ArticleEClinicalMedicine2025
Shared decision making using digital twins in knee osteoarthritis care: a randomized clinical trial of an AI-enabled decision aid versus education alone on decision quality, physical function, and user experience.
Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04805554 (Incorporating Patient-Reported Outcomes Into Shared Decision Making With Patients With Osteoarthritis of the Hip or Knee), which is not on this map. Cited by 9 papers.
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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.
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.
Incorporating Patient-Reported Outcomes Into Shared Decision Making With Patients With Osteoarthritis of the Hip or Knee
Who cites it
9 citing papers in PubMed.
- From Joint Loading to Osteoarthritis: A Multiscale Review of Knee Mechanobiology and Digital Modelling.International journal of molecular sciences · 2026Review
- Determining the minimal clinically important difference for EQ-5D-5L scores in patients undergoing primary total knee arthroplasty.BMC geriatrics · 2026Article
- Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.Critical care (London, England) · 2026Review
- Digital Twins in Orthopedics and Trauma: Concepts, Emerging Evidence, and Barriers to Clinical Translation.Journal of clinical medicine · 2026Review
- Artificial intelligence in orthopaedics: Enhanced examinations, ambient intelligence and the future of clinical practice.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Review
- Article
- Artificial intelligence and big data for precision regenerative medicine in knee osteoarthritis: endotyping, responder prediction, and clinical translation.Frontiers in bioengineering and biotechnology · 2026Review
- Artificial intelligence in the biomechanics of knee osteoarthritis: biomechanical phenotyping, risk stratification, diagnosis, treatment and clinical translation-a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- From structural recognition to mechanistic forecasting: repositioning artificial intelligence in osteoarthritis prevention.Frontiers in immunology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patient decision aids (DAs) improve decision quality during shared decision-making (SDM) for patients seeking care for knee osteoarthritis (OA). However, few DAs incorporate the 'digital twin' concept where comprehensive data are applied to computational models to generate dynamic virtual simulations and predictions to augment decision-making in real-time. We developed an artificial intelligence-enabled DA (AI-DA) that generated digital twins using patient reported outcome measurements (PROMs) and clinical data to enhance SDM by providing personalized predictions of risks and benefits for patients with knee OA considering total knee arthroplasty (TKA). We assessed the impact of the AI-DA on patient- and process-level outcomes. Methods: We performed a randomized open-label clinical trial at a single, university-affiliated orthopaedic clinic in the USA involving patients with knee OA between February 2021 and November 2022. Patients received a full AI-DA incorporating patient education, preference assessment, and person-specific benefit:risk predictions of TKA (intervention group) or patient education only (control group). Outcomes included the Knee OA Decision Quality Instrument (K-DQI) (primary outcome), CollaboRATE SDM survey, Decision Conflict Scale (DCS), Decision Regret Scale (DRS), and Knee Injury and Osteoarthritis Outcome Score Joint Replacement (KOOS JR) for knee-specific health at 3 and 6 months post-randomization, satisfaction, appointment duration, and TKA rates. This study is registered with ClinicalTrials.gov, NCT04805554. Findings: The analytic sample comprised 101 patients ([mean [SD], 64.9 [10.1] years; 54 [54%] women]) in the intervention group and 100 patients (mean [SD] age 63.4 [8] years; 60 [60%] women) in the control group. The intervention group reported higher decision quality (mean [SD] K-DQI: 84.4 [25.2] versus 71.4 [29.8], Interpretation: AI-DAs provide a more personalized, data-augmented SDM experience that can improve decision quality and longer-term health-related outcomes in patients with knee OA considering TKA. Funding: Agency for Healthcare Research and Quality Grant (R21HS027037).
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Registered trials
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.