Evidence map›Paper›PMID 41753994›Full record

ReviewHealthcare (Basel, Switzerland)2026

The Role of Artificial Intelligence in Shaping the Doctor-Patient Relationship: A Narrative Review.

Emanuele Maria Merlo, Giorgio Sparacino, Orlando Silvestro, Maria Laura Giacobello, Alessandro Meduri, Marco Casciaro, Sebastiano Gangemi, Gabriella Martino

Abstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. 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

8 authors.

Emanuele Maria MerloDepartment of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98124 Messina, Italy.ORCID 0000-0001-6041-9186
Giorgio SparacinoCourse Degree in Medicine and Surgery, Department of Adult and Childhood Human Pathology "Gaetano Barresi", University of Messina, 98124 Messina, Italy.ORCID 0009-0004-9236-0392
Orlando SilvestroDepartment of Health Sciences, University Magna Graecia of Catanzaro, 88100 Catanzaro, Italy.ORCID 0009-0002-0966-5642
Maria Laura GiacobelloDepartment of Ancient and Modern Civilizations, University of Messina, 98124 Messina, Italy.
Alessandro MeduriOphthalmology Clinic, Department of Biomedical and Dental Sciences and Morphofunctional Imaging, University of Messina, 98124 Messina, Italy.ORCID 0000-0001-7546-9178
Marco CasciaroDepartment of Clinical and Experimental Medicine, University of Messina, 98124 Messina, Italy.ORCID 0000-0002-5436-1501
Sebastiano GangemiDepartment of Clinical and Experimental Medicine, University of Messina, 98124 Messina, Italy.
Gabriella MartinoDepartment of Clinical and Experimental Medicine, University of Messina, 98124 Messina, Italy.ORCID 0000-0001-9488-2021

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The doctor-patient relationship is a central factor in healthcare delivery. Artificial Intelligence (AI) represents an emerging technological frontier whose implications remain to be fully clarified. Evidence-based studies provide reliable analyses of effects and offer a deeper understanding of both limits and benefits. This narrative review aimed to explore the role of AI in modern clinical practice, with particular reference to its effects on the doctor-patient relationship. Scopus and Web of Science databases were searched between 1 and 10 December 2025 to identify suitable studies. Inclusion criteria comprised English-language articles published in the last 10 years, with a direct focus on the doctor-patient relationship and exclusively employing empirical research designs. A total of 21 studies published between 2021 and 2025 were identified as eligible. The most common AI applications were conceptual systems discussed at a perceptual level (thirteen studies), followed by simulated AI decision-making scenarios (two studies). Implemented AI applications were less frequent and mainly included AI-based clinical decision support systems, administrative and documentation-focused tools, and a small number of conversational or relational AI applications (six studies in total). These studies focused on patients, healthcare professionals, and medical students preparing for future clinical roles. Results highlighted generally positive patient attitudes toward AI, often mediated by educational level, technological familiarity, and risk awareness. Among healthcare professionals, positive attitudes also emerged, although concerns regarding epistemic and professional values were noted. Greater involvement of clinicians in its development was consistently recommended. Findings from academic samples aligned with those of patients and clinicians, showing that integrating AI with traditional clinical practices was consistently preferred. Empathy, compassion, effective communication, accuracy, ethics, and trust were highlighted as fundamental values essential for mitigating risks. These elements are fundamental to the effective implementation of technologies aimed at improving clinical practice, while an integrative perspective is needed to safeguard the doctor-patient relationship. Overall, the use of AI in medical practice emerged as promising. Further studies should strengthen the empirical basis of the field to support an evidence-based approach to AI integration in healthcare.

Indexed as

AIartificial intelligenceclinical psychologydoctor–patient relationshipevidencehuman–computer interactioninternal medicine

Identifiers

PMID41753994
PMCPMC12941156

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.