mlsanityData to 14 Oct 2026

A Review of Challenges and Opportunities in Machine Learning for Health

arXiv 1806.00388 ↗First posted 1 Jun 2018cs.LG cs.CY stat.MLcited by 68

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Abstract (arXiv)

Modern electronic health records (EHRs) provide data to answer clinically meaningful questions. The growing data in EHRs makes healthcare ripe for the use of machine learning. However, learning in a clinical setting presents unique challenges that complicate the use of common machine learning methodologies. For example, diseases in EHRs are poorly labeled, conditions can encompass multiple underlying endotypes, and healthy individuals are underrepresented. This article serves as a primer to illuminate these challenges and highlights opportunities for members of the machine learning community to contribute to healthcare.

Key facts. Cited by 68 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Jun 2018.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 9; 2022: 9; 2023: 13; 2024: 17; 2025: 6; 2026: 14.

68 papers.

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  1. 2021Federated Learning: Opportunities and Challengescited by 84
  2. 2024Building Machines that Learn and Think with Peoplecited by 73
  3. 2023Auxiliary Losses for Learning Generalizable Concept-based Modelscited by 35
  4. 2024Application-Driven Innovation in Machine Learningcited by 24
  5. 2024Health-LLM: Personalized Retrieval-Augmented Disease Prediction Systemcited by 17
  6. 2021Distribution-Free Federated Learning with Conformal Predictionscited by 16
  7. 2024From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcarecited by 16
  8. 2024AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modellingcited by 15
  9. 2022Multimodal data matters: language model pre-training over structured and unstructured electronic health recordscited by 13
  10. 2021Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?cited by 10
  11. 2023Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applicationscited by 10
  12. 2023In the Name of Fairness: Assessing the Bias in Clinical Record De-identificationcited by 9
38 more
  1. 2023Clinically Labeled Contrastive Learning for OCT Biomarker Classificationcited by 9
  2. 2021Machine Learning for Mechanical Ventilation Controlcited by 8
  3. 2023Synthetic Data in Healthcarecited by 8
  4. 2021Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutionscited by 7
  5. 2023MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicinecited by 7
  6. 2023Model-driven Engineering for Machine Learning Components: A Systematic Literature Reviewcited by 7
  7. 2022Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairnesscited by 6
  8. 2023Ethical Framework for Harnessing the Power of AI in Healthcare and Beyondcited by 6
  9. 2023MRxaI: Black-Box Explainability for Image Classifiers in a Medical Settingcited by 6
  10. 2024Understanding Disparities in Post Hoc Machine Learning Explanationcited by 6
  11. 2024What is Fair? Defining Fairness in Machine Learning for Healthcited by 5
  12. 2024SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformerscited by 5
  13. 2022Causal Fairness Assessment of Treatment Allocation with Electronic Health Recordscited by 3
  14. 2024TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Recordscited by 3
  15. 2021Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddingscited by 2
  16. 2024Generalization in Healthcare AI: Evaluation of a Clinical Large Language Modelcited by 2
  17. 2025Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promisescited by 2
  18. 2021A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform to Facilitate Healthcare AI Researchcited by 1
  19. 2022Sex Trouble: Common pitfalls in incorporating sex/gender in medical machine learning and how to avoid themcited by 1
  20. 2022Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnosticscited by 1
  21. 2022Boosting the interpretability of clinical risk scores with intervention predictionscited by 1
  22. 2022Learning predictive checklists from continuous medical datacited by 1
  23. 2023Temporal Causal Mediation through a Point Process: Direct and Indirect Effects of Healthcare Interventionscited by 1
  24. 2024HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aidcited by 1
  25. 2025FairTabGen: High-Fidelity and Fair Synthetic Health Data Generation from Limited Samplescited by 1
  26. 2026GRASP: group-Shapley feature selection for patientscited by 1
  27. 2026FeatEHR-LLM: Leveraging Large Language Models for Feature Engineering in Electronic Health Recordscited by 1
  28. 2021Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?cited by 0
  29. 2021Machine Learning for Mechanical Ventilation Control (Extended Abstract)cited by 0
  30. 2022The Silent Problem -- Machine Learning Model Failure -- How to Diagnose and Fix Ailing Machine Learning Modelscited by 0
  31. 2022When Performance is not Enough -- A Multidisciplinary View on Clinical Decision Supportcited by 0
  32. 2023Interpretability from a new lens: Integrating Stratification and Domain knowledge for Biomedical Applicationscited by 0
  33. 2023Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatmentcited by 0
  34. 2023Learning and DiSentangling Patient Static Information from Time-series Electronic HEalth Record (STEER)cited by 0
  35. 2024OnDev-LCT: On-Device Lightweight Convolutional Transformers towards federated learningcited by 0
  36. 2024Comparison of Machine Learning Classification Algorithms and Application to the Framingham Heart Studycited by 0
  37. 2024Evaluating Pre-Training Bias on Severe Acute Respiratory Syndrome Datasetcited by 0
  38. 2024Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2cited by 0

50 most cited of 68 All 68 (JSON)