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

??????????: “Vision Language Models in Medicine” ??????????????: B′ería Chingnab′e Kalp′elb′e, Angel Gabriel Adaambiik, Wei Peng ??????: 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. ?????? ???????????????????? ???????????????????????? ?????? ???????????????????? ??????????????: ? 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. ?????????????????? ???????????????????? ???? ???????????????????? ???????????????? ???????????????? ??????????????: ? 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

??????????: “AI Applications for Thoracic Imaging: Considerations for Best Practice” ??????????????: Eui Jin Hwang, MD, PhD; Jin Mo Goo, MD, PhD; Chang Min Park, MD, PhD ??????: https://hubs.li/Q038V5NS0 ????????????????: 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. ?????? ????????????: 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. ???????????????????? ????????????: ? 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

?????? ?????????????? ???? ?????? ??.??. ????/???? ????????: ?????????????? ?????? ???????????????????? ???? ????/???? ????????: ? ?????????? ???? ???????????? ???????? ??????????????????: 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). ? ?????????????? ???????????? ???? ?????????????? ??????????????: 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. ?????????? ?????? ???????????????????? ????????????????????????: ? ?????? ???????????????? ???????? ???????????? ???? ??????????????: 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. ? ?????????? ???? ?????????????? ???? ???????????????? ????????????????????: 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.

????????????’?? ???????? ?????????? ?????????? ????????: ????????????, ???? ????-???????????? ?????????? ?????????????????????? ?????????????? ???????????? ???? ??????????, ?????????????????? ?????? 510(??) ?????????????????? ?????? ??????????????, ?? ?????????????? ???????????????? ?????? ????-???????? ?????????? ?????????? ?????????????? (????????????. 2025). ?????? ????????????-??????????-????-?????????????? ???????????? ???????? ???????????????????? ?????? ???? ???????????????????? ???? ???????????? ?????????? ???????????? ?????? ?????????????? ?????????? ???????????????????????? ???????? ????????????????-?????????? ???????????????? (????????????. 2025). ???? ???????????????? ??????????-?????????? ?????????? ?????????????? ???????? ?? ??????????????’?? ????????, ?????????????? ???????? ???? ?????????? ???????????????? ???? ?????????????????? ?????? ?????????????? ?????????????? ????????????????????. ???????????? ?????????? ?? ???????????????????? ???????????? ???? ?????? ????-?????????????? ???????????? ?????? ?????????? ??????????????, ???????????????????? ??????????????????, ?????? ?????????????????? ???? ?????? ??.??. (????????????. 2025).

??????????: Reducing the Workload of Medical Diagnosis through Artificial Intelligence: A Narrative Review ??????????????: Jinseo Jeong, Sohyun Kim, Lian Pan, Daye Hwang, Dongseop Kim, Jeongwon Choi. ??????: https://hubs.li/Q0372qJF0 ????????????????: 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. ?????? ??????????????????: 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. ???????????????????? ????????????: ? 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

??????????: Applications of Deep Learning in Trauma Radiology: A Narrative Review ??????????????: Chi-Tung Cheng, MD; Chun-Hsiang Ooyang; Chien-Hung Liao ??????: https://hubs.li/Q036tM9r0 ????????????????: Deep learning (DL) and artificial intelligence (AI) are transforming trauma radiology by enhancing diagnostic accuracy and efficiency in critical care. In this narrative review, Dr. Chi-Tung Cheng and colleagues discuss the fundamental concepts of DL in trauma imaging and explore current applications across various imaging modalities. The article also addresses challenges and future directions for integrating AI into trauma care. ?????? ??????????????????: 1. Deep Learning in Medical Imaging: DL algorithms, especially convolutional neural networks (CNNs), are vital for tasks like classification, segmentation, and detection in medical images. 2. Ultrasound (FAST Exams): DL models detect free fluid in Focused Assessment with Sonography for Trauma (FAST), aiding rapid assessment of internal bleeding with high accuracy. 3. Chest and Pelvic X-rays: AI assists in identifying traumatic findings such as rib fractures, pneumothorax, and pelvic fractures, improving speed and accuracy in emergency diagnostics. 4. CT Scan Analysis: DL algorithms detect intracranial hemorrhage, vertebral fractures, and organ injuries (spleen, liver, lungs), supporting precise diagnosis and timely interventions. 5. Whole-Body CT in Polytrauma: AI enhances interpretation efficiency in polytrauma patients, quickly identifying life-threatening injuries despite data complexity. 6. Data Acquisition and Privacy Challenges: Patient privacy concerns and the need for large, annotated datasets are significant hurdles in developing robust DL models. 7. Regulatory Considerations: Some AI products in trauma imaging are FDA-approved, but broader adoption requires compliance with FDA guidance on AI/ML-based Software as a Medical Device (SaMD). ???????????? ????????????????????: ? Federated Learning: Enables collaborative model training without sharing patient data, addressing privacy and improving model generalizability. ? Model Explainability: Enhancing transparency and interpretability is crucial for clinician trust and adoption. ? Multimodal Data Integration: Combining imaging with clinical data can provide comprehensive insights, supporting precision medicine. ???????????????????? ????????????: ? Regulatory Compliance: How can AI developers align their trauma imaging tools with FDA regulations on AI/ML-based SaMD to ensure safety and efficacy? ? Data Privacy Strategies: What approaches can be implemented to protect patient privacy while acquiring and sharing medical imaging data for AI development? ? Addressing Data Annotation Challenges: How can the medical community collaborate to create high-quality annotated datasets for training DL models? #DeepLearning #TraumaRadiology #MedicalImaging #ArtificialIntelligence #HealthcareInnovation #MedicalDevices

Continuing the discussion on FDA's recent alert on cybersecurity vulnerabilities in a patient monitor...?? If your device was cleared prior to September 2023, when FDA's latest cybersecurity guidance came into effect, you most likely need to reassess the cybersecurity of your device. This is especially true if your software device's 510(k) summary is a scanned paper document.. ?? (before 2013 when eCopies became mandatory) which was the case for this patient monitor cleared in 2011. Let this be a reminder that advancing technology in medical devices means that we need more sophisticated security measures to protect patient data and device functionality. If you have a legacy device and are considering whether or not you would meet current FDA requirements, find the proper experts who can help you identify gaps and remediate your cybersecurity. #MedicalDevices #FDA #AI

Should data management be included in AI-enabled device submissions? After reading FDA's 2025 latest guidance document on AI-enabled software functions, the message is clear: the quality, diversity, and quantity of your training data directly impacts patient care. The following key requirements seem to strike at the heart of responsible AI development: ? Comprehensive demographic representation in datasets ? Performance validation across multiple clinical sites and populations ? Clear documentation of data sources and collection methodology ? Robust quality control processes Data management isn't just about technical accuracy - it's about ensuring equitable healthcare outcomes. #MedicalDevices #FDA #AI