Hansa Bhargava MD’s Post

The FDA is expressing some interesting thoughts on AI and its applications. I'm sure further guidance will evolve as AI continues to be implemented in healthcare. "Patients have called for more information about when AI is used in their care and how the FDA makes decisions about AI-enabled devices. Most authorized devices must have a submission summary available to the public, and the FDA said developers must include sufficient detail in these documents." https://lnkd.in/g9U7EyQq

Artificial intelligence is reshaping ePrescribing, the digital process of generating and transmitting prescriptions, with major implications for patients, providers, and pharmacies. AI-driven solutions are enhancing medication adherence, reducing administrative burdens, and improving prescription safety. “We need more innovation on ePrescribing technology,” experts argue, highlighting AI’s role in optimizing workflows and ensuring patients get their medications faster. - AI-powered tools will give patients instant access to price comparisons, helping them choose the most affordable medication option. - Providers will spend less time on prior authorizations as AI automates approvals and flags potential coverage issues. - Pharmacies will see fewer errors, as AI enhances medication reconciliation and catches prescription mismatches before they reach patients. With AI integration, ePrescribing could finally eliminate many of the delays and frustrations that have plagued the system. #AI #healthcare https://lnkd.in/eCtRuH-h

🚀 The future of #ClinicalTrials will be powered by #AI.   Clinical trials are essential for medical advancements but often face significant hurdles like regulatory constraints, patient access and infrastructure issues.   But what if AI could transform this challenging landscape? Dr. Shyam Sunder Rao Chepur, Engineering & R&D Services at HCLTech, explores how AI can improve the speed and reliability of clinical trials by bringing precision and efficiency to every step.   Read more: https://lnkd.in/g9tChDME

AI systems used in healthcare can sometimes provide incorrect information, biased predictions, or fail unexpectedly, posing risks to patients and clinicians. In an article by MIT and Boston University professors, it is suggested that AI systems in healthcare should have responsible-use labels similar to FDA labels on medications. These labels would convey critical information such as the model's intended usage, data sources, potential biases, and limitations. Implementing these labels would require developers to disclose important details about the model's training data and optimization processes. Regulating and enforcing these labels would involve validation before deployment, potentially by health agencies under the Department of Health and Human Services. This transparency is crucial to ensure the safe and informed use of AI systems in healthcare settings.

AI systems are increasingly used in healthcare, but they can hallucinate, make biased predictions, or fail unexpectedly, posing risks to patients and clinicians. In a Nature Computational Science article, Massachusetts Institute of Technology's Marzyeh Ghassemi and Boston University's Elaine Nsoesie argue for responsible-use labels, similar to FDA labels on medications. These labels would provide critical information such as model purpose, data origin, and potential biases, helping ensure safe deployment in medical settings. The implementation of such labels would require careful regulation and validation to avoid harm. What do you think of this? #AIinHealthcare #ResponsibleAI #PatientSafety #AIRegulation #BiasInAI #HealthTech #MedicalAI #AITransparency https://lnkd.in/eeDX4XG6

AI Revolutionising Medical Diagnostic and Detection The next big thing is here. The FDA has been approving a remarkable number of AI and machine learning-enabled devices. AI-enabled medical technology is paving the way for better health outcomes, cost savings for hospitals, and attracting investors. With the ability to efficiently analyze complex medical imaging data, AI is transforming diagnostics and detection, particularly in CT scans, PET scans, and ultrasounds. As medical diagnostic tools become more advanced and generate vast amounts of data, AI becomes crucial in analyzing and interpreting this information, ultimately improving patient outcomes. We can expect regulators to issue more approvals for novel technologies tied to AI, leading to widespread adoption of AI-enabled medical devices. Explore the fascinating world of AI in healthcare and how it's becoming a rich source of innovation and disruption in the industry. Let's embrace this transformative technology together 👉: https://ow.ly/8iIG50RfTBz #TwoMinuteTech #MedicalAI #MedTech #AI

With the number of FDA-approved AI-enabled devices rapidly increasing, it's crucial to ensure these tools are safe and effective. The article from MIT highlights the upcoming regulatory conference at the Jameel Clinic in March 2025, but warns that regulating clinical risk scores may prove especially challenging. Should we be worried? What can be done to ensure responsible innovation in AI-powered healthcare? #AIHealth #HealthcareRegulation #MIT #ResponsibleAI #lifescience #biotechnology #biotech #medicaldevice #meddev #pharmaceutical #pharma #healthcare #healthtech #technology https://lnkd.in/gtzw8jqj

🚀𝗠𝗲𝗱𝗧𝗲𝗰𝗵 𝗡𝗲𝘄𝘀🚀 The FDA has released final guidance on predetermined change control plans for AI and Machine Learning in medical devices, aiming to foster innovation while ensuring patient safety. Key highlights include: ✨ Proactive Change Management: Manufacturers can outline potential algorithm modifications during the premarket submission process. ✨ Streamlined Regulatory Pathway: This guidance accelerates updates to devices as technology evolves. ✨ Clear Rationale Required: Companies must provide a solid rationale for changes and demonstrate how they will maintain device performance and safety. This guidance is a significant step toward integrating advanced technologies in healthcare while upholding regulatory standards.🌟 Read more here: https://lnkd.in/eBePNA5G #MedTech #AI #RegulatoryAffairs #Innovation #HealthcareTechnology

🚨 The FDA has released crucial draft guidance for AI/ML-enabled medical devices. https://lnkd.in/eTffq7Ve 📊 Key takeaways on data requirements for development and validation: 👉🏻 Quality, diversity, and quantity of data are paramount for BOTH training and validation data. 👉🏻 Unbiased, representative training data is essential to ensure generalisability to the intended use population. 👉🏻 Validation data must sufficiently and properly represent the intended target population. 👉🏻 Sponsors need to explain variations in data management approaches and characteristics of the training data and validation data. 👉🏻 Fit-for-purpose real-world data (RWD) can be used for validation with rigorous assessment of the relevance and reliability. 💡 The ultimate keyword? - “Representativeness” 🎯 For AI developers and MedTech startup founders, this means you will need to be proactive to generate evidence on meeting these requirements. Want to learn more about how to generate evidence on meeting these requirements? Let's connect! #FDA #Regulatory #AI #MedTech

🚨Important FDA Update on AI in Medical Devices! The FDA has issued draft guidance aimed at ensuring the safety and effectiveness of AI-enabled medical devices throughout their entire product life cycle. This is the first comprehensive guidance of its kind, providing clear recommendations on design, development, maintenance, and documentation to help developers create safer and more effective AI technologies. Key Highlights: ✅ Covers the entire AI lifecycle from development to ongoing performance monitoring. ✅ Emphasizes risk management, transparency, and bias reduction strategies. ✅ Complements the FDA’s recent guidance on predetermined change control plans for AI device updates. ✅ Encourages early and frequent collaboration with the FDA to streamline the approval process. This is a major step forward in balancing innovation with patient safety in the rapidly growing AI health tech space. For developers, this provides a clear framework to follow, which can lead to faster approvals and better patient outcomes. The FDA is seeking public comment until April 7, 2025 and will host a webinar on February 18, 2025 to discuss this further. #medicaldevices #medicalimaging #ai