ArticleBMC musculoskeletal disorders2026
AI-assisted preoperative planning combined with robotic-assisted total knee arthroplasty vs. conventional surgery: a retrospective controlled study.
Article in BMC musculoskeletal disorders, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The application of artificial intelligence in the design of highly compatible knee prostheses: a systematic review.BMC musculoskeletal disorders · 2026Pooled it
- Integrating Artificial Intelligence into Orthopedic Practice: Modeling Attitudes and Intentions to Use AI.Healthcare (Basel, Switzerland) · 2026Article
- Artificial Intelligence in Arthroplasty: A Comprehensive Structured Critical Review and Descriptive Evidence Map of Validation, Uncertainty, and Workflow Integration.Bioengineering (Basel, Switzerland) · 2026Review
- Artificial Intelligence in Orthopaedics: Clinical Performance, Limitations, and Translational Readiness-A Review.Journal of clinical medicine · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo compare perioperative outcomes and early functional recovery between AI-robotic and conventional total knee arthroplasty (TKA).
methodsWe retrospectively analyzed data from 88 patients who underwent primary unilateral TKA for knee osteoarthritis between April 2024 and December 2024. The AI-robotic group (n = 44) received AI-assisted preoperative planning and robot-assisted TKA, while the traditional group (n = 44) underwent conventional 2D templating and manual TKA. Key metrics included preoperative prosthesis prediction accuracy, intraoperative and postoperative blood loss, osteotomy time, postoperative radiographic alignment, and functional scores.
resultsThe AI-robotic group showed significantly higher prosthesis prediction accuracy (femoral: 79.5% vs. 52.3%, P = 0.023; tibial: 84.1% vs. 61.4%, P = 0.042), shorter osteotomy time (15.24 ± 4.71 vs. 18.43 ± 4.76 min, P = 0.002), reduced intraoperative blood loss (197.41 ± 78.41 vs. 234.35 ± 74.53 mL, P = 0.026), and lower 72-hour total blood loss (1022.96 ± 226.14 vs. 1118.71 ± 193.30 mL, P = 0.036). Postoperative lateral femoral component (LFC) angles were superior in the AI-robotic group (5.87 ± 2.18° vs. 6.91 ± 2.10°, P = 0.025). At 6 weeks, the AI-robotic group had better VAS pain scores (2.27 ± 1.12 vs. 2.84 ± 1.22, P = 0.029) and HSS scores (61.57 ± 4.40 vs. 59.59 ± 3.80, P = 0.027).
conclusionAI-assisted preoperative planning with robotic TKA improves prosthesis sizing accuracy, reduces perioperative blood loss and 72 h total blood loss, and enhances early functional outcomes compared to conventional methods. These findings support AI-robotic integration as a precision solution for TKA, particularly in complex cases.
Indexed as
Identifiers
What OpenQuestion holds
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.