The recent advancements in the integration of Large Language Models (LLMs) across various specialized domains, particularly in healthcare, have significantly propelled the field forward. The focus has shifted towards developing adaptive, multi-agent systems that can handle complex tasks with greater efficiency and accuracy. These systems are not only enhancing clinical decision-making and medical curation but also revolutionizing disaster response and mathematical optimization. Notably, the use of LLMs in personalized medicine and oncology care is expanding, though challenges related to health equity and specialist-level performance remain. The field is also witnessing innovations in simulation engines for multi-agent systems, which promise to improve the scalability and performance of AI applications. Overall, the trend is towards more sophisticated, context-aware, and collaborative AI systems that can adapt to diverse and complex real-world scenarios.
Sophisticated AI Systems for Complex Real-World Applications
Sources
A Perspective for Adapting Generalist AI to Specialized Medical AI Applications and Their Challenges
CurateGPT: A flexible language-model assisted biocuration tool
A Demonstration of Adaptive Collaboration of Large Language Models for Medical Decision-Making
Integration of Large Vision Language Models for Efficient Post-disaster Damage Assessment and Reporting
Autoformulation of Mathematical Optimization Models Using LLMs
Can Personalized Medicine Coexist with Health Equity? Examining the Cost Barrier and Ethical Implications
[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI
SMoA: Improving Multi-agent Large Language Models with Sparse Mixture-of-Agents
The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare
Exploring Large Language Models for Specialist-level Oncology Care
AI Metropolis: Scaling Large Language Model-based Multi-Agent Simulation with Out-of-order Execution
From Medprompt to o1: Exploration of Run-Time Strategies for Medical Challenge Problems and Beyond