# Benchmarking the Thinking Mode of Multimodal Large Language Models in Clinical Tasks

arXiv: https://arxiv.org/abs/2511.03328 · HTML: https://mlsanity.com/paper/2511.03328/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 19 of its 38 references that are in the map.
- First posted on arXiv 5 Nov 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)
- Multimodal LLMs / vision-language models / [Medical vision-language models](https://mlsanity.com/mllm/medical-vision-language-models/)

## Builds on (most cited first, 19 of 19)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Stop Overthinking: A Survey on Efficient Reasoning for Large Language Models](https://mlsanity.com/paper/2503.16419/) (2025)
- [Seed1.5-VL Technical Report](https://mlsanity.com/paper/2505.07062/) (2025)
- [HuatuoGPT-Vision, Towards Injecting Medical Visual Knowledge into Multimodal LLMs at Scale](https://mlsanity.com/paper/2406.19280/) (2024)
- [Reasoning Models Can Be Effective Without Thinking](https://mlsanity.com/paper/2504.09858/) (2025)
- [AdaptThink: Reasoning Models Can Learn When to Think](https://mlsanity.com/paper/2505.13417/) (2025)
- [XrayGPT: Chest Radiographs Summarization using Medical Vision-Language Models](https://mlsanity.com/paper/2306.07971/) (2023)
- [AutoRG-Brain: Grounded Report Generation for Brain MRI](https://mlsanity.com/paper/2407.16684/) (2024)
- [From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond](https://mlsanity.com/paper/2411.03590/) (2024)
- [SkinGPT-4: An Interactive Dermatology Diagnostic System with Visual Large Language Model](https://mlsanity.com/paper/2304.10691/) (2023)
- [SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation](https://mlsanity.com/paper/2505.20622/) (2025)
- [Decoder-Hybrid-Decoder Architecture for Efficient Reasoning with Long Generation](https://mlsanity.com/paper/2507.06607/) (2025)
- [DAPE V2: Process Attention Score as Feature Map for Length Extrapolation](https://mlsanity.com/paper/2410.04798/) (2024)
- [Emerging Cyber Attack Risks of Medical AI Agents](https://mlsanity.com/paper/2504.03759/) (2025)
- [SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model](https://mlsanity.com/paper/2507.02822/) (2025)
- [Understanding the Mixture-of-Experts with Nadaraya-Watson Kernel](https://mlsanity.com/paper/2509.25913/) (2025)
- [SAS: Simulated Attention Score](https://mlsanity.com/paper/2507.07694/) (2025)
- [Can Reasoning LLMs Enhance Clinical Document Classification?](https://mlsanity.com/paper/2504.08040/) (2025)
- [Diagnosing Shoulder Disorders Using Multimodal Large Language Models and Consumer-Grade Cameras](https://mlsanity.com/paper/2510.09230/) (2025)

## Built on by (most cited first, 2 of 2)

- [Unveiling the Entropy Dynamics of Chain-of-Thought Reasoning](https://mlsanity.com/paper/2606.02020/) (2026)
- [Evaluating Multi-Turn Multimodal Diagnostic Reasoning on Challenging Real-World Clinical Cases](https://mlsanity.com/paper/2607.25933/) (2026)

## Abstract (arXiv)

A recent advancement in Multimodal Large Language Models (MLLMs) research is the emergence of "reasoning MLLMs" that offer explicit control over their internal thinking processes (normally referred as the "thinking mode") alongside the standard "non-thinking mode". This capability allows these models to engage in a step-by-step process of internal deliberation before generating a final response. With the rapid transition to and adoption of these "dual-state" MLLMs, this work rigorously evaluated how the enhanced reasoning processes of these MLLMs impact model performance and reliability in clinical tasks. This paper evaluates the active "thinking mode" capabilities of two leading MLLMs, Seed1.5-VL and Gemini-2.5-Flash, for medical applications. We assessed their performance on four visual medical tasks using VQA-RAD and ROCOv2 datasets. Our findings reveal that the improvement from activating the thinking mode remains marginal compared to the standard non-thinking mode for the majority of the tasks. Their performance on complex medical tasks such as open-ended VQA and medical image interpretation remains suboptimal, highlighting the need for domain-specific medical data and more advanced methods for medical knowledge integration.