Evidence map›Paper›PMID 30112675›Full record

ReviewInsights into imaging2018

Artificial intelligence as a medical device in radiology: ethical and regulatory issues in Europe and the United States.

Filippo Pesapane, Caterina Volonté, Marina Codari, Francesco Sardanelli

Open access · goldAbstract readReview
In one paragraph

Review in Insights into imaging, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 177 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
177citing papers in PubMed, 7 pooled it
12.5field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

177 citing papers in PubMed, 7 syntheses or guidelines pooled it, 452 citations in OpenAlex.

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  11. Krenning score enhances integrated biomarker models for survival prediction in PRRT-treated GEP-NET: a retrospective cohort study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026
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117 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 3 institutions in 2 countries.

Filippo PesapanePostgraduation School in Radiodiagnostics, Università degli Studi di Milano, Via Festa del Perdono 7, 20122, Milan, Italy. filippo.pesapane@unimi.it.
Caterina VolontéIndependent Researcher, 3 Greenwich Court, Cavell Street, London, E1 2BS, UK.
Marina CodariUnit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy.
Francesco SardanelliUnit of Radiology, IRCCS Policlinico San Donato, Via Morandi 30, 20097 San Donato Milanese, Milan, Italy.
University of Milan · ITIRCCS Policlinico San Donato · ITOldham Council · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Worldwide interest in artificial intelligence (AI) applications is growing rapidly. In medicine, devices based on machine/deep learning have proliferated, especially for image analysis, presaging new significant challenges for the utility of AI in healthcare. This inevitably raises numerous legal and ethical questions. In this paper we analyse the state of AI regulation in the context of medical device development, and strategies to make AI applications safe and useful in the future. We analyse the legal framework regulating medical devices and data protection in Europe and in the United States, assessing developments that are currently taking place. The European Union (EU) is reforming these fields with new legislation (General Data Protection Regulation [GDPR], Cybersecurity Directive, Medical Devices Regulation, In Vitro Diagnostic Medical Device Regulation). This reform is gradual, but it has now made its first impact, with the GDPR and the Cybersecurity Directive having taken effect in May, 2018. As regards the United States (U.S.), the regulatory scene is predominantly controlled by the Food and Drug Administration. This paper considers issues of accountability, both legal and ethical. The processes of medical device decision-making are largely unpredictable, therefore holding the creators accountable for it clearly raises concerns. There is a lot that can be done in order to regulate AI applications. If this is done properly and timely, the potentiality of AI based technology, in radiology as well as in other fields, will be invaluable. TEACHING POINTS: • AI applications are medical devices supporting detection/diagnosis, work-flow, cost-effectiveness. • Regulations for safety, privacy protection, and ethical use of sensitive information are needed. • EU and U.S. have different approaches for approving and regulating new medical devices. • EU laws consider cyberattacks, incidents (notification and minimisation), and service continuity. • U.S. laws ask for opt-in data processing and use as well as for clear consumer consent.

Indexed as

Artificial intelligenceLegislationPolicyPrivacyRadiology

Identifiers

PMID30112675
PMCPMC6206380
OpenAlexW2887382745

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.