Michael Meucci’s Post

The future of healthcare will be co-written by AI and the messaging around it will matter just as much as the models: https://lnkd.in/e-5FQ-EC Here’s what’s on my mind: - How will patients react to AI-enabled care? - Who controls the narrative around risk, bias, and equity? - How do we communicate AI's value without overhyping its capabilities? What’s your take on OpenAI’s move?

OpenAI is moving from powering others’ tools to building its own healthcare applications. With GPT-5’s strong performance on clinical benchmarks and the release of HealthBench, the company is positioning healthcare as a core pillar of its AI strategy. This marks a shift toward end-to-end solutions for clinicians and consumers, entering a space already shaped by Microsoft, Palantir, Abridge, and others. OpenAI’s direct entry signals the next stage of competition and innovation in healthcare AI. https://archive.ph/QIYjw

🩺 **OpenAI is making a bold move into healthcare AI.** With strategic hires from Doximity and Instagram, the company is building out a team dedicated to developing tools for both clinicians and consumers. The launch of HealthBench—a healthcare-focused AI evaluation standard—and GPT-5’s advanced health capabilities show how AI is moving beyond diagnosis to real-world support. This is about making healthcare more accessible, more transparent, and more patient-centric. Read the full story → https://hubs.li/Q03GDTMd0 #AI #HealthcareInnovation #HealthTech #OpenAI #GPT5 #DigitalHealth

https://lnkd.in/dWatdZcx From the article, "OpenAI Makes a Play for Healthcare": "When using AI, no matter how informed we may be about a topic, people tend to value the model’s recommendations over their own beliefs. This bias is made even more dangerous by the fact that AI is inherently a black box: we have no idea why or how it gets to the conclusions it does, making it harder to understand where the reasoning could have gone wrong and whether you should trust the model."

The healthcare AI race is accelerating. OpenAI has signaled its next big move into healthcare with two high-profile hires from Doximity and Instagram to drive product development and go-to-market strategy. With major players like Microsoft, Palantir, and now OpenAI competing for influence, the question becomes: how will this next wave of AI transform care delivery, patient engagement, and healthcare real estate needs? More details via Business Insider: https://bit.ly/4fXxASY

⚡ What if bigger AI models aren’t always the best for healthcare? ⚡ The Chinchilla Scaling Law introduces a fresh perspective on training large language models (LLMs). Instead of just making models larger, it says that the key lies in balancing model size with enough training data — basically, training smaller models on more data can lead to better performance. Why does this matter for healthcare AI? Healthcare data is complex, privacy-sensitive, and often limited. So, how do we get the most out of the compute and data we have? The Chinchilla law helps optimize training by showing how to achieve this balance efficiently. A recent comprehensive study, "Exploring Scaling Laws for EHR Foundation Models" by Sheng Zhang et al., presents the first empirical evidence that these scaling principles apply in healthcare too. Using real clinical data from MIMIC-IV, the research reveals predictable relationships between model size, data volume, and clinical performance — laying crucial groundwork for building efficient, powerful healthcare AI models. Read here- https://lnkd.in/geuAinZk What do you think? Could this approach shape the future of clinical AI models? How might balancing parameters and data impact healthcare AI deployment? Have you seen cases where a smaller model trained well outperformed a larger one? I’d love to hear your insights or experiences with AI scaling in healthcare. #HealthcareAI #ChinchillaScalingLaw #MachineLearning #AIethics #DigitalHealth #MedTech #LLMs #AIinHealthcare #MedicalAI #HealthTech #HealthEquity #AIforGood #ClinicalAI #HealthInnovation #ResponsibleAI Harvey Castro, MD, MBA. Andrew Ng Dr Avneesh Khare Dr. Geeta Nayyar, MD, MBA Halle Tecco, MPH, MBA Alex Dummett, MD, FACP, ABPM-CI, CPPS, CPHFH Benjamin Schwartz, MD, MBA Eric Topol, MD Aletha Maybank, MD, MPH

I have written about the importance for marketers of understanding what AI is looking at to generate results. Reddit, Inc. turns out to be more important than ever. Here is a bit of a cross-over into healthcare marketing as AI increasingly guides physician choice. Again, critical to know what the AI is really looking at. Thank you Nathan Eddy of Healthcare IT News for this thoughtful article. https://lnkd.in/e-FXXkkB

OpenAI is expanding its healthcare efforts by hiring Nate Gross, MD and Ashley Alexander. Nate Gross, cofounder of Doximity, will lead OpenAI's go-to-market strategy in healthcare, focusing on co-creating new technologies with clinicians and researchers. Ashley Alexander, formerly of Instagram, will be vice president of product, developing tech for consumers and clinicians. OpenAI has been involved in healthcare through partnerships and research, but now aims to create its own applications. CEO Sam Altman highlighted ChatGPT's healthcare capabilities during the launch of GPT-5. Read more: https://lnkd.in/eye-83Fj 📰 Subscribe to the Life AI Weekly Newsletter: https://lnkd.in/eC5-u69w #ai #artificialintelligence #ainews #healthcareai

Most clinicians and health leaders have not heard of Guardrailed-AMIE, yet it matters because accountability is the single biggest barrier to safe clinical use of conversational AI. Google’s multi agent approach reframes the problem from accuracy alone to who is responsible for what. 1) Multi agent designs mirror clinical teams: separating diagnosis, verification, and policy review can make AI outputs auditable and easier to integrate into clinician workflows. 2) Separation of duties helps, but it does not solve responsibility. Diffusion of responsibility is a real legal and ethical risk unless roles and signoff rules are explicit. 3) Evaluation must move beyond simulated vignettes to prospective, workflow based studies that measure impact on clinician decision making, not just model metrics. 4) For product teams, practical controls matter more than novelty: provenance of claims, mandatory human signoff, clear escalation paths, and real time audit trails. Takeaway: If you are building or procuring medical conversational AI, design for human accountability from day one—map agent responsibilities to licensed roles, log every decision, and validate in clinical workflows before deployment. #AgenticAI #LanguageModel #ArtificialIntelligence https://lnkd.in/g-FDZ_8a

Medical AI must be explainable—not a black box Opaque model logic can undermine practitioner trust and reduce adoption. Explainability is more than a nice-to-have—it’s foundational. Redwerk illustrates how clarity improves clinician confidence and outcomes. #ExplainableAI #AITransparency #ClinicalAI https://lnkd.in/gWrivv-j