# Capabilities of GPT-5 on Multimodal Medical Reasoning

arXiv: https://arxiv.org/abs/2508.08224 · HTML: https://mlsanity.com/paper/2508.08224/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 10 of its 30 references that are in the map.
- First posted on arXiv 11 Aug 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 10 of 10)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Measuring Massive Multitask Language Understanding](https://mlsanity.com/paper/2009.03300/) (2020)
- [Large Language Models Encode Clinical Knowledge](https://mlsanity.com/paper/2212.13138/) (2022)
- [What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams](https://mlsanity.com/paper/2009.13081/) (2020)
- [Capabilities of GPT-4 on Medical Challenge Problems](https://mlsanity.com/paper/2303.13375/) (2023)
- [MedXpertQA: Benchmarking Expert-Level Medical Reasoning and Understanding](https://mlsanity.com/paper/2501.18362/) (2025)
- [GPT4Graph: Can Large Language Models Understand Graph Structured Data ? An Empirical Evaluation and Benchmarking](https://mlsanity.com/paper/2305.15066/) (2023)
- [EHRNoteQA: An LLM Benchmark for Real-World Clinical Practice Using Discharge Summaries](https://mlsanity.com/paper/2402.16040/) (2024)
- [Triad: Vision Foundation Model for 3D Magnetic Resonance Imaging](https://mlsanity.com/paper/2502.14064/) (2025)
- [Unifying Biomedical Vision-Language Expertise: Towards a Generalist Foundation Model via Multi-CLIP Knowledge Distillation](https://mlsanity.com/paper/2506.22567/) (2025)

## Built on by (most cited first, 30 of 36)

- [HunyuanOCR Technical Report](https://mlsanity.com/paper/2511.19575/) (2025)
- [OmniCT: Towards a Unified Slice-Volume LVLM for Comprehensive CT Analysis](https://mlsanity.com/paper/2602.16110/) (2026)
- [Capabilities of GPT-5 across critical domains: Is it the next breakthrough?](https://mlsanity.com/paper/2508.19259/) (2025)
- [Can LLMs Refuse Questions They Do Not Know? Measuring Knowledge-Aware Refusal in Factual Tasks](https://mlsanity.com/paper/2510.01782/) (2025)
- [Motion2Meaning: A Clinician-Centered Framework for Contestable LLM in Parkinson's Disease Gait Interpretation](https://mlsanity.com/paper/2512.08934/) (2025)
- [Performance of GPT-5 in Brain Tumor MRI Reasoning](https://mlsanity.com/paper/2508.10865/) (2025)
- [Benchmarking GPT-5 for Zero-Shot Multimodal Medical Reasoning in Radiology and Radiation Oncology](https://mlsanity.com/paper/2508.13192/) (2025)
- [Benchmarking GPT-5 for biomedical natural language processing](https://mlsanity.com/paper/2509.04462/) (2025)
- [OmniGround: A Comprehensive Spatio-Temporal Grounding Benchmark for Real-World Complex Scenarios](https://mlsanity.com/paper/2511.16937/) (2025)
- [MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows](https://mlsanity.com/paper/2603.24649/) (2026)
- [M3-SLU: Evaluating Speaker-Attributed Reasoning in Multimodal Large Language Models](https://mlsanity.com/paper/2510.19358/) (2025)
- [Learning from Contrasts: Synthesizing Reasoning Paths from Diverse Search Trajectories](https://mlsanity.com/paper/2604.11365/) (2026)
- [Same Verdict, Different Reasons: LLM-as-a-Judge and Clinician Disagreement on Medical Chatbot Completeness](https://mlsanity.com/paper/2604.16383/) (2026)
- [MeDxAgent: Multi-Agent Consultation for Interactive Medical Diagnosis](https://mlsanity.com/paper/2606.03416/) (2026)
- [WebGen-V Bench: Structured Representation for Enhancing Visual Design in LLM-based Web Generation and Evaluation](https://mlsanity.com/paper/2510.15306/) (2025)
- [Small Agent Group is the Future of Digital Health](https://mlsanity.com/paper/2602.08013/) (2026)
- [Bridging Time and Space: Decoupled Spatio-Temporal Alignment for Video Grounding](https://mlsanity.com/paper/2604.08014/) (2026)
- [Performance of GPT-5 Frontier Models in Ophthalmology Question Answering](https://mlsanity.com/paper/2508.09956/) (2025)
- [Radiology's Last Exam (RadLE): Benchmarking Frontier Multimodal AI Against Human Experts and a Taxonomy of Visual Reasoning Errors in Radiology](https://mlsanity.com/paper/2509.25559/) (2025)
- [Automated Structured Radiology Report Generation with Rich Clinical Context](https://mlsanity.com/paper/2510.00428/) (2025)
- [Tool-RoCo: An Agent-as-Tool Self-organization Large Language Model Benchmark in Multi-robot Cooperation](https://mlsanity.com/paper/2511.21510/) (2025)
- [When LLMs Imagine People: A Human-Centered Persona Brainstorm Audit for Bias and Fairness in Creative Applications](https://mlsanity.com/paper/2602.00044/) (2026)
- [CodeClinic: Evaluating Automation of Coding Skills for Clinical Reasoning Agents](https://mlsanity.com/paper/2605.09675/) (2026)
- [Fine-Tuning MedGemma for Clinical Captioning to Enhance Multimodal RAG over Malaysia CPGs](https://mlsanity.com/paper/2510.15418/) (2025)
- [NeuroVLM-Bench: Evaluation of Vision-Enabled Large Language Models for Clinical Reasoning in Neurological Disorders](https://mlsanity.com/paper/2603.24846/) (2026)
- [When Background Matters: Breaking Medical Vision Language Models by Transferable Attack](https://mlsanity.com/paper/2604.17318/) (2026)
- [RePrompT: Recurrent Prompt Tuning for Integrating Structured EHR Encoders with Large Language Models](https://mlsanity.com/paper/2604.17725/) (2026)
- [From Synthesis to Clinical Assistance: A Strategy-Aware Agent Framework for Autism Intervention based on Real Clinical Dataset](https://mlsanity.com/paper/2605.02916/) (2026)
- [Is ChatGPT-5 Ready for Mammogram VQA?](https://mlsanity.com/paper/2508.11628/) (2025)
- [On Integrating Large Language Models and Scenario-Based Programming for Improving Software Reliability](https://mlsanity.com/paper/2509.09194/) (2025)

## Abstract (arXiv)

Recent advances in large language models (LLMs) have enabled general-purpose systems to perform increasingly complex domain-specific reasoning without extensive fine-tuning. In the medical domain, decision-making often requires integrating heterogeneous information sources, including patient narratives, structured data, and medical images. This study positions GPT-5 as a generalist multimodal reasoner for medical decision support and systematically evaluates its zero-shot chain-of-thought reasoning performance on both text-based question answering and visual question answering tasks under a unified protocol. We benchmark GPT-5, GPT-5-mini, GPT-5-nano, and GPT-4o-2024-11-20 against standardized splits of MedQA, MedXpertQA (text and multimodal), MMLU medical subsets, USMLE self-assessment exams, and VQA-RAD. Results show that GPT-5 consistently outperforms all baselines, achieving state-of-the-art accuracy across all QA benchmarks and delivering substantial gains in multimodal reasoning. On MedXpertQA MM, GPT-5 improves reasoning and understanding scores by +29.26% and +26.18% over GPT-4o, respectively, and surpasses pre-licensed human experts by +24.23% in reasoning and +29.40% in understanding. In contrast, GPT-4o remains below human expert performance in most dimensions. A representative case study demonstrates GPT-5's ability to integrate visual and textual cues into a coherent diagnostic reasoning chain, recommending appropriate high-stakes interventions. Our results show that, on these controlled multimodal reasoning benchmarks, GPT-5 moves from human-comparable to above human-expert performance. This improvement may substantially inform the design of future clinical decision-support systems.