Machine learning in medicine: a practical introduction to natural language processing

Journal article

As healthcare landscapes evolve, Artificial intelligence (AI) has emerged as a transformative force in physiotherapy research in India. The integration of machine learning algorithms, computer vision, and natural language processing has significantly advanced the analysis of patient data, enabling the prediction of treatment outcomes and personalization of physiotherapy interventions. This overview delves into specific examples of successful AI integration in ongoing clinical trials within the Indian context, showcasing notable improvements in trial efficiency and positive impacts on patient outcomes. Challenges in implementing AI, including data security, ethical considerations, and the need for specialized training, are discussed. Proposed solutions encompass robust data encryption, ethical guidelines, interpretability of AI models, and targeted educational programs for healthcare professionals. Looking forward, the future outlook emphasizes personalized treatment plans, expanded tele physiotherapy using wearable technology, and the integration of augmented and virtual reality. Ethical and regulatory frameworks, continued advancements in robotic assistance, and interdisciplinary collaboration are highlighted as key factors shaping the trajectory of AI in physiotherapy clinical trials in India. The primary objectives of this manuscript are to explore the current state of AI in physiotherapy clinical trials in India, assess its utilization, and discuss the potential future developments in the field

Authors: Harrison, CJ; Sidey-Gibbons, CJ

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: BioMed Central
• Journal: BMC Medical Research Methodology
• Volume: 21
• Issue: 1
• Pages: 158-158
• Article number: 158
• Publication date: 2021-07-31
• DOI: 10.1186/s12874-021-01347-1
• EISSN: 1471-2288
• ISSN: 1471-2288
• Language: English
• Keywords: Artificial intelligence; Unified Medical Language System; Computer science; Latent Dirichlet allocation; Support vector machine; Natural language processing; Machine learning; Artificial neural network; Topic model