Sean G.’s Post

#LLMs offer people with cancer a WAZE-like guide needed to navigate the complexities of care. "Dave AI" is the #AI co-pilot everyone with this diagnosis should access. My Medika Life interview with Eliran Malki, Belong.Life Co-Founder, CEO, and an inventor of this incredible 24/7 info resource, showing how it improves #patientengagement with #health professionals and offers rapid connection to #clinicaltrials. This full-length interview reinforces that #AI, #ChatGPT and #LLMs aren't nifty tech toys - it's an example of how smart tech can sustain and potentially save people's lives. https://lnkd.in/gpFhyew8

Eight in 10 (81.4 percent) supported patient consent for AI model use during treatment decisions. In a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, most respondents said they would present both options and let the patient decide (36.8 percent), with those from academic settings more likely than those from other settings to let the patient decide (odds ratio, 2.56). Three-quarters of respondents (76.5 percent) agreed that oncologists should protect patients from biased AI tools, but only 27.9 percent were confident in their ability to identify poorly representative AI models.

𝐇𝐚𝐯𝐞 𝐲𝐨𝐮 𝐫𝐞𝐚𝐝 𝐨𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐛𝐥𝐨𝐠 𝐨𝐧 𝐡𝐨𝐰 𝐀𝐈 𝐢𝐬 𝐦𝐚𝐤𝐢𝐧𝐠 𝐚 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞? AI is revolutionizing how we detect cancer early and tailor treatments like never before. 𝗥𝗲𝗮𝗱 𝗼𝘂𝗿 𝗯𝗹𝗼𝗴: 𝗵𝘁𝘁𝗽𝘀://𝘇𝗮𝘆𝘁𝗿𝗶𝗰𝘀.𝗰𝗼𝗺/𝗮𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹-𝗶𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲-𝗶𝗻-𝗰𝗮𝗻𝗰𝗲𝗿-𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻-𝗶𝗺𝗽𝗮𝗰𝘁𝘀-𝗼𝗻-𝗲𝗮𝗿𝗹𝘆-𝗱𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻-𝗮𝗻𝗱-𝗽𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘀𝗲𝗱-𝘁𝗿𝗲𝗮𝘁𝗺𝗲𝗻𝘁/ Did you know? AI algorithms can analyze medical images faster and more accurately than humans, potentially saving lives through early detection. Our blog dives deep into these advancements and their impact on patient care. What are your thoughts on AI in healthcare? Let's discuss! Drop a comment below or share your insights after reading the full blog. Together, let's explore how technology can transform medicine. #ArtificialIntelligence #EarlyDetection #PrecisionMedicine #ZaytricsInsights #AI #CancerDetection #PersonalizedTreatment #HealthTech #Innovation #AIinMedicine #CommunityDiscussion #zaytrics #tech #ai #ml #predictiveanalytics

In today’s leadership landscape, where access to information is increasingly open source, keeping our workforce informed is imperative. To ensure your team remains agile, proactive, and knowledgeable about developments in their field, encourage them to utilize generative open AI sources for the latest updates. The number of scientific clinical trials and research papers informing systems, laws, and healthcare advancements demands an immense amount of man-hours. The human brain alone cannot efficiently read, analyze, and extract valuable insights from these papers on a large scale. Here, artificial intelligence serves as a powerful tool, capable of analyzing vast numbers of scientific documents in an unprecedented manner. Artificial intelligence not only enhances our ability to process and understand large volumes of information but also ensures we remain at the forefront of innovation and knowledge in our respective fields. Below is a recent paper published by Harvard Medical school using AI to predict Future Pancreatic Cancer https://lnkd.in/dkGcuuvk Whats your thoughts 💭? Do you think Machines learning and deep learning in clinical research might be a field where AI could help!

Age is such a crude way to design screening programs. This study shows that using AI risk prediction algorithms to stratify screening cohorts may improve the cost-effectiveness of screening programs. Key quote: "Artificial intelligence–based risk-stratified programs were estimated to be cost-saving and increase quality-adjusted life-years compared with the current screening program. A screening schedule of every 6 years for lowest-risk individuals, biannually and triennially for those below and above average risk, respectively, and annually for those at highest risk was estimated to give yearly net monetary benefits within the NHS of approximately £60.4 (US $77.3) million and £85.3 (US $109.2) million."

In this week's "Thought Leader Series" post, learn more about how OpenAI is used to help doctors improve cancer patient outcomes through automating the analysis process resulting in improved health outcomes. Read the full article here: https://bit.ly/3VAhkNK #Healthcare #AI #Technology #DataAnalysis #OpenAI Reach out to an Trexin Consulting Advisor today and begin preparing for an AI future: https://bit.ly/3jgLNB7

Can’t wait to hear more about this research - feels like a new CPT code should be in the works- probably several and some new regulations or at least guidelines on AI for healthcare. #aiforgood #bcs #womenshealth #breastcancerresearch #fda #ama #ai #healthcareai

Happy Monday, LinkedIn Community! Back again with another Monday Machine Learning News with Markus, and today we've got something BIG! Harvard scientists just unveiled an AI model that detects cancer with a whopping 96% accuracy! But before we start celebrating too hard, let's dig a bit deeper: This got me thinking, what will the future of AI in medicine actually look like? Imagine getting diagnosed by an app – it identifies the problem, prescribes the treatment, and even schedules your doctor’s appointment if needed. But is it really that simple? Here’s the thing – accuracy, while cool to see, doesn’t always tell the full story. As many scientists know, accuracy alone is a bit of a “meh” metric, especially in medicine. You’ve gotta ask about sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) – that’s where the real truth lies. For instance, if only 4% of people have cancer, a 96% accuracy could be achieved by just classifying everyone as cancer-free. Amazing, you’d have 100% specificity and 96% NPV, but 0% sensitivity and undefined PPV... not quite what we are looking for! So, what do you all think? Are we headed toward a future where AI is your first point of contact in healthcare, or is this just another case of “wait and see” tech that won’t hit mainstream anytime soon? Here’s the article: https://lnkd.in/g7rMwtBY #AI #MachineLearning #HealthTech #CancerResearch #MedicalAI #DataScience #HealthcareAI

Grateful to Jules Adam and Labiotech for the chance to talk about such an important topic: using AI to advance Alzheimer’s research. Honored to have been interviewed and share how AI is starting to break new ground in the fight against this complex disease. With Alzheimer’s, the challenges are immense. Traditional approaches struggle to keep up with its multifaceted nature, but AI is helping us make real progress—enabling earlier detection, exploring new pathways for drug discovery, and even rethinking how we approach existing treatments. At SandboxAQ, we’re working with large quantitative models (LQMs), which simulate molecular interactions and give us insights as though we’re observing them in real-time. It’s this ability to visualize and understand disease mechanisms that gives me hope. Thank you again, Jules, for helping to highlight this important work. Here’s to a future where AI truly transforms Alzheimer’s care. #SandboxAQ #AI #AlzheimersResearch #LQMs #HealthcareInnovation

🌐 The latest AI Act highlights the importance of addressing biases in AI models. In many discussions, whether with clients or during panels, the term "bias" is often used loosely, without a clear understanding of what it truly entails, aside from representing a specific population. 🩺 To identify effective examples of bias, we can look at current practices in medicine. This is explored in Usha Lee McFarling latest article on STAT News, which is part of a series called “Embedded Bias.” The article discusses how Black patients with low T cell counts are automatically classified as being in poor health, even though this count is normal for that population. 🔍 This bias in interpretation stems from a narrow perspective where medical standards are based on a Caucasian model. Addressing such biases is crucial for AI providers as they strive to build models that are more accurate, reliable, and fair. In this way, AI has the potential to correct decades of bias in traditional medicine, benefiting both patients and healthcare providers. https://lnkd.in/duy2c_49 #bias #healthcare #AI