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Barrier check study: why predictive machine learning struggles to reach the operating room.

Sara Ben Hmido^1,2, Chiara Garita^3,2, Frank Bloemers^3,2

¹Department of Surgery, Amsterdam UMC De Boelelaan Site, Amsterdam, Noord-Holland, Netherlands s.benhmido@amsterdamumc.nl.

BMJ Health & Care Informatics

|May 18, 2026

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Implementing machine learning (ML) in surgery faces significant socio-technical barriers, including trust, workflow integration, and data issues. Addressing these requires collaboration and better alignment across technical, clinical, and organizational domains for successful adoption.

Area of Science:

Surgical innovation
Medical artificial intelligence
Health informatics

Background:

Machine learning (ML) offers potential for enhanced surgical decision-making and personalized care.
Clinical implementation of ML tools in surgery is currently limited.

Purpose of the Study:

To identify and categorize key barriers hindering the implementation of ML tools in surgical practice.
To understand the socio-technical challenges impacting ML adoption in surgery.

Main Methods:

Nationwide qualitative survey distributed to healthcare stakeholders involved in ML development and implementation.
Inductive thematic content analysis of open-ended responses detailing perceived barriers, impact, and solutions.

Main Results:

Keywords:

Artificial intelligence Decision Support Systems, Clinical Electronic Health Records

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178 barrier entries identified from 95 participants across 53 organizations.
Key barriers include limited clinician trust, AI literacy, workflow integration challenges, regulatory misalignment, data quality/availability issues, and unclear economic value.
Barriers are interconnected, exacerbating implementation difficulties.

Conclusions:

ML implementation in surgery is a socio-technical challenge, not solely technical.
Misalignment in responsibility, decision authority, and accountability across domains is a primary barrier, especially in time-critical surgical settings.