Jan Beger的动态

Mechanisms / trends in motion - pointing to - like clockwork rollouts. The mechanisms and trends we’re witnessing are rolling out with clockwork precision. We’re at the early stages of evaluating AI’s delivery of early warnings in the healthcare sector, and even now, the performance is astounding. Just imagine the profound impact once we see universal adoption and implementation. This will spur ideation and innovation at an unprecedented scale, driven by brilliant minds. Each incrementally improved idea will not only achieve tangible results but will also inspire others, creating waves of innovation lifting the globe. It’s like clockwork—the clarity and certainty of what’s to come is undeniable. Brilliance is proliferating everywhere, creating a virtuous cycle of ever-increasing innovation. The future is bright and the possibilities, boundless. __________________________________________________________________________________ Pre-AI Rewrite: Mechanisms / trends in motion - pointing to - like clockwork rollouts. Here - see evaluation of deliveries of early warnings with AI in healthcare context - at this early phase and the performance. My gosh - can one imagine the clear blow outs we will see once we start to see universal incorporation adoption and implementation - spurring ideation and innovation at scale with all the brilliant minds out there - further stimulated and spurred by the scale and scope their incrementally improving ideas can concretely deliver and experiencing the expansiveness and contagious seeing others in waves and iterations lift the globe. Clockwork - insofar as the clarity and certainty of expectation goes - brilliance all over the place churning out ever increasing propagating and + feedback looping brilliance underlying it.

WATCH: ClinicalKey AI describes the association between Lisch nodules and NF, including incidence and other possible etiologies. The answer to this question was generated on March 31, 2024. Unlock the power of AI in clinical decision making with ClinicalKey AI. ClinicalKey AI combines trusted, evidence-based clinical content with conversational search powered by generative AI to support clinicians in delivering high-quality patient care. More information can be found at https://spkl.io/60434F0nv Medical disclaimer: Elsevier does not provide medical advice. Elsevier and the content available on its properties (https://spkl.io/60444F0na, YouTube, and other channels) do not provide a diagnosis or other recommendation for treatment and are not a substitute for the professional judgment of a healthcare professional in diagnosis and treatment of any person or animal. The determination of the need for medical services and the types of healthcare to be provided to a patient are decisions that should be made only by a physician or other licensed healthcare provider. Always seek the advice of a physician or other qualified healthcare professional with any questions you have regarding a medical condition.

Kaiser Permanente physicians and staff have tested using ambient AI scribes to help alleviate some of the burden of clinical documentation, increasingly cited as a contributor to clinician burnout. Physicians have published their findings on the initial trial, with great observations on quality, effectiveness, and clinician-patient engagement. This commentary also highlights key components of the implementation process - an essential consideration for any AI tool or model. https://bit.ly/48m99ZQ.

Excited to announce that I will be talking about Bias and Fairness of AI in Clinical Trials next month. Clinical trials are essential to improve patient care. If no clinical trials, how will we know if a treatment is safe or effective? And how will we be able to know if a new way of diagnosing is better than the old way? While clinical trials can be cumbersome and time-consuming, AI can improve parts. Here are a couple of ways: 1. Patient Recruitment and Screening - Find eligible patients based on trial criteria and patient data 2. Data Collection and Analysis - Automate data management and analysis 3. Protocol Design and Optimization - Effective and efficient protocol designing 4. Safety Monitoring - Monitor safety signals and adverse events real-time 5. Predictive Modeling - Identify patient populations to benefit treatments But, what can be risks when using AI in clinical research? Sign up to the webinar to learn more about AI in Clinical Research. Link in comment.

Does SYSTEM 1-based decision-making lead to #diagnostic #errors? Around 5-15% of patients are misdiagnosed. Despite significant advances in #diagnostics, #genetics, and #medical #devices, the way medical decisions are made hasn't changed for decades. And this is a bottleneck of #medicine. ?? ?????? ???????????????? ???????? ???? ?????????? ???? ???????????????? According to Gaurav Singal and Anupam B. Jena, “diagnosis has largely remained a human endeavor, with doctors relying on so-called illness scripts—clusters of signs, symptoms, and diagnostic findings that are hallmarks of a disease.” Eric Topol, MD claims that “with the brief duration of a clinic visit, it is not surprising that there is little time to reflect because it relies on System 1 thinking, which is automatic, near-instantaneous, reflexive, and intuitive.” ?? ?????? ???? ?????????? The human brain has cognitive limitations; thus, having access to more #data does not automatically mean better decisions. Doctors are also susceptible to many biases – including confirmation bias, overconfidence bias, anchoring bias, and availability heuristics – because they rely on intuition instead of facts. This makes it easy to overlook or ignore information crucial to a correct diagnosis, no matter how complete and accessible the data in the EHR is. ?? ???? ???????? ???????? ???? ???????????? ?? ?????? ???????????? ?????? ???????????? ?? However, #AI can bridge the gap between the available data and its actual use in the diagnosis. AI doesn't get tired, can analyze gigabytes of information in a fraction of a second, and is less susceptible to factors leading to medical errors. It can pick up hidden patterns in data, pixels of change on medical images, and small but important records in medical files from several years ago that the human eye cannot detect. ?? ????-???????????????? ?????????????????????? ?????? ???????? ???? ?????? ???????????????? ???????? ???? ?????? ?????????? ?????????????? ?????????????? ???????????? ?We are certainly not there yet. But in the years ahead, as we fulfill the aspiration and potential for building more capable and medically dedicated AI models, it will become increasingly likely that AI will play an invaluable role in providing second opinions,” concludes Topol. Gaurav Singal and Anupam B. Jena agree: "This could be a special moment for diagnosis if we invest enough and do it right.” Read my recent article, which is now available on ICT&Health Int. #ICTandHealth

Volume 1, No. 3 is now available! Here are the latest articles available in the March issue of NEJM AI:?? ? Save this post to revisit later (click the ?? button at top right of post).? ? ?? Editorial:?Policy in Progress — The Race to Frame AI in Health Care https://nejm.ai/48wIYQ9? ? ?? Original Article:?Machine Learning to Infer a Health State Using Biomedical Signals — Detection of Hypoglycemia in People with Diabetes while Driving Real Cars https://nejm.ai/3Sn2UyT? ? ?? Perspective: To Do No Harm — and the Most Good — with AI in Health Care https://nejm.ai/3T9kb0q? ? ?? Perspective: How Academic Medical Centers Govern AI Prediction Tools in the Context of Uncertainty and Evolving Regulation https://nejm.ai/47Y0UCY? ? ?? Original Article: Evaluation of Sepsis Prediction Models before Onset of Treatment https://nejm.ai/3w8nwUr? ? ?? Original Article: External Validation of a Commercial Acute Kidney Injury Predictive Model https://nejm.ai/42z48Mn? ? ?? Original Article: Towards Generalist Biomedical AI https://nejm.ai/3IfDwGF? ? Visit https://ai.nejm.org to read all the latest articles on AI and machine learning in clinical medicine.?

Check out our article on #AI for detecting #hypoglycemia

Efficient trial design is critical to reducing costs and time in drug development. Studies reveal that traditional trials for evaluating 10 interventions increase costs by 57.5% and duration by 311.9%. TrialKey’s TrialGen tool automates protocol design using AI, addressing common inefficiencies such as poor endpoint definition and inadequate patient criteria. By leveraging real-world data, TrialGen ensures precise and efficient trial designs, reducing the need for costly amendments and enhancing overall trial success rates. Learn how our innovative platform can streamline your clinical trials. ?? Book a demo: https://appt.link/minhthu #PlatformTrials #CostEfficiency #TrialOptimization #AIInTrials #TrialGen #ClinicalTrials #ProtocolDesign #RealWorldData #EfficiencyBoost #TrialKey #PatientCriteria #EndpointPrecision #DrugDevelopment #ClinicalResearch #CostReduction

Traditional clinical trials for evaluating 10 interventions result in a 57.5% increase in costs and a 311.9% increase in duration compared to platform trials. Studies highlight that the complexity of trial protocols often leads to significant inefficiencies, including poorly defined endpoints and inadequate patient recruitment criteria. TrialKey’s TrialGen optimises trial designs using AI, reducing these costs and time by automating protocol design and leveraging real-world data. This includes defining precise patient criteria and selecting the best endpoints. By doing so, TrialGen ensures efficient resource allocation and enhances trial success rates. ?? Discover how TrialKey can help: https://appt.link/minhthu #PlatformTrials #CostEfficiency #TrialOptimization #AIInTrials #TrialGen #ClinicalTrials #ProtocolDesign #RealWorldData #EfficiencyBoost #TrialKey #PatientCriteria #EndpointPrecision #DrugDevelopment #ClinicalResearch #CostReduction

AI presents both unprecedented opportunities and potential risks for human health and well-being. A new commentary in National Academy of Medicine Perspectives presents initial discussions by healthcare sector leaders as they develop an AI Code of Conduct. Check it out: https://lnkd.in/evuhGBEe