AI in Oncology: A Clinical Polymath | Future of Cancer Care (Stanford)

My new video focuses on two new research preprints (see below) highlighting the transformative potential of advanced AI systems in addressing healthcare challenges at both macro (population-level) and micro (individual-level) scales, with a focus on oncology.

The first research paper demonstrates the power of Natural Language Processing (NLP) in identifying hidden patterns in patient communications using clustering techniques (e.g., BERT embeddings, UMAP, and BIRCH). This approach converts patient-reported concerns into actionable research priorities, as evidenced by the discovery of connections between chemotherapy and dental health concerns. By integrating expert validation and leveraging LLMs to refine these insights, the study emphasizes a feedback loop where patient voices directly shape medical research and healthcare priorities.

The second AI research paper explores how large language models (LLMs), specifically the o1 model, redefine clinical decision-making. Pure MedAI. By employing advanced reasoning frameworks like Chain-of-Thought (CoT) and Retrieval-Augmented Generation (RAG), the study demonstrates o1’s superiority in diagnostic accuracy, reasoning stability, and adaptability across real-world scenarios like ICU decision-making. The integration of such models into frameworks like the Chain of Diagnosis (CoD) and MedAgents enables dynamic and multi-step reasoning, improving patient care outcomes by aligning diagnostic workflows with real-time data and evolving medical knowledge.

Together, these research insights reveal the potential of AI systems in creating a symbiotic framework for healthcare, where population-level insights (NLP-driven patient data analysis) inform individual-level actions (dynamic clinical decision-making). This dual approach ensures patient-centric care by bridging gaps between research and practice, fostering both innovation in healthcare delivery and improved patient outcomes.

Thank you to all universities, from ‪@stanford‬ to ‪@MayoClinic‬ for publishing their latest research.

All rights w/ authors:
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Can Artificial Intelligence Generate Quality Research Topics Reflecting Patients’ Concerns?
by Stanford School of Medicine
https://arxiv.org/pdf/2411.14456

TOWARDS NEXT-GENERATION MEDICAL AGENT: HOW o1 IS
RESHAPING DECISION-MAKING IN MEDICAL SCENARIOS
by University of Georgia, University of Alberta, Massachusetts General Hospital and Harvard Medical School, Mayo Clinic, ....
https://arxiv.org/pdf/2411.14461

00:00 Multi Ai Agent Sys in clinical med research
02:02 2 new AI research papers on Oncology
03:09 My story of multi AI agents in a clinic
15:08 Technical part of 1st AI paper
19:52 2nd AI paper by Stanford School of Medicine


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