How to Maintain Physician Autonomy With AI Integration

This study could change how every frontline clinic in the world delivers care. Penda Health and OpenAI revealed that an AI tool called AI Consult, embedded into real clinical workflows in Kenya, reduced diagnostic errors by 16% and treatment errors by 13%—across nearly 40,000 live patient visits. This is what it looks like when AI becomes a real partner in care. The clinical error rate went down and clinician confidence went up. 🤨 But this isn’t just about numbers. It’s a rare glimpse into something more profound: what happens when technology meets clinicians where they are—and earns their trust. 🦺 Clinicians described AI Consult not as a replacement, but as a safety net. It didn’t demand attention constantly. It didn’t override judgment. It whispered—quietly highlighting when something was off, offering feedback, improving outcomes. And over time, clinicians adapted. They made fewer mistakes even before AI intervened. 🚦 The tool was designed not just to be intelligent, but to be invisible when appropriate, and loud only when necessary. A red-yellow-green interface kept autonomy in the hands of the clinician, while surfacing insights only when care quality or safety was at risk. 📈 Perhaps most strikingly, the tool seemed to be teaching, not just flagging. As clinicians engaged, they internalized better practices. The "red alert" rate dropped by 10%—not because the AI got quieter, but because the humans got better. 🗣️ This study invites us to reconsider how we define “care transformation.” It's not just about algorithms being smarter than us. It's about designing systems that are humble enough to support us, and wise enough to know when to speak. 🤫 The future of medicine might not be dramatic robot takeovers or AI doctors. It might be this: thousands of quiet, careful nudges. A collective step away from the status quo, toward fewer errors, more reflection, and ultimately, more trust in both our tools and ourselves. #AIinHealthcare #PrimaryCare #CareTransformation #ClinicalDecisionSupport #HealthTech #LLM #DigitalHealth #PendaHealth #OpenAI #PatientSafety

Superhuman AI agents will undoubtedly transform healthcare, creating entirely new workflows and models of care delivery. In our latest paper from  Google DeepMind Google Research Google for Health, "Towards physician-centered oversight of conversational diagnostic AI," we explore how to build this future responsibly. Our approach was motivated by two key ideas in AI safety: 1. AI architecture constraints for safety: Inspired by concepts like 'Constitutional AI,' we believe systems must be built with non-negotiable rules and contracts (disclaimers aren’t enough). We implemented this using a multi-agent design where a dedicated ‘guardrail agent’ enforces strict constraints on our AMIE AI diagnostic dialogue agent, ensuring it cannot provide unvetted medical advice and enabling appropriate human physician oversight. 2. AI system design for trust and collaboration: For optimal human-AI collaboration, it's not enough for an AI's final output to be correct or superhuman; its entire process must be transparent, traceable and trustworthy. We implemented this by designing the AI system to generate structured SOAP notes and predictive insights like diagnoses and onward care plans within a ‘Clinician Cockpit’ interface optimized for human-AI interaction. In a comprehensive, randomized OSCE study with validated patient actors, these principles and design show great promise: 1. 📈 Doctors time saved for what truly matters: Our study points to a future of greater efficiency, giving valuable time back to doctor. The AI system first handled comprehensive history taking with the patient. Then, after the conversation, it synthesized that information to generate a highly accurate draft SOAP note with diagnosis - 81.7% top-1 diagnostic accuracy 🎯 and > 15% absolute improvements over human clinicians - for the doctor’s review. This high-quality draft meant the doctor oversight step took around 40% less time ⏱️ than a full consultation performed by a PCP in a comparable prior study. 2. 🧑⚕️🤝 A framework built on trust: The focus on alignment resulted in a system preferred by everyone. The architecture guardrails proved highly reliable with the composite system deferring medical advice >90% of the time. Overseeing physicians reported a better experience with the AI ✅ compared to the human control groups, and (actor) patients strongly preferred interacting with AMIE ⭐, citing its empathy and thoroughness. While this study is an early step, we hope its findings help advance the conversation on building AI that is not only superhuman in capabilities but also deeply aligned with the values of the practice of medicine. Paper - https://lnkd.in/gTZNwGRx Huge congrats to David Stutz Elahe Vedadi David Barrett Natalie Harris Ellery Wulczyn Alan Karthikesalingam MD PhD Adam Rodman Roma Ruparel, MPH Shashir Reddy Mike Schäkermann Ryutaro Tanno Nenad Tomašev S. Sara Mahdavi Kavita Kulkarni Dylan Slack for driving this with all our amazing co-authors.

The way physicians document clinical care is about to shift dramatically. Traditionally, we write notes, with the very act of writing serving as a critical step to promote thinking. But as AI increasingly prepares draft notes, physicians are transitioning from being the primary writers to becoming editors of clinical documentation. This is a significant change and for this change to be successful doctors will need to develop new skills and organizations will need to develop new tools to promote and measure the quality of the AI-clinician collaboration. Think of our new world this way: AI is like the staff writer at a newspaper, and clinicians are stepping into the role of editor, shaping, refining, and critically assessing the information presented. Are physicians and other clinicians ready to embrace this editorial role? How can we best support them in shifting their critical thinking approach to fit this new workflow? At upcoming conferences in May (AMIA (American Medical Informatics Association) CIC and Epic XGM25), our team will be addressing these concerns. Here’s our structured approach: 1. Develop clear and specific best-practice guidelines for editing AI-generated content. As an analogy, consider how editing roles differ between magazines, newspapers, and comic strips. Similarly, editing guidelines should be tailored specifically to distinct genAI workflows and contexts. 2. Empower clinical staff by clearly outlining the limitations of AI models and highlighting the complementary expertise and critical insights clinicians contribute. 3. Track and analyze automated process metrics at scale to assess editing frequency. Key metrics include the percentage of AI-generated notes edited and the degree of semantic change made by physician editors. 4. Implement structured processes for ongoing quality review to ensure continuous improvement of AI-generated documentation and physician editing. 5. Integrate decision support strategies directly within clinical documentation platforms to facilitate and encourage effective physician editing practices. We’d love to hear your thoughts. How do you envision the role of physicians evolving alongside AI? Share your comments and insights below! Image Credit: OpenAI 4.o image generator.

Can an # AI #Doctor partner with clinicians? Can we please move past the AI versus doctor/clinician comparisons in taking board exams.. solving diagnostically challenging cases... providing more empathetic on-line responses to patients...? and instead focus on improving patient care and their outcomes? The authors, Hashim Hayat, Adam Oskowitz et. al. at the University of California, San Francisco, of a recent study may be hinting at this: envisioning an agentic model (Doctronic) “used in sequence with a clinician” to expand access while letting doctors focus on high‑touch, high‑complexity care and supporting the notion that AI’s “main utility is augmenting throughput” rather than replacing clinicians (https://lnkd.in/e-y3CnuF)  In their study: ▪️ >100 cooperating LLM agents handled history evaluation, differential diagnosis, and plan development autonomously. ▪️ Performance was assessed with predefined LLM‑judge prompts plus human review. ▪️ Primary diagnosis matched clinicians in 81 % of cases and ≥1 of the top‑4 matched in 95 %—with no fabricated diagnoses or treatments. ▪️AI and clinicians produced clinically compatible care plans in 99.2 % of cases (496 / 500).  ▪️In discordant outputs, expert reviewers judged the AI superior 36 % of the time vs. 9 % for clinicians (remainder equivalent). Some key #healthcare AI concepts to consider: 🟢 Cognitive back‑up, in this study, the model identified overlooked guideline details (seen in the 36 % of discordant cases; the model used guidelines and clinicians missed). 🟢 Clinicians sense nuances that AI cannot perceive (like body‑language, social determinants). 🟢 Workflow relief , Automating history‑taking and structured documentation, which this study demonstrates is feasible, returns precious time to bedside interactions. 🟢 Safety net through complementary error profiles – Humans misdiagnose for different reasons than #LLMs; so using both enables cross‑checks that neither party could execute alone and may have a synergistic effect. Future research would benefit from designing trials that directly quantify team performance (clinician/team alone vs. clinician/team + AI) rather than head‑to‑head contests, aligning study structure with the real clinical objective—better outcomes through collaboration. Ryan McAdams, MD Scott J. Campbell MD, MPH George Ferzli, MD, MBOE, EMBA Brynne Sullivan Ameena Husain, DO Alvaro Moreira Kristyn Beam Spencer Dorn Hansa Bhargava MD Michael Posencheg Bimal Desai MD, MBI, FAAP, FAMIA Jeffrey Glasheen, MD Thoughts? #UsingWhatWeHaveBetter

Key strategies for making AI work in healthcare: 💡 Think of AI as a brilliant analyst, not the boss. Use AI's insights to enhance technical solutions - but always filter through clinical expertise. 💡 Context is king. When deploying AI for clinical workflows, success comes from understanding provider workflows, not just efficiency metrics. 💡 Build a culture of healthy skepticism. Teams should challenge AI recommendations. The best innovations emerge from this dialogue. 💡 Keep the human element central. Technology should enhance, not replace, empathy in healthcare delivery. 💡 Use AI strategically. Leverage it for predictive analytics and workflow optimization, while keeping critical patient care decisions in human hands. #HealthcareLeadership #AIinHealthcare #DigitalTransformation #HealthTech #FutureofHealthcare #WisdomAtWork #healthcareonlinkedin

AI in Medicine: 4 Ways Doctors and AI Can Work Together >> 📰A recent NYT op-ed by Eric Topol, MD and Pranav Rajpurkar explored how AI is outperforming doctors in diagnostics. Here’s my summary take on the four emerging models of AI-physician interaction, including one they missed 🔘A.I. is outperforming doctors in diagnostics, but studies show when used as an assistant, physician accuracy barely improves due to skepticism and overconfidence 🔘Having doctors oversee A.I. is like asking a student to grade their tutor’s work. It assumes that, in cases where A.I. outperforms, the weaker performer is always the best judge of accuracy 🔘The solution? A clear division of labor instead of A.I. acting as a subordinate co-pilot physician The article identified three emerging models 1️⃣ Doctor-first – Physicians gather patient history and exams; A.I. analyzes patterns to suggest diagnoses 2️⃣ A.I.-first – A.I. processes medical data, offering diagnoses and treatment plans; doctors refine based on patient-specific factors 3️⃣ A.I. autonomy – A.I. handles routine cases (e.g., normal X-rays, low-risk mammograms), freeing doctors for complex conditions The Op-ed favours #3 based on current data. In Sweden AI boosted breast cancer detection by 20% while halving radiologist workload, and Danish researchers found A.I. could reliably clear half of normal chest X-rays 💬The op-ed fails to consider a Fourth Model which I firmly believe will become the dominant model 4️⃣ True partnership in complex cases, with a clear division of labor where A.I. has autonomy in specific tasks rather than just assisting Example: In cancer treatment, A.I. could autonomously handle genomic analysis, treatment response predictions, and side effect modeling. Doctors would then interpret these insights, factoring in clinical judgment, patient needs, and ethics to finalize the treatment plan 💬 Their second model does comes close but still treats A.I. as an assistant rather than an independent expert in specific aspects of complex cases. The missing piece is a model where in complex cases A.I. and doctors divide tasks with clear autonomy, A.I. handling pattern-based insights, doctors focusing on human-driven decisions ⚠️The challenge for all of these models: Liability, regulation, and training doctors to know when to trust A.I. vs. their own judgment ✅ If applied well, A.I. could reduce bottlenecks, cut wait times, and improve outcomes, making medicine more efficient and, paradoxically, more human 👇Link to op-ed article in comments, it’s definitely worth a read #digitalhealth #ai

Jennifer Cooper et al. (2025) explored primary care providers’ perspectives on AI-assisted decision-making for patients with multiple long-term conditions (MLTC) through in-depth interviews. The interview highlighted essential insights for trust, safety, and human-centered digital transformation. Key Takeaways - Providers grappled with balancing medical needs, psychosocial factors, polypharmacy, and guideline gaps. This points to the nuanced challenge of MLTC care. - HCPs saw potential for AI to enhance safety and decision quality, but voiced concerns that over-reliance could erode therapeutic relationships. - Their top “must-haves” included transparent, explainable AI recommendations; seamless EHR integration; time efficiency; and preservation of clinician and patient autonomy. Dipu’s Take This study reflects critical lessons we emphasize in clinical education and quality improvement: - MLTC care isn’t linear. AI must support multifaceted decision layers, not simplify them. - Explainability, system integration, and time-saving are essential to clinician buy-in. - Empathy remains non-negotiable. Technology should augment, not replace, the clinician’s human connection. - Clinicians need training to appraise AI outputs, challenge algorithmic suggestions wisely, and retain decision autonomy. Let's discuss: How is your organization or program incorporating clinician-centered design into AI tools for complex care? What training and safeguards are you putting in place? https://lnkd.in/eTE9pwJ8