Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial.
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SUBMITTER: Luna A
PROVIDER: S-EPMC8437936 | biostudies-literature | 2021 Sep
REPOSITORIES: biostudies-literature
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Artificial intelligence application versus physical therapist for squat evaluation: a randomized controlled trial.
Luna Alessandro A Casertano Lorenzo L Timmerberg Jean J O'Neil Margaret M Machowsky Jason J Leu Cheng-Shiun CS Lin Jianghui J Fang Zhiqian Z Douglas William W Agrawal Sunil S
Scientific reports 20210913 1
Artificial intelligence technology is becoming more prevalent in health care as a tool to improve practice patterns and patient outcomes. This study assessed ability of a commercialized artificial intelligence (AI) mobile application to identify and improve bodyweight squat form in adult participants when compared to a physical therapist (PT). Participants randomized to AI group (n = 15) performed 3 squat sets: 10 unassisted control squats, 10 squats with performance feedback from AI, and 10 add ...[more]
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Project description:BackgroundThe definitive treatment for knee osteoarthritis is a total knee replacement, which results in a clinically meaningful improvement in pain and physical function. However, evidence suggests that physical activity (PA) remains unchanged after total knee replacement (TKR).ObjectiveThe objective of this study is to investigate the efficacy, fidelity, and safety of a physical therapist-administered PA intervention for people after TKR.DesignThis study will be a randomized controlled trial.SettingThe setting is an outpatient physical therapy clinic.ParticipantsThe participants are 125 individuals who are over the age of 45 and are seeking outpatient physical therapy following a unilateral TKR.InterventionIn addition to standardized physical therapy after TKR, the intervention group will receive, during physical therapy, a weekly PA intervention that includes a wearable activity tracking device, individualized step goals, and face-to-face feedback provided by a physical therapist.ControlThe control group will receive standardized physical therapy alone after TKR.MeasurementsThe efficacy of the intervention will be measured as minutes per week spent in moderate to vigorous PA at enrollment, at discharge, and at 6 months and 12 months after discharge from physical therapy. The fidelity and safety of the intervention will be assessed throughout the study.LimitationsParticipants will not be masked, PA data will be collected after randomization, and the trial will be conducted at a single site.ConclusionsThe goal of this randomized controlled trial is to increase PA after TKR. A protocol for investigating the efficacy, fidelity, and safety of a physical therapist-administered PA intervention for people after TKR is presented. The findings will be used to support a large multisite clinical trial to test the effectiveness, implementation, and cost of this intervention.
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Project description:BackgroundThe number of artificial intelligence (AI) studies in medicine has exponentially increased recently. However, there is no clear quantification of the clinical benefits of implementing AI-assisted tools in patient care.ObjectiveThis study aims to systematically review all published randomized controlled trials (RCTs) of AI-assisted tools to characterize their performance in clinical practice.MethodsCINAHL, Cochrane Central, Embase, MEDLINE, and PubMed were searched to identify relevant RCTs published up to July 2021 and comparing the performance of AI-assisted tools with conventional clinical management without AI assistance. We evaluated the primary end points of each study to determine their clinical relevance. This systematic review was conducted following the updated PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines.ResultsAmong the 11,839 articles retrieved, only 39 (0.33%) RCTs were included. These RCTs were conducted in an approximately equal distribution from North America, Europe, and Asia. AI-assisted tools were implemented in 13 different clinical specialties. Most RCTs were published in the field of gastroenterology, with 15 studies on AI-assisted endoscopy. Most RCTs studied biosignal-based AI-assisted tools, and a minority of RCTs studied AI-assisted tools drawn from clinical data. In 77% (30/39) of the RCTs, AI-assisted interventions outperformed usual clinical care, and clinically relevant outcomes improved with AI-assisted intervention in 70% (21/30) of the studies. Small sample size and single-center design limited the generalizability of these studies.ConclusionsThere is growing evidence supporting the implementation of AI-assisted tools in daily clinical practice; however, the number of available RCTs is limited and heterogeneous. More RCTs of AI-assisted tools integrated into clinical practice are needed to advance the role of AI in medicine.Trial registrationPROSPERO CRD42021286539; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=286539.
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Project description:Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
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Project description:BackgroundLarge language models, exemplified by ChatGPT, have reached a level of sophistication that makes distinguishing between human- and artificial intelligence (AI)-generated texts increasingly challenging. This has raised concerns in academia, particularly in medicine, where the accuracy and authenticity of written work are paramount.ObjectiveThis semirandomized controlled study aims to examine the ability of 2 blinded expert groups with different levels of content familiarity-medical professionals and humanities scholars with expertise in textual analysis-to distinguish between longer scientific texts in German written by medical students and those generated by ChatGPT. Additionally, the study sought to analyze the reasoning behind their identification choices, particularly the role of content familiarity and linguistic features.MethodsBetween May and August 2023, a total of 35 experts (medical: n=22; humanities: n=13) were each presented with 2 pairs of texts on different medical topics. Each pair had similar content and structure: 1 text was written by a medical student, and the other was generated by ChatGPT (version 3.5, March 2023). Experts were asked to identify the AI-generated text and justify their choice. These justifications were analyzed through a multistage, interdisciplinary qualitative analysis to identify relevant textual features. Before unblinding, experts rated each text on 6 characteristics: linguistic fluency and spelling/grammatical accuracy, scientific quality, logical coherence, expression of knowledge limitations, formulation of future research questions, and citation quality. Univariate tests and multivariate logistic regression analyses were used to examine associations between participants' characteristics, their stated reasons for author identification, and the likelihood of correctly determining a text's authorship.ResultsOverall, in 48 out of 69 (70%) decision rounds, participants accurately identified the AI-generated texts, with minimal difference between groups (medical: 31/43, 72%; humanities: 17/26, 65%; odds ratio [OR] 1.37, 95% CI 0.5-3.9). While content errors had little impact on identification accuracy, stylistic features-particularly redundancy (OR 6.90, 95% CI 1.01-47.1), repetition (OR 8.05, 95% CI 1.25-51.7), and thread/coherence (OR 6.62, 95% CI 1.25-35.2)-played a crucial role in participants' decisions to identify a text as AI-generated.ConclusionsThe findings suggest that both medical and humanities experts were able to identify ChatGPT-generated texts in medical contexts, with their decisions largely based on linguistic attributes. The accuracy of identification appears to be independent of experts' familiarity with the text content. As the decision-making process primarily relies on linguistic attributes-such as stylistic features and text coherence-further quasi-experimental studies using texts from other academic disciplines should be conducted to determine whether instructions based on these features can enhance lecturers' ability to distinguish between student-authored and AI-generated work.
| S-EPMC11914838 | biostudies-literature