Artificial Intelligence in Allied Health Practice: Current Evidence, Emerging Applications, And Implications for Patient Safety
Artificial intelligence (AI) is moving from experimental research into routine healthcare workflows, including diagnostic support, clinical documentation, rehabilitation, laboratory medicine, medical imaging, patient education, and service management. Allied health professionals are increasingly involved in these AI-enabled pathways, yet evidence and implementation guidance are often concentrated in physician-led specialties. This narrative review synthesises current evidence on the use of AI across allied health practice and examines implications for patient safety, professional accountability, education, and health equity. Literature from peer-reviewed journals and authoritative guidance was considered, with emphasis on clinical applications, implementation barriers, bias, explainability, human-AI interaction, and generative AI. Current evidence supports potential benefits such as workflow support, image interpretation, pattern recognition, personalised rehabilitation, laboratory decision support, documentation assistance, and improved access to health information. However, technical performance alone does not establish clinical effectiveness or safety. Dataset shift, hidden bias, automation bias, inaccurate generated content, privacy risks, poor interoperability, insufficient local validation, and unclear responsibility can introduce new failure modes. Safe adoption should include a defined intended use, representative validation, transparent performance monitoring, user training, incident reporting, privacy safeguards, human oversight, and periodic re-evaluation after deployment. The review concludes that AI has meaningful potential across allied health practice, but sustainable adoption depends on evidence-based governance and continuous patient-safety monitoring rather than technology adoption alone.