Innolitics

➡️ How CorticoMetrics Accelerated Neuroimage Processing and Streamlined FDA Approval with Our Expertise Neuroimaging is a critical tool in understanding the human brain, but processing MRI scan data efficiently and accurately has its challenges. CorticoMetrics, led by Co-Founder and CEO Nick Schmansky, faced significant hurdles with their neuroimage analysis software, ‘FreeSurfer’, which depended heavily on MATLAB. This dependency led to: 🔹Slow Runtime: MATLAB reliance caused prolonged processing times, hindering productivity. 🔹Complexity and Accessibility Issues: Extensive use of MATLAB made the software less accessible, especially for users with limited programming experience. 🔹Licensing Concerns: Potential violations due to proprietary MATLAB licensing posed risks for commercial use. The Solution • Port MATLAB Code to Python: By rewriting the MATLAB components in Python, we significantly enhanced the software’s speed and performance. Python’s versatility and efficiency made ‘FreeSurfer’ more responsive and capable. • Improve Accessibility: Transitioning to Python simplified the software, making it more user-friendly. This opened doors for a broader user base, including those less familiar with complex programming languages. • Ensure Licensing Compliance: Eliminating MATLAB dependency mitigated licensing risks. The software became fully compatible with open-source licenses like MIT, BSD, or Apache 2.0, facilitating commercial usage without legal concerns. Our Work in Action ⭐ Integration with MRI Machines and PACS Systems: We assisted in integrating ‘AutoRegister’ (an extension of ‘FreeSurfer’) with MRI scanners and hospital Picture Archiving and Communication Systems (PACS), streamlining deployment in clinical settings. ⭐ On-Site Testing Support: Recognizing the challenges of scheduling MRI time, we equipped CorticoMetrics’ engineers with the tools and knowledge to make on-site software adjustments efficiently during limited testing windows. ⭐ Machine Simulation Development: To alleviate dependency on physical MRI machines for testing, we developed an MRI emulator, allowing the team to simulate and test the software extensively without additional costs. ⭐ Integrated Testing and Documentation: We delivered thorough testing protocols and documentation essential for FDA approval processes. Our contributions ensured that the software met regulatory standards and was prepared for a successful 510(k) submission. The Result Our collaboration expedited CorticoMetrics’ path to market. The software was not only rewritten and validated but also fortified with robust documentation and testing, positioning them on track to submit their first 510(k) application. Have you faced similar challenges with software efficiency or regulatory compliance in the medical field? Let’s connect and explore how we can help accelerate your projects. Feel free to share your thoughts or experiences in the comments below! #MedicalDevices #AI #FDA

To any FDA staff who would like to continue working remotely on the mission of bringing safe and effective medical device software to market, please reach out to me or anyone at my company Innolitics You deserve better than this.

𝖮𝗎𝗋 𝗍𝖾𝖺𝗆 𝗂𝗌 𝗀𝗋𝗈𝗐𝗂𝗇𝗀! 𝖶𝖾’𝗋𝖾 𝗅𝗈𝗈𝗄𝗂𝗇𝗀 𝖿𝗈𝗋 𝖺 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗍𝗈 𝗈𝗏𝖾𝗋𝗌𝖾𝖾 𝗆𝗎𝗅𝗍𝗂𝗉𝗅𝖾 𝖼𝗅𝗂𝖾𝗇𝗍 𝗉𝗋𝗈𝗃𝖾𝖼𝗍𝗌, 𝖼𝗈𝗈𝗋𝖽𝗂𝗇𝖺𝗍𝖾 𝖼𝗋𝗈𝗌𝗌-𝖿𝗎𝗇𝖼𝗍𝗂𝗈𝗇𝖺𝗅 𝗍𝖾𝖺𝗆𝗌, 𝖺𝗇𝖽 𝖾𝗇𝗌𝗎𝗋𝖾 𝗈𝗇-𝗍𝗂𝗆𝖾, 𝗈𝗇-𝖻𝗎𝖽𝗀𝖾𝗍 𝗋𝖾𝗌𝗎𝗅𝗍𝗌. 𝗪𝗵𝗼 𝗪𝗲’𝗿𝗲 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗙𝗼𝗿: ✅ 5+ 𝗒𝖾𝖺𝗋𝗌 𝗈𝖿 𝗍𝖾𝖼𝗁𝗇𝗂𝖼𝖺𝗅 𝗉𝗋𝗈𝗃𝖾𝖼𝗍 𝗆𝖺𝗇𝖺𝗀𝖾𝗆𝖾𝗇𝗍 𝗂𝗇 𝖺 𝖡2𝖡 𝖾𝗇𝗏𝗂𝗋𝗈𝗇𝗆𝖾𝗇𝗍 (𝖬𝖾𝖽𝖳𝖾𝖼𝗁/𝗌𝗈𝖿𝗍𝗐𝖺𝗋𝖾 𝗉𝗋𝖾𝖿𝖾𝗋𝗋𝖾𝖽). ✅ 𝖲𝗍𝗋𝗈𝗇𝗀 𝗌𝗍𝖺𝗄𝖾𝗁𝗈𝗅𝖽𝖾𝗋 𝗆𝖺𝗇𝖺𝗀𝖾𝗆𝖾𝗇𝗍 𝖺𝗇𝖽 𝖼𝗈𝗆𝗆𝗎𝗇𝗂𝖼𝖺𝗍𝗂𝗈𝗇 𝗌𝗄𝗂𝗅𝗅𝗌. ✅ 𝖤𝗑𝗉𝖾𝗋𝗂𝖾𝗇𝖼𝖾 𝗎𝗌𝗂𝗇𝗀 𝗉𝗋𝗈𝗃𝖾𝖼𝗍 𝗆𝖺𝗇𝖺𝗀𝖾𝗆𝖾𝗇𝗍 𝗍𝗈𝗈𝗅𝗌 (𝖭𝗈𝗍𝗂𝗈𝗇 𝗂𝗌 𝖺 𝖻𝗂𝗀 𝗉𝗅𝗎𝗌!). ✅ 𝖥𝗅𝖾𝗑𝗂𝖻𝗂𝗅𝗂𝗍𝗒 𝖺𝗇𝖽 𝖺𝖽𝖺𝗉𝗍𝖺𝖻𝗂𝗅𝗂𝗍𝗒 𝗂𝗇 𝖿𝖺𝗌𝗍-𝖼𝗁𝖺𝗇𝗀𝗂𝗇𝗀 𝗉𝗋𝗈𝗃𝖾𝖼𝗍𝗌. 𝗪𝗵𝗮𝘁 𝗪𝗲 𝗢𝗳𝗳𝗲𝗿: 🌟 𝖢𝗈𝗆𝗉𝖾𝗍𝗂𝗍𝗂𝗏𝖾 𝖻𝖺𝗌𝖾 𝗌𝖺𝗅𝖺𝗋𝗒 + 𝗉𝖾𝗋𝖿𝗈𝗋𝗆𝖺𝗇𝖼𝖾 𝖻𝗈𝗇𝗎𝗌. 🌟 𝖢𝗈𝗆𝗉𝗋𝖾𝗁𝖾𝗇𝗌𝗂𝗏𝖾 𝖯𝖳𝖮, 𝗁𝖾𝖺𝗅𝗍𝗁𝖼𝖺𝗋𝖾 𝖻𝖾𝗇𝖾𝖿𝗂𝗍𝗌, 𝖺𝗇𝖽 401(𝗄). 🌟 𝖥𝗎𝗅𝗅𝗒 𝗋𝖾𝗆𝗈𝗍𝖾 𝗐𝗈𝗋𝗄 𝗐𝗂𝗍𝗁 𝗈𝖼𝖼𝖺𝗌𝗂𝗈𝗇𝖺𝗅 𝖼𝗈𝗆𝗉𝖺𝗇𝗒 𝗋𝖾𝗍𝗋𝖾𝖺𝗍𝗌. 🌟 𝖠 𝖼𝗈𝗅𝗅𝖺𝖻𝗈𝗋𝖺𝗍𝗂𝗏𝖾, 𝗌𝗎𝗉𝗉𝗈𝗋𝗍𝗂𝗏𝖾 𝗍𝖾𝖺𝗆 𝗍𝗁𝖺𝗍 𝗏𝖺𝗅𝗎𝖾𝗌 𝖼𝗈𝗇𝗍𝗂𝗇𝗎𝗈𝗎𝗌 𝗂𝗆𝗉𝗋𝗈𝗏𝖾𝗆𝖾𝗇𝗍. 𝖩𝗈𝗂𝗇 𝗎𝗌 𝖺𝗇𝖽 𝗁𝖾𝗅𝗉 𝖽𝖾𝗅𝗂𝗏𝖾𝗋 𝗅𝗂𝖿𝖾-𝖼𝗁𝖺𝗇𝗀𝗂𝗇𝗀 𝗆𝖾𝖽𝗂𝖼𝖺𝗅 𝗌𝗈𝗅𝗎𝗍𝗂𝗈𝗇𝗌! 𝖫𝗂𝗇𝗄 𝗍𝗈 𝖿𝗎𝗅𝗅 𝗃𝗈𝖻 𝗉𝗈𝗌𝗍𝗂𝗇𝗀 𝗁𝖾𝗋𝖾: https://hubs.li/Q03cDXWf0

𝗧𝗶𝘁𝗹𝗲: The Future of Radiology: Multimodal AI and Superdiagnostics 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Felix Nensa 𝗗𝗢𝗜: https://hubs.li/Q03cpRkl0 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: AI has been transforming radiology for over a decade, but where is it really heading? In The Future of Radiology: The Path Towards Multimodal AI and Superdiagnostics, Felix Nensa discusses how AI is evolving from a simple image-processing tool into an integrative system, combining imaging, genomics, pathology, and real-world patient data. 𝗞𝗲𝘆 𝗣𝗼𝗶𝗻𝘁𝘀: 1. Radiology is the AI testbed – Standardized data formats (DICOM) and early adoption of deep learning made radiology the proving ground for AI in medicine. 2. The “replacement” myth – AI won’t replace radiologists but will redefine their roles, turning them into diagnostic orchestrators rather than just image interpreters. This is similar to what AI is doing for software development. It won't replace engineers, but it will turn them into software development orchestrators rather than just coders. 3. Multimodal AI is the next frontier – AI is shifting from unimodal (pure imaging) to multimodal, integrating diverse data streams for a holistic diagnostic approach. 4. From AI hype to practical integration – The early AI excitement led to exaggerated expectations. Now, AI tools are maturing into augmentative, rather than disruptive, technologies. 5. Generative AI is improving usability – LLMs (like ChatGPT) are making AI interfaces more intuitive, reducing barriers to adoption among clinicians. 6. The knowledge gap is real – Radiologists will need training in data science, bioinformatics, and systems biology to effectively lead AI-driven diagnostics. 7. Interdisciplinary collaboration is a must – Radiologists, engineers, and bioinformaticians must work together to synthesize AI-driven insights while managing cognitive overload. 8. Regulatory frameworks need to evolve – AI adoption in radiology will depend on how well regulatory agencies like the FDA adapt to multimodal AI solutions. 9. Structured workflows matter – To maintain trust in AI diagnostics, radiologists must adopt structured, explainable workflows and communicate AI-supported findings transparently. 10. The human element remains crucial – AI enhances decision-making but cannot replace the nuanced clinical judgment of radiologists. 💡 What does this mean for medical device developers, regulatory consultants, and entrepreneurs? Multimodal AI demands new regulations, validation, and risk management. As AI shifts from assisting imaging to full diagnostics, approval pathways will be challenged. AI-enabled SaMD developers should prioritize compliance, explainability, and human oversight. 📢 What are your thoughts? How do you see multimodal AI shaping the future of medical diagnostics? #Radiology #ArtificialIntelligence #MedicalImaging #SaMD #AIinHealthcare #FDA

𝗧𝗶𝘁𝗹𝗲: “Vision Language Models in Medicine” 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: B´ería Chingnab´e Kalp´elb´e, Angel Gabriel Adaambiik, Wei Peng 𝗗𝗢𝗜: https://hubs.li/Q03b0VZc0 Vision-Language Models (VLMs) integrate visual data, such as medical images, with textual information to enhance healthcare tasks like diagnostic accuracy, automated report generation, and clinical decision-making. Key advancements include models such as BLIP-2, MedCLIP, BioViL, VividMed, and Med-Flamingo, each employing techniques like contrastive learning, masked language modeling, and instruction tuning to link visual and textual modalities effectively. 𝗞𝗲𝘆 𝗯𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸𝘀 𝗳𝗮𝗰𝗶𝗹𝗶𝘁𝗮𝘁𝗶𝗻𝗴 𝗩𝗟𝗠 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • CheXpert and CheXpert Plus: Widely used datasets for chest X-ray classification, though initially limited by lack of demographics, improved with CheXpert Plus to include broader annotations. • MIMIC-CXR: Comprehensive chest X-ray dataset used for pathology classification and report generation tasks. • PMC-VQA and OmniMedVQA: Large-scale benchmarks assessing model performance across diverse medical visual question-answering scenarios. • RadBench: Evaluates VLM capabilities across multiple radiology tasks using diverse imaging data. 𝗣𝗿𝗼𝗺𝗶𝗻𝗲𝗻𝘁 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝘁𝗼 𝘄𝗶𝗱𝗲𝘀𝗽𝗿𝗲𝗮𝗱 𝗰𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • Data Scarcity and Biases: Limited availability and diversity of high-quality, annotated medical datasets, especially for rare diseases or underrepresented populations. • Limited Generalization: Models often specialize narrowly, making it difficult to generalize across different imaging modalities (e.g., X-ray to MRI). • Interpretability Issues: Many advanced VLMs lack transparent decision-making processes, causing clinicians to distrust automated predictions. • Ethical Concerns: Privacy risks associated with patient data use, alongside fairness concerns due to demographic imbalances. • Computational Demands: Significant resource requirements to train and maintain advanced VLMs limit accessibility, particularly in resource-constrained environments. • Integration Challenges: Difficulty in seamlessly integrating VLM outputs into existing clinical workflows and electronic health records. Future directions emphasize addressing these challenges through expanding datasets (e.g., Medtrinity-25M), improving cross-modal generalization (e.g., leveraging contrastive learning approaches), enhancing interpretability via explainable AI methods, and developing lightweight models that reduce computational demands (e.g., DeepSeek-VL). Advancing federated learning can also ensure data privacy compliance, fostering broader and ethically responsible adoption in clinical settings. #VisionLanguageModels #MedicalAI #MedTech #SaMD #FDA #HealthcareInnovation #MachineLearning #DigitalHealth #MedicalImaging #AIinMedicine #MultimodalAI

𝗧𝗶𝘁𝗹𝗲: “AI Applications for Thoracic Imaging: Considerations for Best Practice” 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Eui Jin Hwang, MD, PhD; Jin Mo Goo, MD, PhD; Chang Min Park, MD, PhD 𝗗𝗢𝗜: https://hubs.li/Q038V5NS0 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: Here is an insightful article that reviews the current status and practical challenges of integrating AI into thoracic imaging. The paper discusses how AI is applied for tasks such as computer-aided detection and triage on chest radiographs and low-dose chest CT scans for lung cancer screening and pulmonary embolism. It also addresses key implementation challenges, including performance evaluation, IT infrastructure integration, training, liability issues, and potential disparities. Additionally, the article highlights promising next-generation innovations like large multimodal language models (LLMs) that can generate text reports and explain examination findings while emphasizing the necessity of regulatory compliance. 𝗞𝗲𝘆 𝗣𝗼𝗶𝗻𝘁𝘀: 1. AI is rapidly entering thoracic imaging; as of May 2024, 882 FDA-cleared AI-enabled devices exist, with 671 used in radiology. 2. Leading applications include computer-aided detection and triage support on chest radiographs and CT scans for lung cancer screening and pulmonary embolism. 3. Practical implementation requires objective on-site performance evaluations, seamless IT integration, and robust post-deployment monitoring. 4. A major challenge is educating radiologists and trainees to use AI effectively while mitigating liability risks from diagnostic errors. 5. Data distribution disparities and unequal access to technology may exacerbate health inequities. 6. Next-generation platforms, such as multimodal LLMs, hold promise for transforming reporting by automatically generating descriptive text and explaining results, though further research is needed. 7. Real-world studies reveal improvements in reading times and detection yields despite issues like false referrals. 8. The discussion underscores the importance of clear regulatory guidance, referencing recent FDA guidance on AI/ML-enabled SaMD. 9. Continuous performance monitoring is essential to address potential degradation due to data drift or evolving imaging technology. 10. Standardized training data and local customization of AI systems are critical for achieving optimal performance. 11. Integration challenges—such as cybersecurity, interoperability, and workflow continuity—must be addressed for successful AI deployment. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: • How can existing IT systems be integrated with new AI tools while ensuring robust cybersecurity? • What further steps are needed to harmonize FDA regulatory requirements with rapid AI innovation in SaMD? • How might continuous on-site performance monitoring help mitigate data drift and maintain diagnostic accuracy? #AIinHealthcare #MedicalDevices #SaMD #Radiology #FDA #HealthcareInnovation #MedicalImaging

𝗞𝗲𝘆 𝗨𝗽𝗱𝗮𝘁𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗨.𝗦. 𝗔𝗜/𝗠𝗟 𝗦𝗮𝗠𝗗: 𝗙𝘂𝗻𝗱𝗶𝗻𝗴 𝗮𝗻𝗱 𝗜𝗻𝘃𝗲𝘀𝘁𝗺𝗲𝗻𝘁 𝗶𝗻 𝗔𝗜/𝗠𝗟 𝗦𝗮𝗠𝗗: • 𝗠𝗮𝗷𝗼𝗿 𝗔𝗜 𝗛𝗲𝗮𝗹𝘁𝗵 𝗧𝗲𝗰𝗵 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗻𝗴: Abridge, a Pittsburgh-based health AI company, raised $250 million in a funding round led by prominent tech investor Elad Gil and IVP (Pritam Biswas. 2025). Abridge uses AI to automatically generate medical documentation (clinic visit notes) for physicians, and this large Series D investment – at an estimated $2.75 billion valuation – underscores sustained investor appetite for AI-enabled healthcare solutions (Chris Metinko. 2025). • 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗗𝗲𝘃𝗶𝗰𝗲 𝗔𝗜 𝗩𝗲𝗻𝘁𝘂𝗿𝗲 𝗙𝘂𝗻𝗱𝗶𝗻𝗴: VitalConnect, a California medtech firm specializing in wearable biosensors for cardiac monitoring, closed a $100 million financing (equity and debt) to expand its AI-driven remote monitoring platform (Chris Metinko. 2025). The round, led by Ally Bridge Group, will help VitalConnect scale its FDA-cleared sensor technology that tracks vital signs and detects cardiac events. Investors continue to back SaMD companies blending hardware and AI to address clinical needs in real time. 𝗟𝗲𝗴𝗮𝗹 𝗮𝗻𝗱 𝗖𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁𝘀: • 𝗙𝗗𝗔 𝗦𝘁𝗮𝗳𝗳𝗶𝗻𝗴 𝗖𝘂𝘁𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗜 𝗥𝗲𝘃𝗶𝗲𝘄𝘀: A wave of FDA personnel layoffs over the past weekend hit the Center for Devices and Radiological Health (CDRH) particularly hard, eliminating over 200 staff – including many specialists in digital health and AI (MedTech Dive. 2025). Industry groups like AdvaMed warn these cuts could slow review times for AI/ML device submissions and are examining the legality of the terminations (MedTech Dive. 2025). Reports indicate entire teams focused on novel tech were affected, prompting concern about the FDA’s capacity to evaluate cutting-edge SaMD products in the near term. • 𝗖𝗮𝗹𝗹𝘀 𝘁𝗼 𝗥𝗲𝘃𝗶𝘀𝗶𝘁 𝗔𝗜 𝗦𝗼𝗳𝘁𝘄𝗮𝗿𝗲 𝗥𝗲𝗴𝘂𝗹𝗮𝘁𝗶𝗼𝗻: Former FDA Commissioner Scott Gottlieb published a commentary urging the agency to dial back oversight of certain AI-based decision support tools. He argues that recent FDA policies (such as the 2022 clinical decision support guidance) have “added new uncertainties” and may be overly restrictive (Gottlieb. 2025). Gottlieb recommends reverting to the 21st Century Cures Act approach, which exempted many clinical decision support (CDS) software tools from premarket review, so long as they merely augment clinician decision-making and don’t provide autonomous diagnoses or treatments (Gottlieb. 2025). This perspective, coming as the new administration evaluates AI regulations, highlights an ongoing debate over how to balance innovation with patient safety in SaMD compliance.

𝗣𝗿𝗮𝗻𝗮𝗤’𝘀 𝗛𝗼𝗺𝗲 𝗦𝗹𝗲𝗲𝗽 𝗔𝗽𝗻𝗲𝗮 𝗧𝗲𝘀𝘁: 𝖯𝗋𝖺𝗇𝖺𝖰, 𝖺𝗇 𝖠𝖨-𝖽𝗋𝗂𝗏𝖾𝗇 𝗌𝗅𝖾𝖾𝗉 𝖽𝗂𝖺𝗀𝗇𝗈𝗌𝗍𝗂𝖼𝗌 𝗌𝗍𝖺𝗋𝗍𝗎𝗉 𝖻𝖺𝖼𝗄𝖾𝖽 𝖻𝗒 𝖭𝖠𝖵𝖤𝖱, 𝖺𝗇𝗇𝗈𝗎𝗇𝖼𝖾𝖽 𝖥𝖣𝖠 510(𝗄) 𝖼𝗅𝖾𝖺𝗋𝖺𝗇𝖼𝖾 𝖿𝗈𝗋 𝖳𝗂𝗉𝖳𝗋𝖺𝖰, 𝖺 𝖼𝗈𝗆𝗉𝖺𝖼𝗍 𝗐𝖾𝖺𝗋𝖺𝖻𝗅𝖾 𝖿𝗈𝗋 𝖺𝗍-𝗁𝗈𝗆𝖾 𝗌𝗅𝖾𝖾𝗉 𝖺𝗉𝗇𝖾𝖺 𝗍𝖾𝗌𝗍𝗂𝗇𝗀 (𝖯𝗋𝖺𝗇𝖺𝖰. 2025). 𝖳𝗁𝖾 𝗌𝗂𝗇𝗀𝗅𝖾-𝗉𝗈𝗂𝗇𝗍-𝗈𝖿-𝖼𝗈𝗇𝗍𝖺𝖼𝗍 𝖽𝖾𝗏𝗂𝖼𝖾 𝗎𝗌𝖾𝗌 𝖻𝗂𝗈𝗌𝖾𝗇𝗌𝗈𝗋𝗌 𝖺𝗇𝖽 𝖠𝖨 𝖺𝗅𝗀𝗈𝗋𝗂𝗍𝗁𝗆𝗌 𝗍𝗈 𝖽𝖾𝗍𝖾𝖼𝗍 𝖺𝗉𝗇𝖾𝖺 𝖾𝗏𝖾𝗇𝗍𝗌 𝖺𝗇𝖽 𝖺𝗇𝖺𝗅𝗒𝗓𝖾 𝗌𝗅𝖾𝖾𝗉 𝖺𝗋𝖼𝗁𝗂𝗍𝖾𝖼𝗍𝗎𝗋𝖾 𝗐𝗂𝗍𝗁 𝖼𝗅𝗂𝗇𝗂𝖼𝖺𝗅-𝗀𝗋𝖺𝖽𝖾 𝖺𝖼𝖼𝗎𝗋𝖺𝖼𝗒 (𝖯𝗋𝖺𝗇𝖺𝖰. 2025). 𝖡𝗒 𝖾𝗇𝖺𝖻𝗅𝗂𝗇𝗀 𝗆𝗎𝗅𝗍𝗂-𝗇𝗂𝗀𝗁𝗍 𝗌𝗅𝖾𝖾𝗉 𝗌𝗍𝗎𝖽𝗂𝖾𝗌 𝖿𝗋𝗈𝗆 𝖺 𝗉𝖺𝗍𝗂𝖾𝗇𝗍’𝗌 𝗁𝗈𝗆𝖾, 𝖳𝗂𝗉𝖳𝗋𝖺𝖰 𝖺𝗂𝗆𝗌 𝗍𝗈 𝗅𝗈𝗐𝖾𝗋 𝖻𝖺𝗋𝗋𝗂𝖾𝗋𝗌 𝗍𝗈 𝖽𝗂𝖺𝗀𝗇𝗈𝗌𝗂𝗌 𝖺𝗇𝖽 𝗂𝗆𝗉𝗋𝗈𝗏𝖾 𝗉𝖺𝗍𝗂𝖾𝗇𝗍 𝖼𝗈𝗆𝗉𝗅𝗂𝖺𝗇𝖼𝖾. 𝖯𝗋𝖺𝗇𝖺𝖰 𝗉𝗅𝖺𝗇𝗌 𝖺 𝗇𝖺𝗍𝗂𝗈𝗇𝗐𝗂𝖽𝖾 𝗅𝖺𝗎𝗇𝖼𝗁 𝗈𝖿 𝗍𝗁𝖾 𝖠𝖨-𝖾𝗇𝖺𝖻𝗅𝖾𝖽 𝖽𝖾𝗏𝗂𝖼𝖾 𝖿𝗈𝗋 𝗌𝗅𝖾𝖾𝗉 𝖼𝗅𝗂𝗇𝗂𝖼𝗌, 𝗍𝖾𝗅𝖾𝗁𝖾𝖺𝗅𝗍𝗁 𝗉𝗋𝗈𝗏𝗂𝖽𝖾𝗋𝗌, 𝖺𝗇𝖽 𝗁𝗈𝗌𝗉𝗂𝗍𝖺𝗅𝗌 𝗂𝗇 𝗍𝗁𝖾 𝖴.𝖲. (𝖯𝗋𝖺𝗇𝖺𝖰. 2025).

𝗧𝗶𝘁𝗹𝗲: Reducing the Workload of Medical Diagnosis through Artificial Intelligence: A Narrative Review 𝗔𝘂𝘁𝗵𝗼𝗿𝘀: Jinseo Jeong, Sohyun Kim, Lian Pan, Daye Hwang, Dongseop Kim, Jeongwon Choi. 𝗗𝗢𝗜: https://hubs.li/Q0372qJF0 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄: This narrative review examines how AI is reshaping diagnostics by reducing diagnostic time and data volume across specialties. It analyzes 51 studies (from January 2019 to February 2024) that compared AI-enhanced workflows with traditional methods. The paper categorizes AI applications based on their role in supporting or even independently performing diagnoses. It provides valuable regulatory insights—referencing FDA guidance for SaMD and AI/ML-based devices—to ensure safe clinical integration. 𝗞𝗲𝘆 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: 1) The review evaluated 51 studies to assess AI’s impact on reducing clinician workload and improving efficiency. 2) AI applications were classified into four groups: • Category A: Providing supporting materials (e.g., annotated images) to assist clinicians. • Category B: Reducing the volume of data that clinicians must review. • Category C: Allowing AI to perform independent diagnoses. • Category D: Reducing data volume without measured change in diagnostic time. 3) In radiology, AI reduced diagnostic scan time by over 90% in instances like CT lesion detection and contrast-enhanced mammography. 4) Pathology benefits included significant workload reduction by automating tasks such as slide filtering and aiding cancer detection. 5) The review highlights how digitized, standardized imaging in radiology facilitates higher levels of AI performance compared to other fields with more variable data formats. 6) While AI holds promise in addressing workforce shortages and improving accuracy, challenges remain regarding integration into clinical workflows. 7) Some studies noted delays (e.g., data upload times) and workflow inefficiencies that need further optimization. 8) Ethical, data standardization, and regulatory issues are discussed, emphasizing the need for adherence to FDA guidance on SaMD and AI/ML products. 9) The review suggests successful AI integration requires continuous collaboration between clinicians and technologists. 10) Future research should consider expanding AI’s application beyond diagnostics to treatment decisions, patient management, and real-time decision support. 𝗗𝗶𝘀𝗰𝘂𝘀𝘀𝗶𝗼𝗻 𝗣𝗼𝗶𝗻𝘁𝘀: • How can we further streamline AI integration into existing clinical workflows without compromising data security or patient safety? • What strategies might address variability and standardization challenges, specifically in fields like pathology? • How will evolving FDA guidance impact the safe, effective introduction of AI/ML technologies into healthcare? #AIinHealthcare #DigitalHealth #MedTech #SaMD #HealthcareInnovation #RegulatoryAffairs #MedicalDevices #DiagnosticEfficiency

🤔"What is the difference between a De Novo and a 510(k)?" With the surge in AI-enabled medical devices, this question is becoming increasingly common. Let me clarify a key misconception: not every novel device automatically qualifies for the De Novo pathway. In order for a device to be appropriate for a De Novo request, the following must be true: 1. General controls (or general + special controls) must provide reasonable assurance of safety/effectiveness 2. No viable predicate device exists While devices going through either pathway might end up in the same risk classification, the crucial differentiator is predicate existence. Without a predicate, substantial equivalence documentation becomes irrelevant. Moreover, De Novo submissions require documentation that is not requested or applicable in 510(k)s. For example: - Detailed benefit-risk analysis - Proposed classification justification (Class I or II) - Discussion of sufficient general controls or needed special controls Other important considerations for De Novos include: - Review Timeline: De Novo (150 days) vs 510(k) (90 days) - FDA User Fees: De Novo fees are over 6x higher than 510(k) - Documentation Requirements: More complex for De Novo 💡What are other factors to consider when determining whether a De Novo pathway is appropriate? #fda #510k #medicaldevices