SynthesisOrthopadie (Heidelberg, Germany)2025
[AI in instrumental gait analysis : Challenges and solution approaches].
Synthesis in Orthopadie (Heidelberg, Germany), 2025. 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.
- [Orthopaedics of the future : AI meets motion analysis: opportunities and risks].Orthopadie (Heidelberg, Germany) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) is considered a key technology for alleviating the burden on the healthcare system. For instrumental gait analysis, AI-based evaluations promise a direct and intuitive access to clinically relevant information in orthopaedics and trauma surgery, avoiding the challenging and time-consuming manual evaluations of large amounts of patient data.
objectiveThe objective of this work is to investigate the specific challenges and limitations of using AI for clinical evaluation of gait analysis data and to propose effective solutions to address these limitations.
methodThis work combines a systematic literature review on AI in gait analysis with practical experiences from applications of AI in the authors' own published research projects.
resultsSix key challenges have been identified. While AI methods work best when extensive training data, a limited number of influencing factors, and a clearly defined target variable are available, instrumental gait analysis is characterised by opposite conditions (little training data, multiple influencing factors, and fuzzy target variables). To address these contradicting characteristics, a catalogue of possible solution approaches focusing on integrating clinical expert knowledge into AI development and operation is outlined.
conclusionIt is shown that AI offers significant potential for improving the efficiency and quality of gait data exploitation. However, current AI approaches from other fields are only partially transferable to gait analysis due to insufficient fitting. By addressing the specific challenges for AI in gait analysis, it can be expected that specialized procedures and best practices can be developed, which will boost AI assistance in IGA clinical evaluation.
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.