Nam Bui

Registration is opened for THE EARLY DETECTION OF CANCER CONFERENCE sponsored by CRUK, Stanford's Canary Center, and OHSU's Knight Cancer Institute. I am delight to serve as a chair along side with Dr. Utkan Demirci at Stanford and Dr. Sam Janes at University of College London. KEYNOTE SPEAKERS: * Peter Sasieni, King’s College London - Revolutionising cancer screening * Steve Quake, Stanford University - New approaches to early detection of Cancer * Azra Raza, Columbia University - The First Cell: Route for early detection and prevention SCIENTIFIC SECTIONS: * How is biology informing early detection? * Emerging technologies for cancer early detection * Insights from Early Detection Trials * Artificial Intelligence – promises vs. reality for early detection PANEL DISCUSSION: * Global challenges in cancer early detection regarding the differences in biological and environmental factors and healthcare system * Investing into the future: From Lab to Clinic * The future of evaluation of cancer screening technologies NETWORKING: From Lab to Clinic Networking & GREAT DEBATE

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Intellectually invigorated after attending Stanford University School of Medicine's first symposium on the role of AI in bio-medicine, convened by Dean Lloyd Minor and Fei-Fei Li (Co-director, Stanford Human-Centered AI Institute). Learned from leaders at Google, Amazon, Microsoft, American Medical Association, National Academy of Medicine, The National Institutes of Health, NHS, and eminent data scientists and ethicists at Stanford University. My Five KEY take-aways on successfully integrating AI systems into Healthcare. 1. TRANSPARENCY - Clinicians should know WHEN AI integrates into the systems they use. Failure to do so can lead to unintended consequences and patient harm. Example: Boeing made a change in the software in their new automated system, the Maneuvering Characteristics Augmentation System (MCAS), introduced with the 737 Max model. Pilots were either unaware of this change or not adequately trained on it, leading to fatal crashes and loss of life. 2. FUNCTIONALITY - Does it work? How well does it do what it is supposed to do? Post-use validation is paramount to refining systems. Academic and scientific rigor is important during building systems. However, it is unfair to ask our AI systems to be perfect prior to clinical implementation. Instead, the driving question should be - Does this particular AI system improve upon our current standard of care? 3. EXPLAINABILITY - Clinicians should know HOW AI integrates into their systems, and should be involved early on in the designing of these AI modules. End-user clinicians should know the LIMITATIONS and BIASES inherent to various AI modules, so they can deploy them knowledgably. Example: The dermatologist is aware that an AI trained on skin lesions of Caucasian patients may not have hi-fidelity when diagnosing lesions of dark-skinned individuals. 4. SCALABILITY - Health AI module development requires a lot of capital investment, often in the tens of millions of dollars. Most health AI modules are developed based on single institution specific data, and may not be applicable generally. Pooling credible data from multiple institutions/hospitals and using software to scale it for general use will bring down costs and increase adoption. 5. LEGAL/REGULATORY - Who is responsible when an adverse event happens as a result of implementation of an Health AI module- clinician, company, regulatory body? What regulations need to be put in place to deter nefarious actors from taking advantage of AI health modules? BONUS: ENVIRONMENTAL FRIENDLY? - AI systems are extremely water and energy intensive. How do we balance the benefits of Health AI with the adverse environmental impact the deployment of these models may have? AI in Healthcare AI in Healthcare Initiative (AIHI) Stanford Neurosurgery AI in Healthcare

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#transcript Fadi Micaelian, CEO of Sparkdit, and Dr. Andrew Fang, Chief of Orthopedics at Kaiser South San Francisco, have joined together to help patients and doctors make better treatment decisions by considering trade-offs and patient preferences. The platform can help reduce bias and incorporate patient desires into care while considering comorbidities and other factors that may affect treatment decisions. Fadi and Andrew agree that collaboration between technologists and clinicians is essential for advancing the use of AI in healthcare to augment human intelligence and improve outcomes. #MedAI #AI #DigitalHealth #MedTech #Orthopedics #PatientDecisions #DecisionIntelligence sparkdit.com https://lnkd.in/geBPctUH

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This #LLM Cancer Mentor App by Belong.Life is a revolutionary tool for cancer patients, caregivers, and physicians. It’s like having a WAZE for navigating the complex cancer diagnostic and care journey. My review on Medika Life.

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🫀Check out our most recent meta-analysis publication on Chronic Limb-Threatening Ischemia in Infrapopliteal Peripheral Arterial Disease in Cardiology in Review!

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