Large language models in physical therapy: time to adapt and adept.

Healthcare is experiencing a transformative phase, with artificial intelligence (AI) and machine learning (ML). Physical therapists (PTs) stand on the brink of a paradigm shift in education, practice, and research. Rather than visualizing AI as a threat, it presents an opportunity to revolutionize. This paper examines how large language models (LLMs), such as ChatGPT and BioMedLM, driven by deep ML can offer human-like performance but face challenges in accuracy due to vast data in PT and rehabilitation practice. PTs can benefit by developing and training an LLM specifically for streamlining administrative tasks, connecting globally, and customizing treatments using LLMs. However, human touch and creativity remain invaluable. This paper urges PTs to engage in learning and shaping AI models by highlighting the need for ethical use and human supervision to address potential biases. Embracing AI as a contributor, and not just a user, is crucial by integrating AI, fostering collaboration for a future in which AI enriches the PT field provided data accuracy, and the challenges associated with feeding the AI model are sensitively addressed.

Comment on Rochmah et al. Economic Burden of Stroke Disease: A Systematic Review. Int. J. Environ. Res. Public Health 2021, 18, 7552.

We have read with great interest the recently published article titled "Economic Burden of Stroke Disease: A Systematic Review" by Rochmah and colleagues [...].

Clinical utility of ultrasonography imaging in musculoskeletal conditions: A Systematic review and Meta-analysis.

Optimising ultrasonography imaging (UI) applications for clients is a highly specific and sensitive add-on method. The aim of this meta-analysis was to systematically evaluate the clinical utilisation of UI in musculoskeletal conditions by rehabilitation providers in the past decade. Two reviewers independently assessed relevant research articles from five databases electronically (Medline, Cochrane Library, EMBASE, ProQuest, EBSCO) and screened titles and abstracts based on predefined eligibility criteria (2010- 2020). A total of 147 articles were screened for eligibility by two reviewers independently, and any disagreements were resolved by another reviewer using Rayyan QCRI software. Ninety-seven duplicates were removed, and after excluding 21 studies, 16 randomized controlled trials were included and full texts retrieved. Data were synthesised using Revman 5.4 software for qualitative analysis and risk-of-bias assessment. Four similar studies were statistically analysed for heterogeneity of abdominal muscle contraction ratios. Two interventional studies were also analysed to assess the effect of feedback. The diagnostic application of UI was investigated using a consistent amount of literature, though from a rehabilitation perspective the literature is inconclusive. The clinical utility of UI in rehabilitation by physical therapists is conclusive and has potential to advance clinical practice. Further well-designed randomized controlled trials minimising selection biases will help improve the quality in this domain. Critical reflection, clinical reasoning, and mutual goal setting will help practising physical therapists to scrutinize the clinical practice more objectively.