🤖 When and why we use single-agent vs multi-agent system? Our paper reveals this decision can be made based on the input complexity. MAS excel when agents can challenge and verify each other's reasoning in parallel, not just with simple vote. Paper:

🤷‍♂️ When and why do Foundation Models hallucinate or confabulate in healthcare, and what's the real-world impact on medical practice? 💔 Our work tackles this urgent question, defining "Medical Hallucinations" AND revealing experimental results. Paper:

What do you want to know?.

I will be at #NeurIPS2024 from December 10-16. Thrilled to present our oral paper(MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making) on Friday, December 13th (15:50-16:10 PST). 🔍 Learn more: .Project page:

RT @Orson_Xu: [Please RT📢] SEA Lab ( is hiring 1 postdoc in Spring/Fall'25 and 1-2 PhD in Fall'25!. We build next-g….

@chanwoopark20 @HyewonMandyJ I am open to any forms of collaboration for the future work in Healthcare AI domain especially on multi-agent LLM, healthcare AI and wearable sensors. Also, I am actively looking for PhD positions this Fall.

@chanwoopark20 @HyewonMandyJ Our ablation show that the adaptive setting outperforms static complexity settings, with 81.2% accuracy on text-only queries. Most text-only queries were high complexity, while image+text and video+text queries were often low complexity, suggesting visual cues simplify decisions.

@chanwoopark20 @HyewonMandyJ Our findings show that MDAgents consistently reach consensus across different data modalities. text+video modalities converge quickly, while text+image and text-only modalities show a more gradual alignment. Despite varying speeds, all modality cases eventually converged.

@chanwoopark20 @HyewonMandyJ Our ablations reveal that our approach can optimize performance with fewer agents (N=3), improves decision-making at extreme temperatures, and reduces computational costs, making it more efficient and adaptable than Solo and Group settings, especially in complex medical cases.

@chanwoopark20 @HyewonMandyJ Solo settings excel in simpler tasks, achieving up to 83.9% accuracy, while group settings outperform in complex, multi-modal tasks, with up to 91.9% accuracy.

@chanwoopark20 @HyewonMandyJ Surprisingly, our MDAgents significantly outperforms both Solo and Group setting methods, showing the best performance in 7 out of 10 benchmarks. This comprehends both textual information with high precision and visual data.

@chanwoopark20 @HyewonMandyJ MDAgents follows four stages: .1) Medical complexity check to categorize the query.2) Expert recruitment selecting PCC for low and MDT/ICT for moderate and high complexity.3) Initial assessment.4) Collaborative discussion between LLM agents .5) Final decision making by moderator

@chanwoopark20 @HyewonMandyJ Previous approaches in medical decision making have ranged from single- to multi- agent frameworks like voting and debates. However, they often stick to static setups. However, MDAgents dynamically choose the best collaboration structure based on the complexity of medical tasks.

Thrilled to announce our paper "MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making" has been accepted as an oral paper at #NeurIPS2024! 🎉. I had the pleasure of collaborating on this with @chanwoopark20 and @HyewonMandyJ. 📚 Project:

RT @CHILconference: A framework for LLMs to make inference about health based on contextual information and physiological data. Our fine-tu….

Excited to share a #ACL2024 Findings paper "EmpathicStories++: A Multimodal Dataset for Empathy towards Personal Experiences" co-authored with @jocelynjshen. We provide a valuable data for work in empathetic AI, quantitative exploration of cognitive insights and empathy modeling.

RT @taotu831: What unprecedented opportunities can 1M+ context open up in medicine?. Introducing 🩺Med-Gemini, a family of multimodal medica….

I'm excited to share my recent publication in CHIL 2024, "Health-LLM: Large Language Models for Health Prediction via Wearable Sensor Data". Our study reveals the potential of LLMs as personal health learners with wearable sensors. Arxiv:

Happy to share our latest paper "Adaptive Collaboration Strategy for LLMs in Medical Decision Making". We introduce MDAgents - a framework that constructs LLM team for medical decision making, showing best performance in 5 out 7 medical benchmarks.