Evidence map›Paper›PMID 41144723›Full record

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

Is orthopaedics entering the age of generative AI?-A narrative review of current applications challenges and future directions.

Felix C Oettl, James A Pruneski, Balint Zsidai, Yinan Yu, Ting Cong, Thomas Tischer, 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 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. Trial
  2. Article
  3. Review
  4. Article
  5. Is orthopaedics entering the age of generative AI?-A narrative review of current applications challenges and future directions.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    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.

Felix C OettlDepartment of Orthopedic Surgery, Balgrist University Hospital, University of Zürich, Zurich, Switzerland.ORCID https://orcid.org/0000-0001-9721-685X
James A PruneskiDepartment of Orthopaedic Surgery, Tripler Army Medical Center, Honolulu, Hawaii, USA.
Balint ZsidaiSahlgrenska Sports Medicine Center, Gothenburg, Sweden.
Yinan YuDepartment of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden.
Ting CongDepartment of Orthopaedic Surgery, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
Thomas TischerDepartment of Orthopaedic Surgery, University Medicine Rostock, Rostock, Germany.
Michael T HirschmannDepartment of Orthopaedic Surgery and Traumatology, Kantonsspital Baselland, 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) in medicine is undergoing a pivotal transformation, evolving from discriminative models that classify data to generative AI systems capable of creating novel content. Generative AI is a type of artificial intelligence that can learn from and mimic large amounts of data to create content such as text, images, music, videos, code, and more. The generative AI paradigm relies on advanced architectures, including large language models (LLMs), which are likely to redefine key processes in the practice of clinical medicine. The imaging- and procedure-heavy specialty of orthopaedic surgery is uniquely positioned to benefit from innovations in spatial reasoning, biomechanical analysis, and procedural planning using generative AI. Key applications are rapidly emerging, like streamlining clinical workflows through automated documentation, the mediation of patient-provider communication and enhanced interpretability of complex medical information. While an exciting field the current evidence base is quite limited. The continued integration of these technologies promises to enhance surgical precision, democratise access to advanced planning, and ultimately improve patient outcomes. However, realising this potential requires overcoming significant challenges related to the 'black box' nature of models, data bias, and evolving regulatory oversight. Rigorous clinical validation through prospective trials will be essential to ensure the safe, effective, and equitable implementation of generative AI in the future of orthopaedic care. LEVEL OF EVIDENCE: Level V.

Indexed as

Clinical Decision-MakingGenerative Artificial IntelligenceOrthopedicsCommunicationDecision Support TechniquesHumansLarge Language ModelsMedical RecordsOrthopedic ProceduresPatient Care PlanningProfessional-Patient Relationsartificial intelligencegenerative AIlarge language modelsorthopaedic surgerysurgical planning

Identifiers

PMID41144723
PMCPMC12747601

What Socratic 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 Socratic graph.