AI in Healthcare Requires Governance and Clinical Understanding

AI in healthcare is no longer experimental AI is no longer an experiment in healthcare. It is a strategic imperative. The question before us is not whether it will shape the future of medicine—it already is. The true measure of success will be whether we harness it to build a healthcare system that works better for everyone. https://lnkd.in/dCBb5YSR

Clinicians aren't opposed to AI — they just don't want it running the show unsupervised. A new national survey from Carta Healthcare finds near-unanimous consensus on where AI delivers real value, and it's not full automation. The findings may reframe how your practice thinks about implementation. 🔗 https://hubs.li/Q04dfZBz0 #AIinHealthcare #PracticeManagement #HealthcareTechnology #ClinicalAI

One of the most underrated risks in clinical AI is surface-level evidence. As clinicians, we know that an abstract rarely tells the whole story. It may point us in the right direction, but it does not capture the nuance, limitations, methodology, or applicability that matter at the bedside. And abstracts do not practice medicine. Clinicians do. That is why, when I think about AI in clinical care, I keep coming back to the same principles that guided my work as a CMIO: reduce cognitive burden, make it easier to do the right thing, and never create technology that adds friction. Clinical AI needs to be fast enough for the pace of care, but it also needs to be verifiable. Answers should be traceable to reliable full-text sources. Clinicians should be able to understand where information came from, evaluate it in context, and apply their own judgment. ClinicalKey AI is designed with that balance in mind: speed, depth, and verification, without asking clinicians to take shortcuts on evidence. If your organization is evaluating clinical AI, I would make two things non-negotiable: Traceability — Can clinicians verify the source behind the answer? Oversight — Is the tool supporting clinical judgment rather than replacing it? The goal should not be AI for its own sake. The goal should be better, safer clinical decision support. #AIinHealthcare #ClinicalInformatics #ClinicalDecisionSupport #EvidenceBasedMedicine #ClinicalQuality

Executive Health AI Insights for the week of May 11th is out! Insights, action plans, and agentic AI implementation plans include: 1. Clinical AI’s competitive edge will come from proof-to-production discipline, not model selection alone 2. Frontline co-design is emerging as the highest-return AI implementation investment 3. The next AI advantage will go to systems that operationalize governance before regulation fully settles 4. Every new AI-enabled workflow is also a new continuity-of-care dependency https://lnkd.in/gDRBGUix #healthAIinsights #healthAI #healthIT #healthcare #artificialintelligence #agenticAI

“Models are not objective judges, they are opinions embedded in code.” One of the more thought-provoking moments from Calcalist כלכליסט's Mind the Tech New York 2026 conference came from Prof. Ran Balicer, speaking about one of the less discussed challenges in healthcare AI: alignment. Because in medicine, it’s not enough for AI to be powerful. It also needs to be safe, unbiased, and aligned with the values we want guiding care. This thinking led to the development of Optica, an AI governance system designed to evaluate whether AI tools are clinically safe, fair, and responsible before they reach real-world implementation. At the same time, the conversation also highlighted something bigger: Israel is no longer just talking about AI in healthcare, it is implementing it at scale. Every month, hundreds of thousands of AI-driven recommendations are already helping physicians close treatment gaps and deliver more proactive care. Read it fully in the first comment.

What if AI could bring us closer to more human care? AI isn’t replacing the doctor–patient relationship. It’s strengthening it. Research continues to show patients trust AI most when it supports, not replaces, their physician. When used well, AI empowers physicians with deeper insights, helps patients stay more engaged, and creates space for more meaningful connection. Care is evolving: • More informed, real-time decision-making • More engaged patients • More continuous, data-driven relationships And the foundation remains the same: Empathy. Trust. Communication. AI’s real promise is simple by helping us deliver more connected, personalized, and human care. Healthcare Businesswomen’s Association Hippocratic AI HIMSS Modern Healthcare Tina Medley Galloway Shaun Venable, MS Mona Baset #CharlotteHealthcare #AIinHealthcare #DigitalHealth #HealthcareInnovation #PatientExperience #GenerativeAI #HealthcareLeadership #FutureOfHealthcare https://lnkd.in/eHCdv_Y8

Clinical AI Governance: A Patient’s Perspective - Part 2 The Patient as End-User: Experiencing AI in Care Delivery From a governance perspective, patients are often described as “beneficiaries” of AI, but in reality, they are end-users of its consequences. Patients may not see the algorithm, but they experience its outputs: • A diagnosis supported (or challenged) by AI • A triage decision influenced by predictive tools • A care pathway shaped by data-driven recommendations From the patient’s perspective, the key questions are simple: • Is this safe? • Is this correct for me? • Can I trust it? Clinical AI governance must therefore ensure that: 👉 AI performs reliably across different patient populations 👉Outputs are clinically validated, not just technically accurate 👉Decisions remain explainable in a way patients can understand Because when AI fails, patients do not experience it as a system failure, they experience it as their care being wrong. #ClinicalAIGovernance #PatientSafety #AIinHealthcare #HealthcareQuality #DigitalHealth

Authors: Rikke Duus, Mike Cooray, Simon Lilley | Highlights: Public healthcare organizations face multiple challenges to effectively adopt and scale up AI-driven solutions at pace•AI designed to transform healthcare must demonstrate visible value to ensure its impact is understood and recognised•Generation of visible value serves as a mediating factor to legitimize and build confidence in continued AI activities•A framework for AI adoption, AI spheres and visible value generation in healthcare is developed | Open Access ====>>>

Clinical AI Governance: A Patient’s Perspective - Part 1 Clinical AI governance is often discussed from the perspective of clinicians, executives, or digital teams. But there is another perspective that is equally important: the patient. Patients are not passive recipients of AI, they are: • Directly affected by AI-informed decisions • Interacting with AI-enabled systems within healthcare organisations • Increasingly using consumer generative AI tools to inform their own care From a patient's perspective, clinical AI governance is not abstract, it is experienced through: 👉Trust 👉Transparency 👉Safety 👉Outcomes This series explores clinical AI governance through that lens, because ultimately, governance is not judged by how well it is designed but by how it is experienced. #ClinicalAIGovernance #PatientExperience #DigitalHealth #AIinHealthcare #PatientSafety

“Why ‘Accuracy’ Alone is a Dangerous Metric in Clinical AI.” After working closely with clinical systems, one thing becomes clear very quickly: a highly accurate model can still be practically useless. Accuracy lives in controlled environments. Hospitals do not. A model might show 95% accuracy in validation, but that number says nothing about when the output arrives, how it is presented, or whether a clinician can act on it without breaking their workflow. In real settings, timing and usability often matter as much as correctness. I’ve seen systems where the model was technically strong, but results came too late to influence decisions. By the time the output appeared, the clinician had already moved on. In that moment, accuracy had zero value. I’ve also seen the opposite. Slightly less accurate systems that fit seamlessly into existing workflows were used consistently and ended up delivering far more clinical impact. Not because they were smarter, but because they were usable at the right moment. Clinical environments are not just about prediction. They are about decisions under time pressure, with incomplete information, and high consequences. If your system does not align with that reality, accuracy becomes a misleading comfort metric. Another overlooked factor is interpretability in context. It’s not enough for a model to be correct. The output has to be understood quickly, trusted immediately, and verified without cognitive overload. If a clinician has to stop and think too long about what the system is saying, you’ve already lost the advantage. The real benchmark in clinical AI is not “How often is the model right?” It is “How often does the system change a decision when it matters?” That requires alignment with workflow, speed, clarity, and trust. Accuracy is only one piece of that equation, and often not the limiting one. In practice, the systems that succeed are not the ones with the best models. They are the ones that respect how decisions are actually made on the ground. #ClinicalAI #HealthcareAI #DigitalHealth #AIinHealthcare #ClinicalDecisionSupport #HealthTech #MedicalAI #AIethics #ExplainableAI #HealthcareInnovation #RealWorldEvidence #AIImplementation