ReviewJournal of orthopaedic surgery and research2026
Artificial intelligence in postural management: a critical review of detection, correction, and clinical applicability.
Review in Journal of orthopaedic surgery and research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPoor posture and related musculoskeletal conditions represent a growing global health concern. Conventional postural assessment methods are often subjective, intermittent, and insufficient for accurate, continuous monitoring. Advances in artificial intelligence (AI), particularly in computer vision and human pose estimation (HPE), have introduced new possibilities for objective and real-time postural analysis. MAIN BODY: This critical review synthesizes and evaluates current developments in AI technologies for postural management. The review draws on recent literature from computer science, bioengineering, and clinical research, focusing on studies from the past decade that explore the use of AI and HPE in the detection, monitoring, and correction of human posture. AI-based HPE models demonstrate high precision in identifying anatomical landmarks and quantifying postural parameters, offering a robust alternative to traditional assessment methods. Applications are expanding beyond laboratory environments to practical contexts such as ergonomic risk evaluation and sports performance analysis. In addition, AI-driven systems that deliver real-time feedback and support tele-rehabilitation are enhancing user engagement and enabling personalized interventions. Despite these advancements, the field faces several challenges. Evidence from large-scale clinical trials remains limited, and the generalizability of existing models across diverse populations and real-world conditions is uncertain. Concerns related to usability, data privacy, and integration within healthcare systems also pose significant barriers to clinical translation.
conclusionAI holds considerable potential to transform postural management through continuous, objective, and accessible assessment and intervention. To fully realize this potential, future work must extend beyond technical innovation to include rigorous clinical validation, user-centered design, and the establishment of ethical and regulatory frameworks that ensure safe, effective, and equitable implementation.
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What Socratic 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 Socratic graph.