Artificial intelligence: Who is responsible for the diagnosis?
Open accessAbstract
The aim of the paper is to find an answer to the question "Who or what is responsible for the benefits and harms of using artificial intelligence in radiology?" When human beings make decisions, the action itself is normally connected with a direct responsibility by the agent who generated the action. You have an effect on others, and therefore, you are responsible for what you do and what you decide to do. But if you do not do this yourself, but an artificial intelligence system, it becomes difficult and important to be able to ascribe responsibility when something goes wrong. The manuscript addresses the following statements: (1) using AI, the radiologist is responsible for the diagnosis; (2) radiologists must be trained on the use of AI since they are responsible for the actions of machines; (3) radiologists involved in R&D have the responsibility to guide the respect of rules for a trustworthy AI; (4) radiologist responsibility is at risk of validating the unknown (black box); (5) radiologist decision may be biased by the AI automation; (6)risk of a paradox: increasing AI tools to compensate the lack of radiologists; (7) need of informed consent and quality measures. Future legislation must outline the contours of the professional's responsibility, with respect to the provision of the service performed autonomously by AI, balancing the professional's ability to influence and therefore correct the machine, limiting the sphere of autonomy that instead technological evolution would like to recognize to robots.
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Interviewees expressed concerns about the unresolved question of legal responsibility for damage due to false negatives and false positives resulting from an AI-generated diagnosis.
“our interviewees expressed concerns about the unresolved question of legal responsibility for damage occurred due to e.g. false negatives and false positives resulting from an AI-generated diagnosis [34].”
From Implementation of artificial intelligence (AI) applications in radiology: hindering and facilitating factors (2020) · cites this paper as [34]
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- Authors
- Neri, E., Coppola, F., Miele, V., Bibbolino, C., & Grassi, R.
- Affiliations
- University of Pisa (IT)
- IRCCS Azienda Ospedliero-Universitaria di Bologna Policlinico di Sant'Orsola (IT)
- Azienda Ospedaliero-Universitaria Careggi (IT)
- University of Campania "Luigi Vanvitelli" (IT)
- Corresponding author
- Neri, Emanuele
- Venue
- La radiologia medica
- Published
- 2020-01-31
- Volume
- 125
- Issue
- 6
- Pages
- 517-521
- ISSN
- 0033-8362 (ISSN-L)
- 1826-6983
- OpenAlex
- W3003161132
- Cited by
- 240
- References
- 19
- Citations by year
- 2026: 21 · 2025: 31 · 2024: 39 · 2023: 40 · 2022: 51 · 2021: 41 · 2020: 17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- La radiologia medica (publishedVersion)
- PubMed (publishedVersion)
- CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion)
- Florence Research (University of Florence) (submittedVersion)
- Archivio Istituzionale della Ricerca - Università degli Studi della Campania "Luigi Vanvitelli" (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Health Professions › Pharmacy › Medical Malpractice and Liability Issues
- Keywords
- artificial intelligence in radiology, professional responsibility, radiologists, liability, trustworthy AI, black box, algorithmic bias, informed consent, quality metrics, AI regulation, radiology education
- MeSH terms
- Artificial Intelligence, Clinical Competence, Humans, Radiology, Liability, Legal
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