Evidence map›Paper›PMID 40515934›Full record

ReviewCurrent reviews in musculoskeletal medicine2025

The Emergence of Applied Artificial Intelligence in the Realm of Value Based Musculoskeletal Care.

Jefferson Hunter, Philippe Dentino, Prakash Jayakumar

Abstract readReview
In one paragraph

Review in Current reviews in musculoskeletal medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
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

3 authors.

Jefferson HunterDepartment of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
Philippe DentinoDepartment of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, TX, USA.
Prakash JayakumarDepartment of Surgery and Perioperative Care, Dell Medical School, University of Texas at Austin, Austin, TX, USA. Prakash.Jayakumar@austin.utexas.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purpose of reviewTo establish the state-of-the-art in applied artificial intelligence (A.I) related to value-based musculoskeletal health care. We performed a literature review of A.I applications in orthopaedics and contextualized these studies based on their alignment with allocative value, technical value, and personal value. We synthesized our findings using descriptive analysis and the Gartner Hype Cycle. RECENT

findings82% of research activity involving A.I and its applications in musculoskeletal care is dominantly focused on technical value, which can be divided into three main sub-groups: imaging and diagnostics, prognostic outcomes and risk factors, and A.I integration within medical devices and care pathways. A.I advancing personal value (18% of studies) is rapidly gaining traction. Relatively few studies (< 1%) focused on allocative value. Emerging applications of A.I in orthopaedics providing 'technical value' include machine learning algorithms for predicting risk and prognosis, diagnostic computer vision algorithms, models advancing surgical robotics; 'personal value' include ambient listening technology, A.I scribes, triage of clinical messages, patient engagement via LLM chatbots, pre-charting applications; and emotional intelligence. Application of these technologies to Gartner's Hype Cycle suggests big data analytics and robotic surgery applications are approaching the plateau of productivity while multiagent / autonomous systems, emotional intelligence, and AI-enabled decision intelligence serve as innovation triggers but are in their infancy. A.I offers the opportunity to improve medical diagnosis, processes, practices and patient experiences and outcomes within musculoskeletal care delivery. While applications enhancing technical value and personal value are being actively researched and rapidly developed in the digital health industry, studies on how A.I provides value through equitable and fair allocation of resources at a population level should be promoted.

Indexed as

Artificial intelligenceHigh value careMachine learningMusculoskeletalOrthopaedic

Identifiers

PMID40515934
PMCPMC12325828

What OpenQuestion holds

Textmetadata
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.