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Health Canada digital health head says AI/ML guidance imminent https://lnkd.in/gkrc4Up9 Health Canada is close to publishing new guidance on AI/machine learning (AI/ML) medical devices, which includes incorporating predetermined change control plans (PCCPs). Speaking at the 2024 Medtech Conference, Marc Lamoureux, manager of the Digital Health Division, highlighted three key challenges in the AI/ML space: algorithmic drift, transparency, and postmarket monitoring. He emphasized that AI/ML models need continuous updates to maintain performance, and transparency is critical for trust and adoption. Health Canada’s draft guidance, released in September 2023, takes a total product lifecycle approach, addressing issues like bias and the need for postmarket monitoring. Regulatory provisions will ensure higher-risk devices are closely monitored. Lamoureux also noted Health Canada is adopting PCCPs, following the lead of the US FDA and Japan’s regulatory agency. The final guidance will be published soon.

Here are some questions to get you thinking about A.I. in medicine: 1️⃣ Do you trust artificial intelligence, especially when it comes to treating human conditions? 2️⃣ What does transparency mean for AI in healthcare? How can we ensure a level of transparency that is reassuring to the general public? 3️⃣ How do we ensure machine learning models in medical devices are trustworthy and effective? At DIA Labs, we are committed to advancing healthcare innovation responsibly. Health Canada's recent publication on Transparency of Machine Learning-enabled Medical Devices outlines crucial guiding principles to ensure safety, effectiveness, and trust in AI-driven technologies. Key Principles: ✅ Transparency: Clear documentation of machine learning models and their decision-making processes. ✅ Accountability: Continuous monitoring and reporting of AI system performance. ✅ Trust: Building confidence through robust validation and regulatory compliance. As more and more devices look to harness the infinite capabilities of A.I., it is crucial to adhere to these principles, ensuring that all medical innovations are safe, reliable, and transparent. Read more about Health Canada's guiding principles: https://lnkd.in/e3UJ54CX #HealthcareInnovation #MachineLearning #MedicalDevices #Transparency #DIALabs #HealthCanada

This article from Physicians Weekly discusses the potential of AI in transforming the healthcare industry, emphasizing the need for effective regulation to unlock its benefits. Over 500 medical AI devices have been approved by the FDA, with AI systems offering advancements like sepsis warnings and cardiac assessments. For AI to be productive in healthcare, factors such as physician buy-in, patient acceptance, and ethical considerations are crucial. The FDA's Software as a Medical Device pathway is a step forward, but broader regulatory frameworks are needed to address both clinical and administrative AI tools. Collaborative regulation can enhance AI's impact on healthcare. https://lnkd.in/eQPAuMB4 #healthcare #healthcareinnovation #healthcareit #healthcareai #ai #genai #generativeai #llm #bigdata #ml #MedicalDevices #PatientEngagement #PatientCare #PatientOutcomes #AccessToCare

Transparency! A critical aspect of AI development and use, but somewhat tricky to define! Lucky for us, yesterday, FDA - along with Health Canada | Santé Canada and the United Kingdom's Medicines and Healthcare products Regulatory Agency - jointly released guiding principles for transparency for machine learning-enabled medical devices (MLMDs). The principles focus on the who, what, when, where, why, and how behind transparency of MLMDs and advocate for a "holistic understanding of users, environments and workflows" that may be addressed "by applying human-centred design principles." What's more? They summarized these principles in a handy chart. Learn more at the link below! (15/52) #artificialintelligence #ai #aitransparency #machinelearning

MedTech Dive's "Trendline" highlights key AI and digital health trends in MedTech, ranging from how the FDA is regulating these technologies, how they may improve patient outcomes, how clinicians feel about emerging tech, and interviews from some of the industry's leaders on how AI and digital are shaping the future of healthcare. https://lnkd.in/eNZwgpk9 #medtech #ai #digitalhealth

Transforming Healthcare with Responsible AI: A UniqueMinds.ai Perspective The rapid integration of AI into healthcare is a game changer, with nearly 1,000 FDA-authorized AI-enabled medical devices now shaping patient outcomes. This milestone demonstrates the power of innovation and the responsibility we bear as leaders in this space to ensure AI is developed and deployed ethically and effectively.   At UniqueMinds.ai, we believe this is where our Responsible AI Framework for Healthcare (RAIFH) truly shines. The RAIFH principles—spanning Fit for Use, Accuracy, Transparency, Privacy, Security, Fairness, and Accountability—align seamlessly with the FDA's total product life cycle approach for AI regulation. By embedding these principles into every phase of an AI solution’s journey, from design to post-market monitoring, we not only ensure compliance but also champion trust, equity, and value creation in healthcare innovation.   As FDA Commissioner Dr. Robert M. Califf highlights, a comprehensive strategy that integrates real-world data and global collaboration is key to maintaining the balance between technological advancement and patient safety. RAIFH enhances this vision by offering actionable guidance that empowers healthcare stakeholders to navigate the complexities of responsible AI adoption. The healthcare landscape is evolving rapidly, and we’re proud to lead the way in ensuring that innovation remains grounded in values that prioritize patient safety, equity, and trust.   I invite healthcare leaders and innovators to join the conversation on driving responsible AI adoption—let’s work together to ensure every AI solution is not just innovative but responsible as well.   #InsideUniqueMinds #AIinHealthcare #ResponsibleAI #RAIFH https://lnkd.in/gmiVNk93

🇬🇧Medicines and Healthcare products Regulatory Agency Embraces AI to Revolutionize Healthcare Regulation MHRA has this week released the publication, “Impact of AI on the Regulation of Medical Products Implementing the AI White Paper principles,” aiming to establish the UK as a science and technology superpower by 2030. The document details use of AI from three perspectives: 🔬 Regulatory Oversight: MHRA is advancing the regulation of AI across the entire medical product lifecycle. 🚀 Efficient Public Service: Leveraging AI to enhance the speed and accuracy of critical decision-making processes. 🛡️ Evidence-Based Decision-Making: Ensuring public and patient safety through decisions grounded in robust evidence. The updated technology roadmap emphasizes innovation, cybersecurity, and the elimination of outdated systems, which are crucial for the successful deployment of AI technologies. By applying AI and machine learning to analyze real-world data, MHRA is enhancing its understanding of the relationships between medical product exposures and clinical outcomes. This effort will be supported by insights from The Sudlow Review’s ‘Unifying Health Data in the UK.’ Several AI tools are in development across all business areas including regulatory activities and a Medicines Website Checking tool to combat the online sale of counterfeit medications and medical devices, promoting public safety and regulatory compliance. Exciting times ahead with the integration of AI to deliver safer and more effective healthcare solutions. 💊🖥️ Find it here https://lnkd.in/ekQBKVGb #AIinHealthcare #UKInnovation #RegulatoryExcellence #AI

The FDA recently provided insights into the regulatory processes for medical devices utilizing artificial intelligence or machine learning, collaborating with Health Canada and the UK's Medicines and Healthcare products Regulatory Agency to establish key principles for effective machine learning practices in 2021. Emphasizing transparency, the agencies highlighted the importance of disclosing how AI models generate results to users. This strategic move aims to clarify the role of AI in medical equipment and enhance overall understanding and trust in AI-driven healthcare technologies. #AIinHealthcare #RegulatoryInsightshttps://buff.ly/3VuGsWg

An important new paper from FDA, the agency lays out its thinking on transparency in AI. The agency methodically goes through the following general principles: 1. who (relevant audiences) 2. why (motivation) 3. what (relevant information) 4. where (placement of information) 5. when (timing) 6. how (methods used to support transparency) The biggest of those is what, i.e. what information needs to be shared. It's a general framework, but a good checklist.

Principles for good machine learning practice (GMLP) ensure the development of safe, effective, and high-quality AI/ML technologies that can learn from real-world use and improve device performance. While mainly focused on medical devices, GMLP can also be applied to all GXP applications emphasizing patient safety and product quality.