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Mayo Clinic researchers have created an algorithm to identify certain heart pump problems. The #AI algorithm can also be used to detect some heart diseases, including hypertrophic cardiomyopathy and cardiac amyloidosisthey. Researchers  have also created a new class of #AI called "hypothesis-driven AI" that may help to improve interpretability of AI algorithms for health care treatments, particularly for cancer. #health #wellbeing #artificialintelligence #innovation #technology #healthtech https://lnkd.in/ewhr_uJY

I was speaking to Holyrood Communications recently (alongside Clarisse de Vries) about the impact that AI was having in healthcare. They asked me if it was fair to say that AI is a revolution in healthcare. My answer might surprise some. No, I said. Not yet. And probably not for some time. To revolutionise healthcare you'd need system-wide adoption of AI and we're struggling to adopt AI for even the simplest use cases. I'm not sure how useful it is to keep talking in nebulous terms about AI's revolutionary powers. We must be aspirational, but there's a big difference between aspiration and hype. Surely it's time to talk about #RealisticHealthcareAI In my mind, it's far more useful to talk about, and focus upon, what I'd call 'mini revolutions' - the introduction of AI as part of systemic change within individual disease pathways. Were we to make a concerted and coordinated national effort, I think it's possible to talk about a near-term #miniAIrevolution in areas like breast cancer, lung cancer, etc. Any area where we can identify productive AI in multiple parts of a patient's journey (from risk stratification all the way through treatment and recovery), is a candidate for a mini revolution. Equally, any area that impacts a broad range of healthcare services, is ripe for a mini revolution. Things like self-service, referral management, staff rostering, administration of patient flow, etc. Although, to be fair, there's not much AI on the market in these areas. What do you think? Is it time to pursue #RealisticHealthcareAI and start some of those #miniAIrevolutions. Jan Beger, Leanne Summers, Gerald Lip, David Lowe, Dr. Derrick Khor, Dr Amrita Kumar, Dr Hugh Harvey, Mark Parsons, West of Scotland Innovation Hub. Dominic Cushnan #AI #AIForGood #HealthcareAI #Healthcare https://lnkd.in/ez96n4gT

Lots of people are asking me how Midi Health is using AI. Yesterday at an event at Stanford, I was introduced to Open Evidence - and told that I could save money by having our providers use it for advice. The founders say they developed OpenEvidence to cut through all the noise and misinformation that is the modern internet and build tools that are unbiased, widely accessible, international, up to date to the day, accurate, and free. I bet it often gets things right. But not always. And I am afraid it will be that way for a long time. I asked Open Evidence whether women with BRCA could use hormone replacement therapy. At Midi Health we know that estrogen is safe for women with BRCA and I was curious about the answer. We also know that If you haven't had your uterus removed, your provider will typically prescribe estrogen along with progesterone or progestin (progesterone-like medication). This is because estrogen alone, when not balanced by progesterone, can stimulate growth of the lining of the uterus, increasing the risk of endometrial cancer. Unfortunately, in this case, the feedback from Open Evidence is just completely incorrect. As far as I can tell - the advice is completely made up - a true hallucination. When we think about how to use AI in healthcare - we are using it to make our providers more efficient and take away easy tasks - but definitely do not rely on it for medical care. You should not either. Zachary Ziegler and Travis Zack, MD, PhD it would be great to get this right!

Think AI will magically cure cancer? Not so fast. This article dives into the hype versus reality in healthcare. It exposes how AI can actually deepen inequalities, leaving vulnerable patients in the lurch. From flawed automated systems to biased medical practices, there's a lot to unpack.

Embark on a journey into healthcare innovation: Explore how Generative AI revolutionizes early disease detection, offering proactive interventions and improved patient outcomes. Full coverage here: https://lnkd.in/gvxRS3Jw Delve into cutting-edge Generative AI solutions in healthcare innovation with Ambilio. Together, we shape a healthier tomorrow through transformative technology and personalized patient care, pioneering the future of medicine. #generatieveai #healthcare

Could AI be the key to saving lives? Researchers at the University of Leeds believe so, as they’ve developed an AI tool named Optimise that could revolutionize how GPs identify patients at high risk for heart-related conditions. Analyzing health records of over two million individuals, Optimise found more than 400,000 high-risk patients, accounting for a staggering 74% of heart-related deaths. Even more impressive, in a pilot study, the AI discovered undiagnosed conditions in one out of five high-risk patients, prompting timely interventions that could prevent serious complications like heart failure, stroke, and diabetes. Dr. Ramesh Nadarajah, a health data research fellow, emphasized the power of AI in providing crucial insights from readily available data, potentially allowing healthcare professionals to offer more timely and effective care. This proactive approach not only aims to save lives but also to alleviate the growing pressures on the NHS. Looking ahead, the team plans to conduct a larger clinical trial to further validate these promising findings. Backing the study, Prof. Bryan Williams, chief scientific and medical officer at the British Heart Foundation, highlighted the potential of early diagnosis in reducing hospital admissions and the significant role AI could play in tackling heart and circulatory diseases, which account for a quarter of all deaths in the UK. This collaboration between advanced technology and medical expertise offers hope for a future where heart disease can be detected and treated before it’s too late. Summary produced with help from ChatGPT. https://lnkd.in/d_PApwEj

The Role of Artificial Intelligence in Health Diagnostics Artificial intelligence \(AI\) has rapidly evolved from a futuristic concept into a crucial tool in modern healthcare. The integration of AI into health diagnostics is transforming the way medical professionals detect, diagnose, and treat diseases. By analyzing vast amounts of data with... [...] #AI #Diagnostics Read more... https://lnkd.in/dTeENazm

"In tests, the AI model achieved impressive accuracy, recording 95.79% accuracy for the Iowa dataset and 99.83% accuracy for the San Diego dataset in classifying Parkinson’s disease patients versus healthy controls." If predictive AI only unlocks an additional 10% accuracy and reduces backlog for analyzing scans by 20% by deploying algorithmic analysis, it would mean another 20,000 tests annually without mistakes (positive or negative) and nearly 40,000 more scans that could be handled annually without new backlog. Now imagine that across every major company or industry over the next 24 months. Story: https://lnkd.in/diNwEzCW _____ #Healthcare #PredictiveAI P.S. If you're looking for a home for quickly launching Predictive AI across your org, find a day/time that works well for you and we'll demo and discuss capabilities: https://lnkd.in/e5kNHCqG

Improving Patient Outcomes. The primary goal of leveraging AI in JIA diagnosis is to improve patient outcomes. Click for more https://bsapp.ai/Ti1XBrBKQ #lifestyle #JuvenileIdiopathicArthritis #JIA #diagnostic

𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗲𝘀 𝗣𝗮𝗿𝗸𝗶𝗻𝘀𝗼𝗻’𝘀 𝗗𝗶𝘀𝗲𝗮𝘀𝗲 𝗦𝘆𝗺𝗽𝘁𝗼𝗺 𝗧𝗿𝗮𝗰𝗸𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀𝗶𝗼𝗻 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻 Exciting advancements are on the horizon for #Parkinsons disease management, thanks to the power of #MachineLearning. A recent article from News Medical explores how this technology is transforming symptom tracking and progression prediction for Parkinson’s patients. 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀: 📊𝙄𝙢𝙥𝙧𝙤𝙫𝙚𝙙 𝙎𝙮𝙢𝙥𝙩𝙤𝙢 𝙏𝙧𝙖𝙘𝙠𝙞𝙣𝙜: Machine learning algorithms are enhancing the accuracy and efficiency of monitoring Parkinson’s symptoms, providing more detailed and actionable data. 📊𝙋𝙧𝙚𝙙𝙞𝙘𝙩𝙞𝙫𝙚 𝘼𝙣𝙖𝙡𝙮𝙩𝙞𝙘𝙨: The technology is offering predictive insights into disease progression, helping clinicians anticipate changes and tailor treatment plans more effectively. 📊𝙀𝙣𝙝𝙖𝙣𝙘𝙚𝙙 𝙋𝙖𝙩𝙞𝙚𝙣𝙩 𝘾𝙖𝙧𝙚: By leveraging data-driven approaches, machine learning supports more personalized care and better management strategies for patients. 💡 #EducateYourself: As we witness the integration of machine learning into #healthcare, it's clear that these innovations are paving the way for more precise and effective management of #chronicconditions like #ParkinsonsDisease. These advancements signify a major leap forward in managing Parkinson’s disease, highlighting the potential of AI to improve outcomes and patient #qualityoflife. Staying informed about such breakthroughs is essential for understanding the future of #patientcare. #MachineLearning #HealthcareInnovation #AI #DiseaseManagement #NewsMedical https://lnkd.in/gkGfyfCC