Evidence map›Paper›PMID 41676363›Full record

ReviewEuropean cardiology2026

Ethical and Regulatory Frameworks for Artificial Intelligence in Clinical Research: A European Perspective on the Artificial Intelligence Act for Ethics Committees and Researchers.

Andrea Barucci, Valentina Colcelli, Salvatore De Masi, Martina Falconi, Maria Carmela Leo, Antonio Marzola, Ilaria Romagnuolo, Carolina Sforzi, Roberto Pini

Abstract readReview
In one paragraph

Review in European cardiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
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

7 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
  6. Article
  7. Review
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

9 authors.

Andrea Barucci"Nello Carrara" Institute of Applied Physics, National Research Council of Italy Florence, Italy.ORCID https://orcid.org/0000-0002-3759-7512
Valentina Colcelli"Nello Carrara" Institute of Applied Physics, National Research Council of Italy Florence, Italy.ORCID https://orcid.org/0000-0001-5488-2991
Salvatore De MasiClinical Trial Center, Careggi University Hospital Florence, Italy.ORCID https://orcid.org/0000-0001-5889-9280
Martina FalconiSecretariat of the Paediatric Ethics Committee of the Tuscany Region, Meyer IRCCS University Hospital Florence, Italy.ORCID https://orcid.org/0009-0009-7122-0952
Maria Carmela LeoSecretariat of the Paediatric Ethics Committee of the Tuscany Region, Meyer IRCCS University Hospital Florence, Italy.ORCID https://orcid.org/0000-0003-3591-9060
Antonio MarzolaDepartment of Industrial and Information Engineering and Economics, University of L'Aquila L'Aquila, Italy.ORCID https://orcid.org/0000-0002-7842-4562
Ilaria RomagnuoloClinical Trial Center, Careggi University Hospital Florence, Italy.ORCID https://orcid.org/0009-0006-7646-3863
Carolina SforziSecretariat of the Paediatric Ethics Committee of the Tuscany Region, Meyer IRCCS University Hospital Florence, Italy.
Roberto Pini"Nello Carrara" Institute of Applied Physics, National Research Council of Italy Florence, Italy.ORCID https://orcid.org/0000-0001-8588-9897

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rapid integration of artificial intelligence (AI) into clinical research is transforming the landscape of biomedical innovation, influencing numerous phases of research, with critical ethical and legal implications. Regulation (EU) 2024/1689, commonly referred to as the AI Act and issued in 2024, introduced a new regulatory framework that classifies AI systems used in clinical settings as 'high risk', requiring increased scrutiny by ethics committees and national authorities. This review addresses ethical and regulatory challenges and discusses the application of the AI Act within real-world clinical research. We propose a three-phase lifecycle (training, real-world testing and post-marketing monitoring) to align regulatory burdens with AI maturity. Our recommendations include transparent protocol design with explicit data-use declarations, complementary application of the Medical Device Regulation and the AI Act, with particular attention to the early research phases. This approach provides practical indications for researchers and operational evaluation criteria for ethics committees to ensure patient safety while fostering trustworthy AI deployment in clinical trials.

Indexed as

Artificial intelligenceclinical researchethical principlesethics committeesEuropean Union regulations

Identifiers

PMID41676363
PMCPMC12888102

What Socratic holds

Textmetadata
LicenceCC BY-NC
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 Socratic graph.