Evidence map›Paper›PMID 40182313›Full record

ArticleBMJ surgery, interventions, & health technologies2025

Patient perspectives on AI-based decision support in surgery.

Sara Ben Hmido, Houssam Abder Rahim, Corrette Ploem, Saskia Haitjema, Olga Damman, Geert Kazemier, Freek Daams

Abstract read
In one paragraph

Article in BMJ surgery, interventions, & health technologies, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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  4. Observational
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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

7 authors.

Sara Ben HmidoDepartment of Surgery, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, The Netherlands.ORCID 0009-0004-9893-8407
Houssam Abder RahimTU Delft, Delft, The Netherlands.ORCID 0009-0001-6598-7067
Corrette PloemQuality of Care, Amsterdam Public Health Research Institute, Amsterdam, North Holland, The Netherlands.
Saskia HaitjemaExperimental Cardiology, UMC Utrecht, Utrecht, The Netherlands.
Olga DammanCancer Centre Amsterdam, Amsterdam, Noord-Holland, The Netherlands.
Geert KazemierDepartment of Surgery, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, The Netherlands.
Freek DaamsDepartment of Surgery, Amsterdam UMC Locatie VUmc, Amsterdam, Noord-Holland, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Predictive machine learning in healthcare, especially in surgical decisions, is advancing swiftly. Yet, literature on patient views regarding predictive machine learning, specifically its use throughout the clinical course, is scarce. Views among patients who underwent colorectal surgery (CRS) on the use of intra-operative predictive machine learning (IPML) by surgeons, particularly those aiming to predict colorectal anastomotic leakage (CAL), were explored in this study. Objective: This study investigated the views of patients who previously underwent CRS on the implementation of IPML models. Domains of interest were perceptions of IPML, perceived role in decision-making and information provided in the clinical encounter. Methods: A qualitative research design was employed, using focus groups and semi-structured interviews with patients who had undergone CRS. Descriptive thematic analysis was used to analyse data and identify prevailing themes and attitudes. The associations in the code tree were established based on a co-occurrence table. The patient sample size was determined using a saturation analysis. Results: A study with n=19 participants across four focus groups and seven interviews found a generally positive perception regarding the use of IPML models in CRS. Participants recognised their potential to enhance surgical decision-making but stressed the surgeon's role as the primary decision-maker, suggesting IPML models act as advisory tools, with surgeons able to override recommendations. Personalised communication and consideration of quality of life were emphasised, highlighting the need for a balanced integration of IPML models to support clinical judgement and the construction of patient preferences. Conclusion: IPML in CRS is well-received by participants, provided that surgeons retain the ability to override model recommendations and document their decisions transparently. Trust in the surgeon remains a key factor in patient acceptance of IPML, reinforcing the need for clear explanations during consultation sessions. Regardless of the use of IPML, tailoring patient communication and addressing the quality-of-life impacts of anastomosis vs stoma are also critical.

Indexed as

Colon and Rectal DevicesExploration StudyHealth Technology

Identifiers

PMID40182313
PMCPMC11966983

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Registered trials

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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.