AI in Clinical Decision-Making Processes

Did you see the recent news??? Microsoft recently unveiled its latest AI Diagnostic Orchestrator (MAI DxO), reporting an impressive 85.5% accuracy on 304 particularly complex cases from the New England Journal of Medicine, compared to just ~20% for physicians under controlled conditions . These results—quadrupling the diagnostic accuracy of human clinicians and more cost-effective than standard pathways — have gotten a lot of buzz. They may mark a significant milestone in clinical decision support and raise both enthusiasm but also caution. Some perspective as we continue to determine the role of AI in healthcare. 1. Validation Is Essential Promising results in controlled settings are just the beginning. We urge Microsoft and others to pursue transparent, peer reviewed clinical studies, including real-world trials comparing AI-assisted workflows against standard clinician performance—ideally published in clinical journals. 2. Recognize the value of Patient–Physician Relations Even the most advanced AI cannot replicate the human touch—listening, interpreting, and guiding patients through uncertainty. Physicians must retain control, using AI as a tool, not a crutch. 3. Acknowledge Potential Bias AI is only as strong as its training data. We must ensure representation across demographics and guard against replicating systemic biases. Transparency in model design and evaluation standards is non-negotiable. 4. Regulatory & Liability Frameworks As AI enters clinical care, we need clear pathways from FDA approval to liability guidelines. The AMA is actively engaging with regulators, insurers, and health systems to craft policies that ensure safety, data integrity, and professional accountability. 5. Prioritize Clinician Wellness Tools that reduce diagnostic uncertainty and documentation burden can strengthen clinician well-being. But meaningful adoption requires integration with workflow, training, and ongoing support. We need to look at this from a holistic perspective. We need to promote an environment where physicians, patients, and AI systems collaborate, Let’s convene cross sector partnerships across industry, academia, and government to champion AI that empowers clinicians, enhances patient care, and protects public health. Let’s embrace innovation—not as a replacement for human care, but as its greatest ally. #healthcare #ai #innovation #physicians https://lnkd.in/ew-j7yNS

Every second counts in a stroke. When blood flow to the brain is blocked or a vessel ruptures, millions of neurons are lost each minute. The difference between full recovery and lifelong disability often comes down to speed, accuracy, and access to the right treatment. Symptoms can appear suddenly: facial droop, arm weakness, slurred speech, loss of balance, or vision changes. These are moments of crisis where rapid recognition and immediate medical attention save lives. Despite global awareness campaigns, many patients arrive too late for the most effective interventions like clot busting drugs or thrombectomy. This is where artificial intelligence can make a profound difference. 1. Early Detection Algorithms trained on millions of CT and MRI scans can detect subtle changes in brain tissue faster than the human eye. This can alert clinicians immediately, even in hospitals without a full-time neuroradiologist. 2. Triage and Workflow Optimization AI systems can prioritize cases, send automatic alerts, and ensure that stroke teams are activated the moment a scan is uploaded. This reduces the “door-to-needle” time and helps align every step of care. 3. Predictive Analytics By analyzing patient history, vital signs, and lab results, AI can identify those at highest risk before a stroke occurs. This opens the door to prevention strategies and early interventions. 4. Telemedicine Integration AI-powered stroke networks can extend expert care to rural and underserved regions. A patient in a small town can receive the same level of diagnostic precision as one in a major academic hospital. 5. Rehabilitation Support After a stroke, recovery is a marathon. AI-driven rehabilitation tools, including virtual reality and motion tracking, can personalize therapy and track progress, improving outcomes over time. The goal is clear: no patient should suffer preventable disability because the system was too slow to act. With AI as a partner, the chain of survival and recovery can become stronger, faster, and more human-centered. Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. Image ref : Mayo Clinic #Stroke #HealthcareInnovation #AI #DigitalHealth #Neurology #StrokeAwareness #HealthTech #AIinMedicine #EmergencyMedicine #PreventiveHealth #BrainHealth #StrokeRecovery #Telemedicine #ClinicalAI #MedicalImaging #FutureOfHealthcare #PatientCare #HealthcareEquity #InnovationInHealth #StrokeSurvivor

The time to design AI-native architectures isn’t after operational gaps appear. It’s now. Healthcare doesn’t need more AI pilots. It needs systems that can reason, coordinate, and decide — together, in real time. On that line of thought, sharing this recent peer-reviewed commentary by Dr. Andrew Borkowski that outlines how multiagent AI systems are reshaping the frontier of clinical intelligence. These systems go far beyond today’s static tools and LLM wrappers. They orchestrate collaboration — across agents, workflows, and decision points. The commentary shares an example of sepsis management, where seven AI agents work in parallel to: • Clean and integrate unstructured data • Interpret imaging and vitals via deep learning • Stratify risk with Sequential Organ Failure Assessment (SOFA) and qSOFA scores • Generate treatment plans using reinforcement learning • Optimize hospital logistics with queue theory and genetic algorithms • Detect anomalies in real time via streaming forecasts • Auto-document every step into structured EHR records Every decision is governed by explainable AI, a quality-control agent, and confidence-calibrated outputs. Federated learning enables continuous evolution, while blockchain and OAuth 2.0 protect system integrity. This isn’t a distant vision. It’s a working blueprint for health systems under pressure to scale intelligence, not just automation. 📌 Read the commentary here → https://lnkd.in/g5X5PADk #AIsystems