Evidence map›Paper›PMID 41225689›Full record

ReviewJournal of anesthesia, analgesia and critical care2025

Top three priorities for artificial intelligence integration into emergency, critical, and perioperative medicine: an interdisciplinary clinical expert consensus.

Beatrice Loriga, Francesco Baglivo, Valentina Bellini, Chiara Adembri, Jonathan Montomoli, Marco Cascella, Giacomo Diedenhofen, Luigi De Angelis, Nicola Gentili, Mattia Altini and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of anesthesia, analgesia and critical care, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

13 authors.

Beatrice LorigaUnit of Anesthesiology, Critical Care and Pain Medicine, Emergency Department, Tuscany South East Local Health Authority, Montepulciano, Siena, Italy. beatriceloriga9@gmail.com.
Francesco BaglivoDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
Valentina BelliniAnesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.
Chiara AdembriDepartment of Health Sciences, Section of Anesthesia, Intensive Care and Pain, University of Florence, Careggi University Hospital, Florence, Italy.
Jonathan MontomoliDepartment of Anesthesia and Intensive Care, Infermi Hospital, Romagna Local Health Authority, Rimini, Italy.
Marco CascellaDepartment of Medicine, University of Salerno, Salerno, Italy.
Giacomo DiedenhofenPostgraduate School of Health Statistics and Biometry, Department of Public Health and Infectious Diseases, Sapienza University of Rome, Rome, Italy.
Luigi De AngelisDepartment of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
Nicola GentiliIRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) Dino Amadori, Meldola, Italy.
Mattia AltiniModena Local Health Authority, Via San Giovanni del Cantone, 23, Modena, Italy.
Antonio PastoriRegional Complex Structure for Development and Innovation of the Emergency Response System, University Hospital of Parma, Parma, Italy.
Raffaella GaggeriIRCCS Istituto Romagnolo Per Lo Studio Dei Tumori (IRST) Dino Amadori, Meldola, Italy.
Elena Giovanna BignamiAnesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is increasingly applied in emergency, critical, and perioperative medicine, yet its implementation remains limited and fragmented. Variability in digital maturity, governance, and clinical readiness continues to challenge large-scale adoption.

methodsA multidisciplinary expert consensus was conducted to identify key priorities for the safe and effective integration of AI in high-acuity settings. The consensus process included an independent literature review, group discussion, and blinded online voting. Priorities that reached at least 70% agreement on a 9-point Likert scale were considered consensual.

resultsThree priorities reached the predefined consensus threshold: 1. Digitalization and sharing of healthcare data (92.3% agreement): Digitalize the Emergency, Critical, and Perioperative Department patient journey by adopting a shared standard structure for electronic medical records that is optimized for data sharing and interoperability. 2. Efficacy and validation of AI models (93.4% agreement): Use only AI models that have demonstrated impact on patient outcomes, decision-making processes, or risk stratification validated through prospective studies or randomized clinical trials. 3. AI education of healthcare professionals (100% agreement): Healthcare professionals must acquire a digital health literacy level appropriate for their specific role, with individuals with leadership and management roles having more in-depth knowledge.

conclusionsThe consensus identifies three strategic priorities to guide the integration of AI in high-acuity settings. Together, they outline a pragmatic roadmap for translating AI potential into safe and clinically meaningful practice.

Indexed as

AIArtificial intelligenceConsensusCritical CareDigitalizationDigital literacyEmergency MedicineHealthcareHigh-acuity careMedicinePerioperative Medicine

Identifiers

PMID41225689
PMCPMC12613334

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.