# Probabilistic Medical Predictions of Large Language Models

arXiv: https://arxiv.org/abs/2408.11316 · HTML: https://mlsanity.com/paper/2408.11316/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 50 references that are in the map.
- First posted on arXiv 21 Aug 2024.

## Builds on (most cited first, 26 of 26)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Qwen2 Technical Report](https://mlsanity.com/paper/2407.10671/) (2024)
- [Gemma 2: Improving Open Language Models at a Practical Size](https://mlsanity.com/paper/2408.00118/) (2024)
- [Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone](https://mlsanity.com/paper/2404.14219/) (2024)
- [The Rise and Potential of Large Language Model Based Agents: A Survey](https://mlsanity.com/paper/2309.07864/) (2023)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [Chatbot Arena: An Open Platform for Evaluating LLMs by Human Preference](https://mlsanity.com/paper/2403.04132/) (2024)
- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [Yi: Open Foundation Models by 01.AI](https://mlsanity.com/paper/2403.04652/) (2024)
- [Implicit Chain of Thought Reasoning via Knowledge Distillation](https://mlsanity.com/paper/2311.01460/) (2023)
- [Benchmarking LLMs via Uncertainty Quantification](https://mlsanity.com/paper/2401.12794/) (2024)
- [Benchmarking Benchmark Leakage in Large Language Models](https://mlsanity.com/paper/2404.18824/) (2024)
- [Language Contamination Helps Explain the Cross-lingual Capabilities of English Pretrained Models](https://mlsanity.com/paper/2204.08110/) (2022)
- [Positional Description Matters for Transformers Arithmetic](https://mlsanity.com/paper/2311.14737/) (2023)
- [A Comprehensive Evaluation of Large Language Models on Benchmark Biomedical Text Processing Tasks](https://mlsanity.com/paper/2310.04270/) (2023)
- [NumeroLogic: Number Encoding for Enhanced LLMs' Numerical Reasoning](https://mlsanity.com/paper/2404.00459/) (2024)
- [Guiding Clinical Reasoning with Large Language Models via Knowledge Seeds](https://mlsanity.com/paper/2403.06609/) (2024)
- [Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making](https://mlsanity.com/paper/2204.05030/) (2022)
- [Large Language Models Leverage External Knowledge to Extend Clinical Insight Beyond Language Boundaries](https://mlsanity.com/paper/2305.10163/) (2023)
- [Understanding Token Probability Encoding in Output Embeddings](https://mlsanity.com/paper/2406.01468/) (2024)
- [Using Pretrained Large Language Model with Prompt Engineering to Answer Biomedical Questions](https://mlsanity.com/paper/2407.06779/) (2024)

## Built on by (most cited first, 16 of 16)

- [BRIDGE: Benchmarking Large Language Models for Understanding Real-world Clinical Practice Text](https://mlsanity.com/paper/2504.19467/) (2025)
- [AOR: Anatomical Ontology-Guided Reasoning for Medical Large Multimodal Model in Chest X-Ray Interpretation](https://mlsanity.com/paper/2505.02830/) (2025)
- [MoMA: A Mixture-of-Multimodal-Agents Architecture for Enhancing Clinical Prediction Modelling](https://mlsanity.com/paper/2508.05492/) (2025)
- [MedKGent: A Large Language Model Agent Framework for Constructing Temporally Evolving Medical Knowledge Graph](https://mlsanity.com/paper/2508.12393/) (2025)
- [LLMs in Disease Diagnosis: A Comparative Study of DeepSeek-R1 and O3 Mini Across Chronic Health Conditions](https://mlsanity.com/paper/2503.10486/) (2025)
- [Rehabilitation Exercise Quality Assessment and Feedback Generation Using Large Language Models with Prompt Engineering](https://mlsanity.com/paper/2505.18412/) (2025)
- [Scalable Medication Extraction and Discontinuation Identification from Electronic Health Records Using Large Language Models](https://mlsanity.com/paper/2506.11137/) (2025)
- [Multi-modal Integration Analysis of Alzheimer's Disease Using Large Language Models and Knowledge Graphs](https://mlsanity.com/paper/2505.15747/) (2025)
- [HySemRAG: A Hybrid Semantic Retrieval-Augmented Generation Framework for Automated Literature Synthesis and Methodological Gap Analysis](https://mlsanity.com/paper/2508.05666/) (2025)
- [Towards Reliable Medical LLMs: Benchmarking and Enhancing Confidence Estimation of Large Language Models in Medical Consultation](https://mlsanity.com/paper/2601.15645/) (2026)
- [EpiScreen: Early Epilepsy Detection from Electronic Health Records with Large Language Models](https://mlsanity.com/paper/2603.28698/) (2026)
- [CLSGen: A Dual-Head Fine-Tuning Framework for Joint Probabilistic Classification and Verbalized Explanation](https://mlsanity.com/paper/2604.11801/) (2026)
- [Language-Induced Priors for Domain Adaptation](https://mlsanity.com/paper/2605.14301/) (2026)
- [TRIAGE: Dialectical Reasoning for Explainable Risk Prediction on Irregularly Sampled Medical Time Series with LLMs](https://mlsanity.com/paper/2606.09030/) (2026)
- [Evidence-Grounded AI for Musculoskeletal Care](https://mlsanity.com/paper/2607.12527/) (2026)
- [Large Language Models Show Metacognitive Sensitivity in Medical Reasoning](https://mlsanity.com/paper/2608.14552/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have shown promise in clinical applications through prompt engineering, allowing flexible clinical predictions. However, they struggle to produce reliable prediction probabilities, which are crucial for transparency and decision-making. While explicit prompts can lead LLMs to generate probability estimates, their numerical reasoning limitations raise concerns about reliability. We compared explicit probabilities from text generation to implicit probabilities derived from the likelihood of predicting the correct label token. Across six advanced open-source LLMs and five medical datasets, explicit probabilities consistently underperformed implicit probabilities in discrimination, precision, and recall. This discrepancy is more pronounced with smaller LLMs and imbalanced datasets, highlighting the need for cautious interpretation, improved probability estimation methods, and further research for clinical use of LLMs.