Evidence map›Paper›PMID 42242875›Full record

Trial reportJournal of nuclear medicine : official publication, Society of Nuclear Medicine2026

How Following Medical Artificial Intelligence Advice Can Mitigate Malpractice Liability: Cross-National Insights from a Randomized Trial.

Alessandro Tacconelli, Jakob Merane, Aileen Nielsen, Kevin Tobia, Björn Hackanson, Alexander Stremitzer

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in Journal of nuclear medicine : official publication, Society of Nuclear Medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

6 authors.

Alessandro TacconelliCenter for Law and Economics, ETH Zurich, Zurich, Switzerland; alessandro.tacconelli@gess.ethz.ch.
Jakob MeraneCenter for Law and Economics, ETH Zurich, Zurich, Switzerland.
Aileen NielsenCenter for Law and Economics, ETH Zurich, Zurich, Switzerland.
Kevin TobiaSchool of Law, Georgetown University Law Center, Washington, DC; and.
Björn HackansonComprehensive Cancer Center Augsburg as Part of the CCC-WERA Consortium, University Medical Center Augsburg and Department of Medicine I, University Faculty of Medicine, Medical Center Freiburg, University of Freiburg, Germany.
Alexander StremitzerCenter for Law and Economics, ETH Zurich, Zurich, Switzerland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) increasingly influences clinical decision-making, yet its recommendations may diverge from standard care. Although malpractice concerns are thought to discourage physicians from following AI advice, experimental evidence from the United States suggests the opposite: lay jurors are more likely to hold physicians liable when they reject AI recommendations. Whether this pattern extends to systems in which court-appointed experts, not lay jurors, determine liability remains unknown.

Indexed as

Artificial IntelligenceLiability, LegalMalpracticeAdultFemaleHumansMaleMiddle AgedPhysiciansartificial intelligencelegal systemsliabilityprecision medicine

Identifiers

PMID42242875
PMCPMC13544199

What OpenQuestion holds

Textmetadata
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.