Evidence map›Paper›PMID 41417443›Full record

ArticleInternal and emergency medicine2026

Artificial intelligence in medicine: a position paper by the Italian Society of Internal Medicine.

Clara Balsano, Federico Cabitza, Sebastiano Cicco, Marco Gori, Donato Malerba, Marco Montagna, Roberto Tarquini, Angelo Vacca, Working Group on Artificial Intelligence, Digital Therapies of the Italian Society of Internal Medicine (SIMI)

Abstract read
In one paragraph

Article in Internal and emergency medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. 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.

Clara BalsanoGeriatric Unit, School of Emergency-Urgency Medicine, Department of Life, Health and Environmental Sciences-MESVA, University of L'Aquila, L'Aquila, Italy.
Federico CabitzaDepartment of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy.
Sebastiano CiccoDepartment of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), Unit of Internal Medicine "Guido Baccelli", University of Bari Aldo Moro, Bari, Italy. sebastiano.cicco@uniba.it.
Marco GoriDepartment of Information Engineering and Mathematics, University of Siena, Siena, Italy.
Donato MalerbaDepartment of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), Telemedicine Research Center, University of Bari Aldo Moro, Bari, Italy.
Marco MontagnaSchool of Medicine, Vita-Salute San Raffaele University, Milan, Italy. montagna.marco@hsr.it.ORCID 0000-0002-0907-7640
Roberto TarquiniSOC Medicina Interna, USL Toscana Centro, Empoli, Italy.
Angelo VaccaDepartment of Precision and Regenerative Medicine and Ionian Area (DiMePRe-J), Unit of Internal Medicine "Guido Baccelli", University of Bari Aldo Moro, Bari, Italy.
Working Group on Artificial Intelligence, Digital Therapies of the Italian Society of Internal Medicine (SIMI)

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) represents an innovative technological support for clinical practice. The Italian Society of Internal Medicine (SIMI) emphasizes the need for clear guidance on the use of AI in medicine, recognizing that knowledge in this field is continuously evolving. This position paper presents a comprehensive vision for the responsible integration of AI into clinical practice. AI should serve as a support tool-not a replacement-for clinicians. It has the potential to improve diagnostic accuracy, reduce administrative workload, and strengthen the physician-patient relationship. In the light of these characteristics, SIMI advocates for transparency, data privacy, equity, and sustainability in the development and implementation of AI systems. SIMI also highlights several ethical, legal, and methodological challenges that must be addressed, including algorithmic bias, environmental impact, and disparities in access. Ultimately, SIMI envisions a future in which AI augments human expertise, enabling more efficient, personalized, and compassionate care. SIMI calls for active clinician participation in the co-design and validation of AI tools to ensure alignment with real-world clinical needs. Key recommendations include the preferential use of certified AI systems, the integration of AI education into medical training, and continuous monitoring after deployment.

Indexed as

Artificial IntelligenceInternal MedicineHumansItalySocieties, MedicalAdvocacyChecklistsExplainable AIKey opinion leadersMachine learningRecommendations

Identifiers

PMID41417443
PMCPMC12948900

What OpenQuestion holds

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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.