These issues are central to the volume \u201cIs AI the Perfect Doctor?\u201d, which provides a comparative analysis of European and U.S. regulatory frameworks and offers a comprehensive reflection on the future of AI in medicine. For TrendSanit\u00e0, the book\u2019s authors – Oreste Pollicino (Full Professor of Constitutional Law and AI Regulation, Bocconi University), Francesca Aurora Sacchi (specialized in science policy, AI regulation, and bioethics), and Noemi Conditi (Lawyer, Stefanelli & Stefanelli Law Firm)<\/strong> – analyze AI\u2019s potential, risks, and the legal aspects of the different international approaches.<\/p>\n\n\n\n In healthcare, the European risk-based regulatory approach represents a significant paradigm shift, though not without practical challenges. The AI Act rightly identifies healthcare as a high-impact domain for fundamental rights, but its effectiveness will depend on translating high-level principles into actionable tools for clinical and organizational practice.<\/p>\n\n\n\n As Oreste Pollicino<\/strong> points out, \u201cIn healthcare, the most complex risks to regulate involve algorithmic opacity, data bias, and the secondary use of sensitive health information<\/strong>.\u201d These are structural elements of AI systems that cannot be addressed solely through formal compliance obligations\u2014they directly affect clinical decision quality and care equity.<\/p>\n\n\n\n The adoption of AI requires clear governance, high-quality data, and workforce training<\/p>\n\n\n\n According to Pollicino, \u201cThe AI Act\u2019s risk-based framework is an important step\u201d, but it is insufficient if it remains abstract. \u201cDetailed sector-specific guidelines are needed to translate high-level principles into operational requirements for developers and healthcare organizations<\/strong>,\u201d guiding algorithm design, validation, and use in everyday care settings.<\/p>\n\n\n\n Data governance is another critical node, representing AI\u2019s real infrastructure. \u201cStrengthening data governance is also essential<\/strong>: we need systems that protect privacy and security while enabling responsible secondary use of data to accelerate research and innovation,\u201d Pollicino emphasizes. In this perspective, protecting fundamental rights and fostering technological development are not conflicting objectives\u2014they must be integrated through advanced, responsible governance models.<\/p>\n\n\n\n Alongside regulatory challenges, the book examines the evolution of AI tools already available or in advanced testing phases. The emerging picture is that of a rich, rapidly transforming ecosystem in which some applications are beginning to yield significant evidence.<\/p>\n\n\n\n Pollicino notes that \u201cMany tools are showing strong potential<\/strong>,\u201d citing \u201cimaging algorithms for early diagnosis, predictive models for therapy personalization, clinical decision-support systems, and platforms that analyze real-world data.<\/strong>\u201d Complementing these are natural language processing solutions, which \u201care also advancing rapidly and improving documentation and research workflows.\u201d<\/p>\n\n\n\n Artificial intelligence can improve medicine only if guided by strong ethical principles<\/p>\n\n\n\n The real leap, however, concerns the ability to integrate these technologies into care pathways. \u201cThe next step is integrating these tools into care pathways<\/strong>,\u201d Pollicino emphasizes, highlighting the need to pair technological innovation with structured investment in skills. In this sense, \u201cdigital upskilling programs for healthcare professionals\u2014as foreseen by the AI Act\u2014are essential\u201d \u201cto ensure safe, transparent, and responsible clinical use.\u201d<\/p>\n\n\n\n Digital transformation in healthcare does not develop within national borders. Data circulation, research, and algorithm development make comparison between regulatory models inevitable. In this scenario, dialogue between the EU and the U.S. is strategic<\/strong>.<\/p>\n\n\n\n Francesca Aurora Sacchi<\/strong>, also board member of the Italian Society for Artificial Intelligence in Medicine (SIIAM), emphasizes that \u201cArtificial intelligence can transform healthcare only if grounded in strong ethical principles, but we must also strike a balance that allows innovation to flourish.\u201d Differences between the two models are evident: \u201cIn Europe, GDPR and the AI Act emphasize transparency and patient safety, while the U.S. tends to adopt a more flexible, market-driven approach.<\/strong>\u201d<\/p>\n\n\n\n The challenge is to avoid fragmentation that penalizes both innovation and citizen protection<\/strong>. \u201cToday\u2019s priority is creating a common ground between these models,\u201d Sacchi observes, \u201cpromoting global standards that ensure quality, algorithmic traceability, and interoperability.\u201d<\/p>\n\n\n\n Public trust in the National Health Service depends on the transparency, usability, and safety of AI systems<\/p>\n\n\n\n A crucial element of this convergence concerns access to health data. \u201cWithout large, representative datasets, even the most sophisticated algorithms cannot deliver clinical value.<\/strong>\u201d Therefore, \u201cbuilding a secure, privacy-preserving, and well-governed data-sharing ecosystem is essential to develop trustworthy AI solutions that are genuinely useful for clinical practice.\u201d<\/p>\n\n\n\n The period of research for the book coincided with an unprecedented acceleration in AI technology. \u201cDuring the writing process, we witnessed extremely rapid evolution in AI tools – both in analytic capabilities and accessibility<\/strong>,\u201d Sacchi recounts. Solutions initially perceived as experimental quickly became \u201cintegral to clinical and research workflows.\u201d<\/p>\n\n\n\n Integrating digital twins into clinical pathways reduces development timelines and risks<\/p>\n\n\n\n Among the most notable innovations are digital twins<\/strong>: \u201cDynamic virtual replicas of patients that integrate clinical, physiological, and genomic data to simulate disease progression or treatment response.\u201d Their potential also extends to R&D: \u201cDigital twins make it possible to model interventions, optimize dosing, shorten development timelines, and potentially reduce reliance on early-stage traditional trials.\u201d<\/p>\n\n\n\n This evolution also brings new responsibilities. \u201cWe also observed growing attention to data quality and algorithmic explainability,<\/strong>\u201d marking a shift to a \u201cmore mature, but also more complex\u201d landscape that demands \u201ccontinuous regulatory and educational updates.\u201d<\/p>\n\n\n\n One of the most delicate areas is AI integrated into medical devices, where innovation directly intersects with patient safety. Noemi Conditi <\/strong>notes that \u201cIntegrating AI into medical devices requires simultaneous compliance with Regulation 2024\/1689 (AI Act) and Regulation 2017\/745 (MDR)<\/strong>.\u201d<\/p>\n\n\n\n Medical devices with AI require strict regulatory compliance and continuous attention to safety<\/p>\n\n\n This regulatory combination \u201caims to ensure consistent levels of safety, performance, and quality throughout the product lifecycle,\u201d but also \u201cincreases obligations for all actors in the supply chain<\/strong>,\u201d with \u201csignificant administrative and resource implications.\u201d<\/p>\n\n\n\n On liability, \u201cThe liability regime has not undergone a radical transformation,\u201d Conditi explains, \u201cas the proposal for a dedicated directive was ultimately abandoned; therefore, standard rules continue to apply.\u201d<\/p>\n\n\n\n AI adoption in daily practice is growing but remains uneven<\/strong>. \u201cThe share of healthcare professionals using AI tools is steadily increasing, with almost geometric growth,\u201d Conditi observes. However, many digital solutions \u201ccurrently support relatively simple tasks,\u201d while \u201cthe adoption of more complex systems remains challenging.\u201d<\/p>\n\n\n\n Data protection and algorithmic transparency are essential for patient acceptance<\/p>\n\n\n\n The potential impact on the physician\u2013patient relationship is significant. \u201cIn principle, AI can significantly influence the physician\u2013patient relationship<\/strong>.\u201d On one hand, patients \u201ccan benefit from improved care quality,\u201d but on the other hand, \u201censuring usability, inclusiveness, and transparency is essential to maintain trust in the healthcare system as a whole.\u201d<\/p>\n\n\n\n Artificial intelligence is not the \u201cperfect doctor,\u201d but a powerful tool that can improve the quality, efficiency, and equity of healthcare systems. As highlighted by the authors of Is AI the Perfect Doctor?, its impact will depend on the ability to integrate technological innovation, effective regulation, and professional training.<\/p>\n\n\n\n Today\u2019s challenge is no longer whether to adopt AI in healthcare, but how to do so responsibly, transparently, and sustainably<\/strong>, preserving citizens\u2019 trust and reinforcing the public value of the healthcare system.<\/p>\n","protected":false},"excerpt":{"rendered":" From decision support to predictive diagnostics, artificial intelligence is opening unprecedented scenarios in medicine. Yet, adopting these technologies requires clear governance, high-quality data, and training for healthcare professionals. For TrendSanit\u00e0, the authors of the book \u201cIs AI the Perfect Doctor?\u201d analyze AI\u2019s potential, risks, and legal aspects<\/p>\n","protected":false},"author":91,"featured_media":32353,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[544],"tags":[1010,549,1012,1462],"rubrica":[],"speciale":[],"class_list":{"0":"post-55239","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-across-europe-en","8":"tag-ai-en","9":"tag-europe-en","10":"tag-legal","11":"tag-usa"},"yoast_head":"\nAI Act and healthcare: algorithmic opacity, data, and governance<\/h2>\n\n\n
![\"\"](\"https:\/\/trendsanita.it\/wp-content\/uploads\/2025\/12\/Oreste-Pollicino.jpg\")
From technological innovation to integration into care pathways<\/h2>\n\n\n\n
Ethics, data, and regulatory convergence: bridging Europe and the U.S.<\/h2>\n\n\n
![\"\"](\"https:\/\/trendsanita.it\/wp-content\/uploads\/2025\/12\/Francesca-Aurosa-Sacchi.jpg\")
From research to practice: emerging tools, digital twins, and new complexities<\/h2>\n\n\n\n
AI in medical devices: safety, obligations, and liability<\/h2>\n\n\n\n
![\"\"](\"https:\/\/trendsanita.it\/wp-content\/uploads\/2025\/12\/Noemi-Conditi.jpg\")
Clinical adoption and physician\u2013patient relationship: a matter of trust<\/h2>\n\n\n\n
A transformation to govern, not endure<\/h2>\n\n\n\n