AI-Driven Decision Support Systems for Doctors

Imagine AI models working together like a team of doctors, each contributing their expertise to solve complex medical cases. This is what "MDAgents: An Adaptive Collaboration of LLMs for Medical Decision-Making" explores, as recently presented at NeurIPS 2024. Working: MDAgents brings a novel approach to using Large Language Models (LLMs) in medicine by dynamically creating a collaborative environment tailored to the complexity of each medical query: 1) Complexity check: Each medical question is evaluated for complexity, determining whether it necessitates a basic, moderate, or advanced collaborative response. 2) Expert recruitment: Based on complexity, MDAgents "recruits" AI agents to act as specialists, forming either a solo practitioner model, a Multi-disciplinary Team (MDT), or an Integrated Care Team (ICT). 3) Analysis and synthesis: The agents engage in collaborative reasoning, using techniques like Chain-of-Thought (CoT) prompting to draw insights and resolve disagreements for more nuanced cases. 4) Decision-making: Synthesizing diverse inputs, the framework reaches a final decision, informed by external medical knowledge and structured discussions among the AI agents. Achievements: 1) MDAgents outperformed both solo and group LLM setups in 7 out of 10 medical tasks, enhancing decision accuracy by up to 11.8%. 2) Demonstrated the critical balance between performance and computational efficiency by adapting the number of participating agents based on task demands. Link to the full paper -> https://lnkd.in/gR7Zwm7t #AI #Healthcare #NeurIPS2024 #MedicalAI #Collaboration #InnovationInMedicine #ResearchInsights

A must-read study in JAMA Network Open just compared a traditional diagnostic decision support system (DDSS), DXplain, with two large language models, ChatGPT-4 (LLM1) and Gemini 1.5 (LLM2), using 36 unpublished complex clinical cases. Key Findings: - When lab data was excluded, DDSS outperformed both LLMs: 56% vs. 42% (LLM1) and 39% (LLM2) in listing the correct diagnosis. - When lab data was included, performance improved for all: DDSS (72%), LLM1 (64%), LLM2 (58%). - Importantly, each system captured diagnoses that the others missed, indicating potential synergy between expert systems and LLMs. While DDSS still leads, the exponential improvement in #LLMs cannot be ignored. The study presents a compelling case for hybrid approaches—combining deterministic rule-based systems with the linguistic and contextual fluency of LLMs, while also incorporating structured data with standardized coding, such as LOINC codes and SNOMED International..etc The inclusion of structured data significantly enhanced diagnostic accuracy across the board. This validates the notion that structured and unstructured data must collaborate, not compete, to deliver better #CDS outcomes. #HealthcareonLinkedin #Datascience #ClinicalInformatics #HealthIT #AI #GenAI #ClinicalDecisionSupport

Can an # AI #Doctor partner with clinicians? Can we please move past the AI versus doctor/clinician comparisons in taking board exams.. solving diagnostically challenging cases... providing more empathetic on-line responses to patients...? and instead focus on improving patient care and their outcomes? The authors, Hashim Hayat, Adam Oskowitz et. al. at the University of California, San Francisco, of a recent study may be hinting at this: envisioning an agentic model (Doctronic) “used in sequence with a clinician” to expand access while letting doctors focus on high‑touch, high‑complexity care and supporting the notion that AI’s “main utility is augmenting throughput” rather than replacing clinicians (https://lnkd.in/e-y3CnuF)  In their study: ▪️ >100 cooperating LLM agents handled history evaluation, differential diagnosis, and plan development autonomously. ▪️ Performance was assessed with predefined LLM‑judge prompts plus human review. ▪️ Primary diagnosis matched clinicians in 81 % of cases and ≥1 of the top‑4 matched in 95 %—with no fabricated diagnoses or treatments. ▪️AI and clinicians produced clinically compatible care plans in 99.2 % of cases (496 / 500).  ▪️In discordant outputs, expert reviewers judged the AI superior 36 % of the time vs. 9 % for clinicians (remainder equivalent). Some key #healthcare AI concepts to consider: 🟢 Cognitive back‑up, in this study, the model identified overlooked guideline details (seen in the 36 % of discordant cases; the model used guidelines and clinicians missed). 🟢 Clinicians sense nuances that AI cannot perceive (like body‑language, social determinants). 🟢 Workflow relief , Automating history‑taking and structured documentation, which this study demonstrates is feasible, returns precious time to bedside interactions. 🟢 Safety net through complementary error profiles – Humans misdiagnose for different reasons than #LLMs; so using both enables cross‑checks that neither party could execute alone and may have a synergistic effect. Future research would benefit from designing trials that directly quantify team performance (clinician/team alone vs. clinician/team + AI) rather than head‑to‑head contests, aligning study structure with the real clinical objective—better outcomes through collaboration. Ryan McAdams, MD Scott J. Campbell MD, MPH George Ferzli, MD, MBOE, EMBA Brynne Sullivan Ameena Husain, DO Alvaro Moreira Kristyn Beam Spencer Dorn Hansa Bhargava MD Michael Posencheg Bimal Desai MD, MBI, FAAP, FAMIA Jeffrey Glasheen, MD Thoughts? #UsingWhatWeHaveBetter

🚀 AI Agent-Powered Multi-Medical Diagnostics 🔍 Redefining Diagnosis Through Multi-Modal Intelligence and Adaptive Clinical Reasoning 🧠 As the complexity of patient presentations increases—with comorbidities, fragmented data, and diagnostic uncertainty—traditional clinical models are being pushed to their limits. ✨ AI agents: autonomous, modular, learning-capable systems that serve as real-time Clinical Decision Support Systems (CDSS). These agents don’t replace clinicians—they augment their reasoning by integrating imaging, labs, genomics, wearables, and clinical notes into a unified, intelligent diagnostic interface. 📊 From multi-modal data fusion to multi-diagnostic reasoning, AI agents tackle diagnostic silos and provide contextualized insights at the point of care. Whether it’s parsing symptoms of cardiac distress across CT, ECG, and labs—or simulating comorbidity scenarios like sepsis vs. hepatic encephalopathy—they help clinicians think deeper, faster, and more accurately. 🧬 Powered by a modular architecture (perception, cognition, memory, world models, and emotion-aware interaction), these agents represent a leap in diagnostic intelligence—yet always under human oversight. 🔮 While widespread clinical deployment is still on the horizon, the vision is compelling: 🔗 Unified diagnostic assistants in radiology and oncology 🩺 Ambient monitoring for cardiopulmonary deterioration 🧑⚕️ AI copilots in primary care visits 🌍 Equitable diagnostics through decentralized AI agent networks ⚖️ But with innovation comes responsibility: explainability, trust, regulation (FDA SaMD), and ethical design are key to success. 💡 The future of diagnostics isn’t just automated. It’s collaborative, context-aware, and AI agent-augmented—designed to elevate human clinical judgment, not replace it. #AIinHealthcare #Diagnostics #ClinicalDecisionSupport #MultiModalA #HealthTech #MedicalAI #DigitalHealth #AIagents