feedback Classification.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Nguyen, D., Chen, C., He, H., Tan, C. “Pragmatic Radiology Report Generation.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Kinyanjui, N.M., Johansson, F.D. ADCB: “An Alzheimer’s disease simulator for benchmarking observational estimators of causal effects”. Proceedings of the Conference on Health, Inference, and Learning (CHIL), 2022.

Zhou, H., Chen, Y., Lipton, Z. “Evaluating Model Performance in Medical Datasets Over Time”. Proceedings of the Conference on Health, Inference, and Learning (CHIL), 2023.

Area 3: Impact and Society: Policy, Public Health, Social Outcomes, and Economics
Description
Algorithms do not exist in a vacuum: instead, they often explicitly aim for important social outcomes. This track considers issues at the intersection of algorithms and the societies they seek to impact, specifically for health. Submissions could include methodological contributions such as algorithmic development and performance evaluation for policy and public health applications, large-scale or challenging data collection, combining clinical and non-clinical data, as well as detecting and measuring bias. Submissions could also include impact-oriented research such as determining how algorithmic systems for health may introduce, exacerbate, or reduce inequities and inequalities, discrimination, and unjust outcomes, as well as evaluating the economic implications of these systems. We invite submissions tackling the responsible design of AI applications for healthcare and public health. System design for the implementation of such applications at scale is also welcome, which often requires balancing various tradeoffs in decision-making. Submissions related to understanding barriers to the deployment and adoption of algorithmic systems for societal-level health applications are also of interest. In addressing these problems, insights from social sciences, law, clinical medicine, and the humanities can be crucial.

Example Papers
Guerra-Adames A, Avalos M, Dorémus O, Gil-Jardiné C, Lagarde E. “Uncovering Judgment Biases in Emergency Triage: A Public Health Approach Based on Large Language Models.” Proceedings of the 4th Machine Learning for Health Symposium (ML4H), 2024.

Afzal, M. M., Khan, M. O., Mirza, S. “Towards Equitable Kidney Tumor Segmentation: Bias Evaluation and Mitigation.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Cheng, J. J., Huling, J. D., Chen, G. “Meta-Analysis of Individualized Treatment Rules via Sign-Coherency.” Proceedings of the 2nd Machine Learning for Health Symposium (ML4H), 2022.

Gupta, M., Gallamoza, B., Cutrona, N., Dhakal, P., Poulain, R., Beheshti, R. “An Extensive Data Processing Pipeline for MIMIC-IV.” Proceedings of the 2nd Machine Learning for Health Symposium (ML4H), 2022.

Khan, M. O., Afzal, M. M., Mirza, S., Fang, Y. “How Fair Are Medical Imaging Foundation Models?.” Proceedings of the 3rd Machine Learning for Health Symposium (ML4H), 2023.

Lopez-Martinez, D., Yakubovich, A., Seneviratne, M., Lelkes, A. D., Tyagi, A., Kemp, J., Steinberg, E., et al. “Instability in Clinical Risk Stratification Models Using Deep Learning.” Proceedings of the 2nd Machine Learning for Health Symposium (ML4H), 2022.

Merrill, M., Safranchik, E., Kolbeinsson, A., Gade, P., Ramirez, E., Schmidt, L., Foschini, L., Althoff, T. “Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Datset with Laboratory Tested Ground Truth of Influenza Infections.” Proceedings of the Conference on Health, Inference, and Learning (CHIL), 2023.

Demos Track
There is a growing need for the evaluation of the challenges, solutions, and maturity of real-world ML4H tools. The Demo track invites submissions which showcase real-world applications of ML4H technologies, bridging the gap from proof-of-concept to practical utility. Accepted submissions will be non-archival and have the opportunity to present their live demo during the symposium. Check the Call for Demos for more details to come.