Predictive Med AI

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  • 🧠 “If You Build It, Will They Trust It?” — Why End-User Testing Is the Secret Sauce in Medical AI

    🧠 “If You Build It, Will They Trust It?” — Why End-User Testing Is the Secret Sauce in Medical AI

    In the world of AI-powered healthcare, the tech is only half the battle. The real question? Will doctors and patients actually trust—and use—what we build?

    As clinician-entrepreneurs push the boundaries of predictive medicine, it’s easy to get caught up in performance metrics and algorithmic breakthroughs. But a tool that impresses in testing may still flop in practice if it doesn’t fit the workflows, values, or emotional needs of its users.

    That’s where end-user testing comes in—not as an afterthought, but as a strategic imperative.

    A recent NHS England guide on AI deployment underscores this point, emphasizing that “AI tools should be co-developed with healthcare professionals and patients to ensure usability, safety and trust.” This isn’t just bureaucracy—it’s smart business. Involving users early leads to products that are not only more ethical but also more adoptable.

    In our own recent study, we interviewed kidney transplant patients and surgeons about AI-powered decision aids. The takeaway? Both groups were cautiously optimistic—but only if tools are transparent, explainable, and designed for real human concerns. Patients want to understand why an algorithm makes a recommendation. Clinicians want backup—not a black box.

    This aligns with findings from a broader 2023 study published in JAMIA Open, which showed that trust and transparency significantly affect clinicians’ willingness to adopt AI tools. Without thoughtful interface design, clear communication of risk, and iterative user testing, even the most powerful models may go unused.

    So, if you’re a clinician-founder building the next AI solution for healthcare, don’t just ask: “Does it work?” Ask: “Will they want to use it?”

    Because the future of AI in medicine doesn’t belong to whoever builds the smartest model. It belongs to whoever builds the most trusted one.

  • 🛠️ Smart Help or False Hope? Testing the Tools Guiding Medical Decisions

    🛠️ Smart Help or False Hope? Testing the Tools Guiding Medical Decisions

    Clinical Decision Support Systems (CDSS) are increasingly integrated into healthcare settings, promising to enhance clinical decisions, improve patient outcomes, and reduce errors. But how effective are these tools really? And are they delivering on the promise of AI-driven revolution, or merely creating false hope?

    What Are Clinical Decision Support Systems?

    CDSS are software tools designed to assist clinicians by providing knowledge, patient-specific information, or evidence-based recommendations at the point of care. They range from simple alerts and reminders to complex algorithms predicting patient outcomes. Despite their growing adoption, many CDSS still rely on traditional rule-based systems rather than advanced AI.

    The Real-World Impact of AI in Decision Support

    My recent research with the team at the University of Oxford involved conducting a systematic review study analyzing how AI-powered decision support systems perform in real clinical environments. The study highlights key challenges in model calibration and the importance of trust in AI outputs by healthcare professionals. You can read the full study here.

    Our findings show that while AI-enhanced CDSS can significantly aid diagnosis and treatment planning, the accuracy and reliability of these tools must be rigorously evaluated in real-world settings to avoid over-reliance or misinterpretation.

    Why Trust and Transparency Matter

    The success of CDSS depends heavily on clinician trust. Without clear explanations and transparent processes, even the most sophisticated AI tools risk being ignored or misused. Ethical frameworks such as those outlined by The Hastings Center provide guidance on ensuring responsible AI use in healthcare, emphasizing transparency, fairness, and accountability. You can explore their recommendations here.

    The Future of Decision Support in Medicine

    Looking ahead, institutions like the MIT Jameel Clinic are pioneering innovative AI models that integrate seamlessly with clinical workflows to provide personalized, actionable insights in real time. Their research is pushing the boundaries of AI-driven CDSS to be more adaptive, explainable, and trusted by clinicians. Discover their latest work here.

    Recent news also highlights increasing adoption of AI-powered predictive tools in hospitals worldwide, signaling a shift towards more data-driven, patient-centric care.