Defining operational safety in clinical artificial intelligence systems.

Young-Tak Kim, Hyunji Kim, Manisha Bahl, Michael H Lev, Ramon Gilberto González, Michael S Gee, Synho Do

Abstract read

In one paragraph

Article in NPJ digital 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

7 authors.

Young-Tak KimDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Hyunji KimDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Manisha BahlDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Michael H LevDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Ramon Gilberto GonzálezDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Michael S GeeDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.

Synho DoDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. sdo@mgh.harvard.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The clinical adoption of artificial intelligence (AI) has focused on enabling automation, but conventional accuracy metrics fail to answer a key question: when is it safe to trust an AI system? We introduce the Safety-Aware Receiver Operating Characteristic (SA-ROC) framework, which defines operational safety as an ability to meet pre-specified reliability levels. The SA-ROC curve delineates a Rule-in and a Rule-out Safe Zone, where autonomous action is permitted, and a Gray Zone, where human review is mandated. To quantify this non-automated workload, we introduce the Gray Zone Area (Γ

Identifiers

PMID41720960

DOI10.1038/s41746-026-02450-7

PMCPMC13050326

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.