# A Review of Challenges and Opportunities in Machine Learning for Health

arXiv: https://arxiv.org/abs/1806.00388 · HTML: https://mlsanity.com/paper/1806.00388/

## Key facts

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

## Built on by (most cited first, 30 of 68)

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- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
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- [Health-LLM: Personalized Retrieval-Augmented Disease Prediction System](https://mlsanity.com/paper/2402.00746/) (2024)
- [Distribution-Free Federated Learning with Conformal Predictions](https://mlsanity.com/paper/2110.07661/) (2021)
- [From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare](https://mlsanity.com/paper/2409.09727/) (2024)
- [AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modelling](https://mlsanity.com/paper/2411.17284/) (2024)
- [Multimodal data matters: language model pre-training over structured and unstructured electronic health records](https://mlsanity.com/paper/2201.10113/) (2022)
- [Does "AI" stand for augmenting inequality in the era of covid-19 healthcare?](https://mlsanity.com/paper/2105.07844/) (2021)
- [Fed-BioMed: Open, Transparent and Trusted Federated Learning for Real-world Healthcare Applications](https://mlsanity.com/paper/2304.12012/) (2023)
- [In the Name of Fairness: Assessing the Bias in Clinical Record De-identification](https://mlsanity.com/paper/2305.11348/) (2023)
- [Clinically Labeled Contrastive Learning for OCT Biomarker Classification](https://mlsanity.com/paper/2305.15154/) (2023)
- [Machine Learning for Mechanical Ventilation Control](https://mlsanity.com/paper/2102.06779/) (2021)
- [Synthetic Data in Healthcare](https://mlsanity.com/paper/2304.03243/) (2023)
- [Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions](https://mlsanity.com/paper/2107.09546/) (2021)
- [MedGPTEval: A Dataset and Benchmark to Evaluate Responses of Large Language Models in Medicine](https://mlsanity.com/paper/2305.07340/) (2023)
- [Model-driven Engineering for Machine Learning Components: A Systematic Literature Review](https://mlsanity.com/paper/2311.00284/) (2023)
- [Imputation Strategies Under Clinical Presence: Impact on Algorithmic Fairness](https://mlsanity.com/paper/2208.06648/) (2022)
- [Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond](https://mlsanity.com/paper/2309.00064/) (2023)
- [MRxaI: Black-Box Explainability for Image Classifiers in a Medical Setting](https://mlsanity.com/paper/2311.14471/) (2023)
- [Understanding Disparities in Post Hoc Machine Learning Explanation](https://mlsanity.com/paper/2401.14539/) (2024)
- [What is Fair? Defining Fairness in Machine Learning for Health](https://mlsanity.com/paper/2406.09307/) (2024)
- [SynEHRgy: Synthesizing Mixed-Type Structured Electronic Health Records using Decoder-Only Transformers](https://mlsanity.com/paper/2411.13428/) (2024)
- [Causal Fairness Assessment of Treatment Allocation with Electronic Health Records](https://mlsanity.com/paper/2211.11183/) (2022)
- [TEE4EHR: Transformer Event Encoder for Better Representation Learning in Electronic Health Records](https://mlsanity.com/paper/2402.06367/) (2024)
- [Incorporating Domain Knowledge into Health Recommender Systems using Hyperbolic Embeddings](https://mlsanity.com/paper/2106.07720/) (2021)
- [Generalization in Healthcare AI: Evaluation of a Clinical Large Language Model](https://mlsanity.com/paper/2402.10965/) (2024)
- [Leveraging MIMIC Datasets for Better Digital Health: A Review on Open Problems, Progress Highlights, and Future Promises](https://mlsanity.com/paper/2506.12808/) (2025)
- [A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform to Facilitate Healthcare AI Research](https://mlsanity.com/paper/2112.06883/) (2021)

## 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.