mlsanityData to 14 Oct 2026

Meta-learning in healthcare: A survey

arXiv 2308.02877 ↗First posted 5 Aug 2023cs.LG cs.AIcited by 9

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Abstract (arXiv)

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the model's capabilities by employing prior knowledge and experience. A meta-learning paradigm can appropriately tackle the conventional challenges of traditional learning approaches, such as insufficient number of samples, domain shifts, and generalization. These unique characteristics position meta-learning as a suitable choice for developing influential solutions in various healthcare contexts, where the available data is often insufficient, and the data collection methodologies are different. This survey discusses meta-learning broad applications in the healthcare domain to provide insight into how and where it can address critical healthcare challenges. We first describe the theoretical foundations and pivotal methods of meta-learning. We then divide the employed meta-learning approaches in the healthcare domain into two main categories of multi/single-task learning and many/few-shot learning and survey the studies. Finally, we highlight the current challenges in meta-learning research, discuss the potential solutions, and provide future perspectives on meta-learning in healthcare.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 76 of its 241 references that are in the map. First posted on arXiv 5 Aug 2023.

Builds on

76 of 241 references.

References · 76

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38 more
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50 most cited of 76 All 76 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 3; 2025: 5; 2026: 1.

9 papers.

  1. 2025One Subgoal at a Time: Zero-Shot Generalization to Arbitrary Linear Temporal Logic Requirements in Multi-Task Reinforcement Learningcited by 8
  2. 2024Electrocardiogram-Language Model for Few-Shot Question Answering with Meta Learningcited by 5
  3. 2024Robust Meta-Model for Predicting the Need for Blood Transfusion in Non-traumatic ICU Patientscited by 0
  4. 2024Multi-omics data integration for early diagnosis of hepatocellular carcinoma (HCC) using machine learningcited by 0
  5. 2025Riemannian Geometric-based Meta Learningcited by 0
  6. 2025Meta-Learning Approaches for Speaker-Dependent Voice Fatigue Modelscited by 0
  7. 2025Zero-Training Task-Specific Model Synthesis for Few-Shot Medical Image Classificationcited by 0
  8. 2025Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithmcited by 0
  9. 2026Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agentcited by 0