Color’s Post

Machine Learning Approach Enhancing Precision Medicine Treatments - DistilINFO Hospital IT Summary: #MachineLearning in #PrecisionMedicine is revolutionizing healthcare by personalizing treatments based on individual patient data. This blog post explores how machine learning algorithms are being used to enhance precision medicine treatments, leading to more effective and targeted care for patients. Key Points: 1. #PersonalizedMedicine: Machine learning algorithms analyze vast amounts of patient data to identify patterns and tailor treatments to individual patients, improving outcomes and reducing side effects. 2. #ClinicalDecisionSupport: Machine learning tools provide clinicians with real-time insights and recommendations for treatment options, helping them make more informed decisions and improve patient care. 3. #DrugDiscovery: Machine learning is accelerating the drug discovery process by predicting drug responses and identifying potential targets for new therapies, ai.mediformatica.com #medicine #learning #precisionmedicine #machinelearning #cancer #outcomes #patientoutcomes #healthcare #personalizedtreatment #personalizedtreatmentplans #acceibility #tools #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/3xoQ8JB)

In numerous scenarios, patients tragically lose their lives due to delays in diagnosis and the challenge of identifying the root cause of diseases. These delays can have grave consequences. It is crucial for healthcare systems to prioritize timely and accurate diagnoses to prevent such devastating outcomes. Indeed, artificial intelligence (AI) is revolutionizing the healthcare industry in several impactful ways like Accurate Diagnosis, quickly analyze large datasets, identifies patterns, and provides rapid insights. AI-enabled systems reduce operational costs by automating administrative and clinical workflows. As AI continues to evolve, its impact on healthcare will only grow, enhancing patient outcomes and transforming the way we provide and receive medical care. https://lnkd.in/dRbxMsYY

🔷In January 2023, Microsoft took an investment position in Paige (pathology tech company). 🔷Later, Microsoft invested in d-Matrix, a California-based AI Chip maker. 🔷In September, Paige announces (to no one's surprise) a collaboration with Microsoft to build a larger database to power AI in Anatomic Pathology dx. 🔷✅A classic in how to build scale around a healthcare AI application. ...and for all those AP's out their submitting patient microscopy and dx data--- neither you nor your patients will see a positive ROI...thank you very much...hugs and kisses MSN. https://lnkd.in/eZsuR_Vk

Microsoft and Providence have taken a multimodal approach to solving current challenges NHS oncologists are facing... "As if the medical puzzle pieces weren’t challenging enough to fit together, basic logistical information may also be missing from patient records. It may be surprising, but identifying a patient’s oncologist or other specialists that have been involved isn’t necessarily straightforward. This kind of information should be readily organized in the patient record, but in reality it’s often fragmented or absent,” Dr. Leidner says... "AI, however, can summarize this information quickly. Most importantly, it doesn’t require information to be formatted – it can vacuum up lab results, doctors’ notes and digitized scans as they are. It also can figure out that two different terms refer to the same thing, because it can work with natural language." #cancerservices #azureplatform #azureai #assistedcare

AI & other means to improve clinical trial efficacy

The exemplary configuration of the AI system in standalone mode achieved a sensitivity of 84·2% (95% CI 82·4–85·8) and a specificity of 89·5% (89·0–89·9) on internal-test data, and a sensitivity of 84·6% (83·3–85·9) and a specificity of 91·3% (91·1–91·5) on external-test data, but was less accurate than the average unaided radiologist. By contrast, the simulated decision-referral approach significantly improved upon radiologist sensitivity by 2·6 percentage points and specificity by 1·0 percentage points, corresponding to a triaging performance at 63·0% on the external dataset; the AUROC was 0·982 (95% CI 0·978–0·986) on the subset of studies assessed by AI, surpassing radiologist performance.

Chief Product and Strategy Officer Kathy Dalton Ford addresses the importance of building AI models that clinicians can trust. “We want that clinician to be able to determine their level of trust in our insights - We show the number of cohorts that matched this patient at this moment in time.” With Ronin, clinicians are empowered to analyze model outputs with an interpretable interface that delivers a breakdown of the data, presents potential biases, and highlights impactful clinical factors. Learn more about Ronin’s approach to designing for trust: https://hubs.la/Q02d5T8J0 #AIinHealthcare #SupportingClinicians #TrustingAI

ArteraAI's MMAI #biomarker test leverages a unique, clinically validated algorithm that assesses thousands of digital images from a patient’s biopsy and learns from a patient's clinical data - but assessing and interpreting gigapixel-sized images is no easy feat. By partnering with Union.ai, we're accomplishing things that conventional techniques and computers cannot do alone - allowing #ArteraAI to focus on the core of our mission - developing AI tests to personalize #cancertherapy. ➡️ Learn more about how we're making a genuine difference in clinical outcomes with the help of Union:

Super inspiring to see tech and healthcare joining forces to make a real difference in people’s lives. This short and interesting article highlights the benefits of AI to provide faster and personalized treatment for cancer patients. There is a lot of unstructured data in the healthcare system. Responsible AI can make a real difference and improve patients' treatment experience. Have you come across any innovative AI use cases in healthcare? If so, please share, I would love to learn more 🚀 😊 https://lnkd.in/eq3acUS9

Two differing use cases for AI in Healthcare. Clinical vs Non Clinical, all which play a part in some way. Salesforce's perspective, from Amit Khanna, SVP & GM for Health : “These new data, AI, and CRM features (from salesforce) help reduce the administrative and operational burden for healthcare providers and care teams, leading to better outcomes for their patients. And with Salesforce’s trusted AI, healthcare organisations excited about generative AI — but nervous about clinical and security concerns — can confidently use these innovations in their everyday workflows.”  https://lnkd.in/eB_kEYKG Insightful as I see this as a pragmatic step. Clinical Setting: An incredible use case of AI for Cancer Screening. Amazon Web Services have some interesting tools here. https://lnkd.in/eyfR7r7x