Medical Heuristic Learning: An LLM-Driven Framework for Interpretable and Auditable Clinical Decision Rules
Authors: Wei Xu, Ke Yang, Gang Luo, Keli Zheng, Lingyan Hu, Jing Wang, Kefeng Li
Organizations: The Centre for Artificial Intelligence Driven Drug Discovery, Macao Polytechnic University, Macao SAR · The Key Laboratory of Short-Range Radio Equipment Testing and Evaluation, Ministry of Industry and Information Technology, and the Terahertz Science Application Center (TSAC), Beijing Institute of Technology, Zhuhai, China · The Faculty of Education, The University of Hong Kong, Hong Kong SAR · The College of Information Engineering, Dalian University, Dalian, Liaoning, China · The Department of Critical Care Medicine, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China
Abstract
Predictive modeling for clinical decision support requires both strong predictive performance and transparent, auditable, and human-reviewable decision logic. Although deep learning and tree-based ensemble methods can achieve high accuracy, their black-box nature remains a major obstacle to trustworthy clinical deployment. Moreover, clinical prediction often operates under practical constraints, including limited sample sizes, severe class imbalance, and feature evolution arising from changes in diagnostic criteria or clinical documentation practices. We propose Medical Heuristic Learning (MHL), a constrained paradigm for LLM-assisted rule learning. Rather than relying on updates to implicit model weights, MHL integrates statistical probes, medical knowledge probes, initial rule synthesis, and iterative rule optimization to construct an executable rule-based expert system. The resulting rule system is expressed entirely using the native logical and control-flow constructs of a programming language. Valid rule versions are recorded and retained along the search trajectory, making the decision logic explicit, interpretable, and auditable. MHL also supports continual learning by using previously validated rules as a starting point and iteratively revising them in response to updated feature information under data drift or feature evolution. MHL is not tied to any specific programming language. Comprehensive experiments on medical datasets show that MHL achieves predictive performance comparable to that of state-of-the-art methods, performs favorably in small-sample and highly imbalanced settings, and supports the transfer and adaptive revision of validated rules under feature evolution. Overall, these findings suggest that non-gradient-based heuristic systems offer an approach to balancing predictive performance and transparency in clinical decision support.
Adapting large language models (LLMs) to clinical workflows often requires costly fine-tuning or manual prompt and pipeline engineering. We study LLM-guided MAP-Elites evolution as an inference-time alternative for discovering medical decision strategies and provide an implementation repository at https://github.com/univanxx/llm_guided_evo_medical. We formulate urgency triage, interactive consultation, and medical image classification as evolutionary searches over executable artifacts optimized by task-specific fitness functions. Across all three settings, evolution improves over manually designed baselines under practical constraints. In triage, evolved programs increase Semigran accuracy from 77.3% to 87.1% and emergency recall from 0.60 to 0.97, while improving safety-weighted held-out MIMIC-ESI performance. In interactive consultation, evolved policies improve the accuracy--cost frontier across Llama-3, Qwen-3.5, and Gemma-4 and transfer to held-out iCRAFTMD. In PneumoniaMNIST, prompt-only evolution improves frozen MedGemma VLMs while preserving strict JSON outputs. Qualitative analysis shows that the gains come from interpretable program-level mechanisms, calibrated triage boundaries, targeted evidence acquisition, selective commitment, and finding-oriented visual decision rules, rather than superficial prompt rewording alone.
Hallucinations in medical large language models (LLMs) pose serious risks for clinical decision support, particularly when models must reason over complex electronic health records (EHRs). However, existing benchmarks often lack a realistic clinical context and provide limited insight into how hallucinations can be mitigated in practice. We introduce Med-HEAL, a framework for systematically identifying, analyzing, and mitigating hallucinations in medical LLMs using clinically grounded data. Building on the EHRNoteQA benchmark derived from MIMIC-IV discharge summaries, we construct a hallucination dataset by evaluating BioMistral-7B on open-ended clinical question answering tasks. Model outputs are labeled through a dual evaluation pipeline that combines LLM-as-a-Judge assessment (GPT-4o) with human auditing by medical student reviewers, producing correctness judgments and annotations of reasoning errors via a custom web-based evaluation system. We then leverage this dataset to investigate mitigation strategies: a self-critique pipeline, in which the test model reviews its own answers to detect potential errors and regenerates responses for flagged cases, and retrieval-augmented in-context learning (RA-ICL), which exposes the model to hallucinated and corrected examples. Experiments across five open-source LLMs-BioMistral, Llama-3.1, DeepSeek, Qwen2.5, and Qwen3, show that the self-critique strategy improves accuracy for three of five models (p < 0.05) without requiring parameter updates. Med-HEAL provides both a reusable hallucination dataset and a practical framework for studying and mitigating hallucinations in medical LLMs, supporting safer deployment of AI systems in clinical environments. Our code and data are publicly available at https://github.com/yimingliao-blad/med-heal.git.
Yiming Liao, Zeno Franco, Jose Eduardo Lizarraga Mazaba +1
Continual learning aims to update models under distribution shift without forgetting, yet many high-stakes deployments, such as healthcare, also require interpretability. In practice, models that adapt well (e.g., deep networks) are often opaque, while models that are interpretable (e.g., decision trees) are brittle under shift, making it difficult to achieve both properties simultaneously. In response, we propose Tree of Concepts, an interpretable continual learning framework that uses a shallow decision tree to define a fixed, rule-based concept interface and trains a concept bottleneck model to predict these concepts from raw features. Continual updates act on the concept extractor and label head while keeping concept semantics stable over time, yielding explanations that do not drift across sequential updates. On multiple tabular healthcare benchmarks under continual learning protocols, our method achieves a stronger stability-plasticity trade-off than existing baselines, including replay-enhanced variants. Our results suggest that structured concept interfaces can support continual adaptation while preserving a consistent audit interface in non-stationary, high-stakes domains.