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Responsible AI practice and AI education are central to AI implementation: a rapid review for all medical imaging professionals in Europe

Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and eff...

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Detalles Bibliográficos
Autores principales: Walsh, Gemma, Stogiannos, Nikolaos, van de Venter, Riaan, Rainey, Clare, Tam, Winnie, McFadden, Sonyia, McNulty, Jonathan P, Mekis, Nejc, Lewis, Sarah, O'Regan, Tracy, Kumar, Amrita, Huisman, Merel, Bisdas, Sotirios, Kotter, Elmar, Pinto dos Santos, Daniel, Sá dos Reis, Cláudia, van Ooijen, Peter, Brady, Adrian P, Malamateniou, Christina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The British Institute of Radiology. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10636340/
https://www.ncbi.nlm.nih.gov/pubmed/37953871
http://dx.doi.org/10.1259/bjro.20230033
Descripción
Sumario:Artificial intelligence (AI) has transitioned from the lab to the bedside, and it is increasingly being used in healthcare. Radiology and Radiography are on the frontline of AI implementation, because of the use of big data for medical imaging and diagnosis for different patient groups. Safe and effective AI implementation requires that responsible and ethical practices are upheld by all key stakeholders, that there is harmonious collaboration between different professional groups, and customised educational provisions for all involved. This paper outlines key principles of ethical and responsible AI, highlights recent educational initiatives for clinical practitioners and discusses the synergies between all medical imaging professionals as they prepare for the digital future in Europe. Responsible and ethical AI is vital to enhance a culture of safety and trust for healthcare professionals and patients alike. Educational and training provisions for medical imaging professionals on AI is central to the understanding of basic AI principles and applications and there are many offerings currently in Europe. Education can facilitate the transparency of AI tools, but more formalised, university-led training is needed to ensure the academic scrutiny, appropriate pedagogy, multidisciplinarity and customisation to the learners’ unique needs are being adhered to. As radiographers and radiologists work together and with other professionals to understand and harness the benefits of AI in medical imaging, it becomes clear that they are faced with the same challenges and that they have the same needs. The digital future belongs to multidisciplinary teams that work seamlessly together, learn together, manage risk collectively and collaborate for the benefit of the patients they serve.