| Autor: |
Contaldo MT; Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy., Pasceri G; Information Society Law Center, Department 'Cesare Beccaria', University of Milan, 20122 Milan, Italy., Vignati G; Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy., Bracchi L; Cerba Healthcare Italia, 20139 Milan, Italy., Triggiani S; Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy., Carrafiello G; Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy.; Radiology and Inverventional Radiology Department, Fondazione IRCCS Cà Granda, Policlinico di Milano Ospedale Maggiore, 20122 Milan, Italy. |
| Abstrakt: |
The application of Artificial Intelligence (AI) facilitates medical activities by automating routine tasks for healthcare professionals. AI augments but does not replace human decision-making, thus complicating the process of addressing legal responsibility. This study investigates the legal challenges associated with the medical use of AI in radiology, analyzing relevant case law and literature, with a specific focus on professional liability attribution. In the case of an error, the primary responsibility remains with the physician, with possible shared liability with developers according to the framework of medical device liability. If there is disagreement with the AI's findings, the physician must not only pursue but also justify their choices according to prevailing professional standards. Regulations must balance the autonomy of AI systems with the need for responsible clinical practice. Effective use of AI-generated evaluations requires knowledge of data dynamics and metrics like sensitivity and specificity, even without a clear understanding of the underlying algorithms: the opacity (referred to as the "black box phenomenon") of certain systems raises concerns about the interpretation and actual usability of results for both physicians and patients. AI is redefining healthcare, underscoring the imperative for robust liability frameworks, meticulous updates of systems, and transparent patient communication regarding AI involvement. |
