🔬 New Surgical Endoscopy publication summary from the @classicaproject team, 𝑷𝒆𝒓𝒊𝒕𝒐𝒏𝒆𝒂𝒍 𝒐𝒑𝒆𝒏𝒊𝒏𝒈 𝒅𝒖𝒓𝒊𝒏𝒈 𝒕𝒓𝒂𝒏𝒔𝒂𝒏𝒂𝒍 𝒆𝒏𝒅𝒐𝒔𝒄𝒐𝒑𝒊𝒄 𝒎𝒊𝒄𝒓𝒐𝒔𝒖𝒓𝒈𝒆𝒓𝒚: 𝒄𝒂𝒏 𝒑𝒓𝒆𝒐𝒑𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝒑𝒐𝒔𝒊𝒕𝒊𝒐𝒏𝒊𝒏𝒈 𝒂𝒔𝒔𝒆𝒔𝒔𝒎𝒆𝒏𝒕 𝒊𝒎𝒑𝒓𝒐𝒗𝒆 𝒊𝒏𝒕𝒓𝒂𝒐𝒑𝒆𝒓𝒂𝒕𝒊𝒗𝒆 𝒎𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕? Peritoneal opening during TEM/TEO surgery is largely predictable, and when well-managed, doesn't increase overall patient risk. Important findings for safer surgical planning. 🏥 👉 @albertoarezzo, Ammirati, Distefano et al., @unito #MinimallyInvasiveSurgery #Colorectal #TEM #KnowledgeUnwrapped 📚 classicaproject.eu/2026/03/k…

In an August 2025 🇪🇺 @classicaproject publication in npj Digital Medicine, '𝑬𝒏𝒂𝒃𝒍𝒊𝒏𝒈 𝒔𝒆𝒄𝒖𝒓𝒆 𝒂𝒏𝒅 𝒔𝒆𝒍𝒇 𝒅𝒆𝒕𝒆𝒓𝒎𝒊𝒏𝒆𝒅 𝒉𝒆𝒂𝒍𝒕𝒉 𝒅𝒂𝒕𝒂 𝒔𝒉𝒂𝒓𝒊𝒏𝒈 𝒂𝒏𝒅 𝒄𝒐𝒏𝒔𝒆𝒏𝒕 𝒎𝒂𝒏𝒂𝒈𝒆𝒎𝒆𝒏𝒕,' CLASSICA PI @TiMinCeBIL, @CeBIL_Center, @UCPH_Research, with co-authors Cindy Welzel, TU Dresden, Max Ostermann, Technische Universität Dresden, Hannah Louise Smith, University of Copenhagen (Københavns Universitet), @toralf_kirsten, Leipzig University, and Stephen Gilbert, Technische Universität Dresden, presented a conceptual framework for secure and self-determined health data sharing, discussing how emerging digital technologies can support trustworthy consent management. This research aims to explore how people can gain meaningful control over how their health data, which can be generated by apps and wearable devices, is shared and used for healthcare and research. Why is this research important? Check out our easy-to-digest, “Knowledge Unwrapped” summary 👉🏽 classicaproject.eu/2026/02/k…

📣 Publication news! @CLASSICAproject explores how AI analysis of fluorescence perfusion can support in-situ classification of significant rectal polyps. 📄 "𝑨𝒓𝒕𝒊𝒇𝒊𝒄𝒊𝒂𝒍 𝒊𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆 𝒄𝒍𝒂𝒔𝒔𝒊𝒇𝒊𝒄𝒂𝒕𝒊𝒐𝒏 𝒐𝒇 𝒓𝒆𝒄𝒕𝒂𝒍 𝒏𝒆𝒐𝒑𝒍𝒂𝒔𝒊𝒂 𝒃𝒚 𝒆𝒏𝒅𝒐𝒔𝒄𝒐𝒑𝒊𝒄 𝒇𝒍𝒖𝒐𝒓𝒆𝒔𝒄𝒆𝒏𝒄𝒆 𝒑𝒆𝒓𝒇𝒖𝒔𝒊𝒐𝒏 𝒂𝒏𝒂𝒍𝒚𝒔𝒊𝒔" by @PtrckBlnd et al. in @SciReports classicaproject.eu/2026/02/c… #HorizonEU #AI #colorectalcancer

How does the EU AI Act address dataset quality and bias in AI systems, and what are the practical and legal implications of these provisions—particularly regarding data governance, the "debiasing exception" for sensitive data, and enforcement mechanisms? ⚖️ Authors of the Journal of Law and the Biosciences article, "𝑾𝒊𝒍𝒍 𝒕𝒉𝒆 𝑬𝑼 𝑨𝑰 𝑨𝒄𝒕 𝒉𝒆𝒍𝒑 𝒕𝒐 𝒎𝒊𝒕𝒊𝒈𝒂𝒕𝒆 𝒅𝒂𝒕𝒂𝒔𝒆𝒕 𝒃𝒊𝒂𝒔 𝒊𝒏 𝒎𝒆𝒅𝒊𝒄𝒂𝒍 𝑨𝑰?", @classicaproject researcher Emilia Niemiec, @CeBIL_Center, University of Copenhagen, Peter Davis, CeBIL, U of Copenhagen, and Mathias K. Hauglid, Wikborg Rein Advokatfirma AS, examine how Europe's new AI law tries to make AI systems fairer by setting rules about the data used to train them, requiring representative datasets and even allowing use of highly-protected sensitive data specifically to fix bias problems. The authors analysed whether these rules will work in practice, especially in medical AI, given the tension between accessing data to check for bias and protecting patient privacy. Read on 👉classicaproject.eu/2026/02/c…

#AI is transforming healthcare, but who's liable when it gets a diagnosis wrong? Professor @gerke_sara's @classicaproject research on "nutrition labels" for AI medical devices is sparking crucial conversations in @MedEconomics and beyond about transparency, accountability, and the future of medical malpractice. Read a recap of the recent media flurry ❄️ here: classicaproject.eu/2026/01/p…

What if AI medical devices came with labels as clear as the nutrition facts on your food? That's the vision behind groundbreaking research from @classicaproject Principal Investigator @SaraGerke, Associate Professor of Law at the @UofIllinois Urbana-Champaign. In her new article published in the Emory Law Journal (Volume 74, Issue 5), Gerke addresses a critical gap. There are currently no labeling requirements tailored to AI/ML-based medical devices in the U.S. This leaves healthcare providers and patients without essential information about how these AI systems work, what data they're trained on, and how to use them responsibly. Here is our latest "Knowledge Unwrapped" article, where we break down Professor Gerke's comprehensive labeling framework in an easy-to-understand format. classicaproject.eu/2026/01/k…

Who bears responsibility and potential tort liability when AI-powered ambient clinical documentation scribes generate inaccurate patient information that causes patient injury, particularly in high-stakes contexts, like cancer care? In the latest @classicaproject publication, 𝑳𝒊𝒂𝒃𝒊𝒍𝒊𝒕𝒚 𝑹𝒊𝒔𝒌𝒔 𝒐𝒇 𝑨𝒎𝒃𝒊𝒆𝒏𝒕 𝑪𝒍𝒊𝒏𝒊𝒄𝒂𝒍 𝑾𝒐𝒓𝒌𝒇𝒍𝒐𝒘𝒔 𝑾𝒊𝒕𝒉 𝑨𝒓𝒕𝒊𝒇𝒊𝒄𝒊𝒂𝒍 𝑰𝒏𝒕𝒆𝒍𝒍𝒊𝒈𝒆𝒏𝒄𝒆 𝒇𝒐𝒓 𝑪𝒍𝒊𝒏𝒊𝒄𝒊𝒂𝒏𝒔, 𝑯𝒐𝒔𝒑𝒊𝒕𝒂𝒍𝒔, 𝒂𝒏𝒅 𝑴𝒂𝒏𝒖𝒇𝒂𝒄𝒕𝒖𝒓𝒆𝒓𝒔, in the Journal of Clinical Oncology: Oncology Practice, authors Associate Professor @SaraGerke, Dipl Jur-Univ, MA of the University of @UofIllinois Urbana-Champaign, Associate Professor @david__simon, PhD, JD, LLM of @Northeastern University and Benjamin R. Roman, MD, MBA, MSHP of @MSKCancerCenter, New York examine the liability and governance challenges posed by AI ambient clinical documentation tools that automatically transcribe and summarize patient-clinician conversations, which are increasingly being adopted by major health systems despite not being regulated as medical devices by the FDA. classicaproject.eu/2026/01/c…