Evidence map›Paper›PMID 41705361›Full record

ReviewKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2026

Artificial intelligence in orthopaedics: Enhanced examinations, ambient intelligence and the future of clinical practice.

Alexander Bouterse, James A Pruneski, Felix C Oettl, Balint Zsidai, Thomas Tischer, Umile Giuseppe Longo, Romain Seil, Michael T Hirschmann, Kristian Samuelsson

Abstract readReview
In one paragraph

Review in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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.

Alexander BouterseDepartment of Orthopaedic Surgery, Tripler Army Medical Center, Honolulu, Hawaii, USA.ORCID https://orcid.org/0000-0001-5068-6635
James A PruneskiDepartment of Orthopaedic Surgery, Tripler Army Medical Center, Honolulu, Hawaii, USA.
Felix C OettlDepartment of Orthopaedic Surgery, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.
Balint ZsidaiSahlgrenska Sports Medicine Center, Gothenburg, Sweden.
Thomas TischerDepartment of Orthopaedic Surgery, Universitymedicine Rostock, Rostock, Germany.
Umile Giuseppe LongoFondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, Roma, Italy.ORCID https://orcid.org/0000-0003-4063-9821
Romain SeilDepartment of Orthopaedic Surgery, Centre Hospitalier de Luxembourg, Luxembourg City, Luxembourg.
Michael T HirschmannKantonsspital Baselland, University Department of Orthopaedic Surgery and Traumatology, Bruderholz, Switzerland.
Kristian SamuelssonSahlgrenska Sports Medicine Center, Gothenburg, Sweden.ORCID https://orcid.org/0000-0001-5383-3370

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) continues to rapidly transform the practice of medicine, with clinicians increasingly adopting data-driven decision-making aids and diagnostic support tools. Orthopaedic physicians are well poised to harness the capabilities of AI, with an abundance of quantifiable imaging, biomechanical data, and structured clinical parameters lending themselves to algorithmic interpretation and automation. Namely, AI-augmented vision systems may increase the breadth of information readily available to clinicians, whereas smart exam rooms and automated clinical summaries may soon streamline clinical workflows to decrease administrative burden and allow more time for direct patient care. Personalised education materials and visual aids may improve patient understanding and compliance, with the aim of optimising patient outcomes. Generative medical and orthopaedic event models may soon alter decision-making heuristics and improve patient counselling. While the widespread adaptation of AI into clinical practices is not without limitations, physicians will likely come to share an increasingly symbiotic relationship with these platforms throughout their continued evolution. Accordingly, it is imperative that current and future orthopaedic practitioners become well-versed in harnessing the capabilities of AI and continue to identify new avenues for such technologies to benefit clinicians and patients alike. As such, the current manuscript provides a narrative review of the potential future applications of AI within orthopaedic practices by exploring current and developing technologies and detailing how the continued integration of AI-powered systems may serve to revolutionise the delivery of orthopaedic care. LEVEL OF EVIDENCE: Level V.

Indexed as

Artificial IntelligenceOrthopedicsForecastingHumansIntelligent Systemsartificial intelligencedeep learningmachine learningorthopaedics

Identifiers

PMID41705361
PMCPMC13122758

What OpenQuestion holds

Textmetadata
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.