Evidence map›Paper›PMID 41464276›Full record

ReviewHealthcare (Basel, Switzerland)2025

Unveiling the Algorithm: The Role of Explainable Artificial Intelligence in Modern Surgery.

Sara Lopes, Miguel Mascarenhas, João Fonseca, Maria Gabriela O Fernandes, Adelino F Leite-Moreira

Abstract readReview
In one paragraph

Review in Healthcare (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

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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

5 authors.

Sara LopesPortuguese Institute of Oncology of Porto, 4200-072 Porto, Portugal.ORCID 0000-0001-9166-9508
Miguel MascarenhasFaculty of Medicine, University of Porto, 4200-437 Porto, Portugal.ORCID 0000-0002-0340-0830
João FonsecaFaculty of Medicine, University of Porto, 4200-437 Porto, Portugal.ORCID 0000-0002-0887-8796
Maria Gabriela O FernandesFaculty of Medicine, University of Porto, 4200-437 Porto, Portugal.ORCID 0000-0002-3549-0770
Adelino F Leite-MoreiraFaculty of Medicine, University of Porto, 4200-437 Porto, Portugal.ORCID 0000-0001-7808-3596

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial Intelligence (AI) is rapidly transforming surgical care by enabling more accurate diagnosis and risk prediction, personalized decision-making, real-time intraoperative support, and postoperative management. Ongoing trends such as multi-task learning, real-time integration, and clinician-centered design suggest AI is maturing into a safe, pragmatic asset in surgical care. Yet, significant challenges, such as the complexity and opacity of many AI models (particularly deep learning), transparency, bias, data sharing, and equitable deployment, must be surpassed to achieve clinical trust, ethical use, and regulatory approval of AI algorithms in healthcare. Explainable Artificial Intelligence (XAI) is an emerging field that plays an important role in bridging the gap between algorithmic power and clinical use as surgery becomes increasingly data-driven. The authors reviewed current applications of XAI in the context of surgery-preoperative risk assessment, surgical planning, intraoperative guidance, and postoperative monitoring-and highlighted the absence of these mechanisms in Generative AI (e.g., ChatGPT). XAI will allow surgeons to interpret, validate, and trust AI tools. XAI applied in surgery is not a luxury: it must be a prerequisite for responsible innovation. Model bias, overfitting, and user interface design are key challenges that need to be overcome and will be explored in this review to achieve the integration of XAI into the surgical field. Unveiling the algorithm is the first step toward a safe, accountable, transparent, and human-centered surgical AI.

Indexed as

artificial intelligencebioethicsChatGPTexplainable artificial intelligencegenerative AIsurgeryXAI

Identifiers

PMID41464276
PMCPMC12732924

What OpenQuestion holds

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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.