Evidence map›Paper›PMID 42077270›Full record

ReviewRegenerative therapy2026

Current and emerging therapies for knee osteoarthritis: From conventional approaches to machine learning and stem cell innovations.

Arthur W Cowman, Jordan N Tang, Jeffrey Deng, Ammar Abu-Halawa, Haiyue Jin, Christopher Z Liu, Ryan Hoang, Walter Cowman, Michael S Kim, Sultan Baz and 5 more

Abstract readReview
In one paragraph

Review in Regenerative therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

15 authors.

Arthur W CowmanUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Jordan N TangUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Jeffrey DengGeisel School of Medicine, Dartmouth College, Hanover, NH, 03755, USA.
Ammar Abu-HalawaUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Haiyue JinUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Christopher Z LiuUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Ryan HoangUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Walter CowmanUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Michael S KimUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Sultan BazUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
MacKinnly T KnoerzerUniversity of California Irvine School of Medicine, Irvine, CA, 92617, USA.
Amir-Ala MahmoudDepartment of Plastic Surgery, University of California Davis, Sacramento, CA, 95817, USA.
Amir-Ali MahmoudDepartment of Radiology, University of California Davis, Sacramento, CA, 95817, USA.
Miles D MelamedUniversity of California San Diego School of Medicine, San Diego, CA, 92093, USA.
Hong-Wen DengTulane Center for Biomedical Informatics and Genomics, Deming Department of Medicine, School of Medicine, Tulane University, New Orleans, LA 70112, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Osteoarthritis is characterized by cartilage degradation and joint distortion. The most prevalent version of osteoarthritis, knee osteoarthritis (KOA), affects millions, acting as a leading cause of disability. With the average life expectancy continuing to grow, the global burden of KOA is expected to increase. KOA involves chronic pain that limits function and adversely impacts patients' quality of life. Long-term management is costly and burdensome, often requiring continuous treatment that only delays disease progression unless patients receive a total knee arthroplasty. Recently, traditional treatment modalities for KOA have been augmented by advancements in machine learning (ML), which enable automated disease grading, predictive modeling for early diagnosis, and personalized treatment strategies, alongside growing interest in stem cell-based therapies. With the goal of developing less invasive treatments that slow or reverse KOA progression, investigation into these novel therapies have increased. Stem cell-based therapies offer the potential to modulate inflammation, reduce pain, and preserve cartilage integrity, addressing key limitations of existing treatments. Mesenchymal stem cells have shown promising preclinical and clinical results, demonstrating improvements in pain, function, and cartilage-related outcomes through immunomodulatory and paracrine mechanisms. This narrative review synthesizes current evidence on conventional and emerging therapies for KOA, with a dual focus on ML-driven clinical applications and stem cell-based regenerative strategies, and discusses their potential roles in improving diagnosis, treatment selection, and patient outcomes. While further research is needed to understand the full potential of stem cells for treating KOA, these promising findings make it a useful topic for current and future clinical applications.

Indexed as

Cartilage regenerationKnee osteoarthritisMachine learningMesenchymal stem cellsTotal knee arthroplasty

Identifiers

PMID42077270
PMCPMC13133973

What OpenQuestion holds

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LicenceCC BY
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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.