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This article presents a conceptual framework for using artificial intelligence (AI) to advance precision medicine in Tourette Syndrome (TS) management, addressing challenges in conventional diagnostic and therapeutic methods. It discusses predictive model construction, personalised diagnosis and treatment strategies, and intelligent monitoring. The authors suggest that AI-driven precision medicine improves diagnostic accuracy, optimises treatments, and enhances patient prognosis.
AI-driven precision medicine holds promise for improving diagnostic accuracy, optimising treatments, and enhancing patient prognosis in Tourette Syndrome.
Tourette Syndrome (TS) is a complex neurodevelopmental disorder characterised by motor and vocal tics that significantly impair quality of life. Conventional diagnostic and therapeutic methods face challenges due to subjectivity, lack of personalisation, and difficulties in prognostic prediction. Artificial Intelligence (AI) offers novel solutions, advancing TS management towards precision medicine. This article presents a conceptual framework for AI-driven technologies in TS, advocating for a paradigm shift from empirical treatment to precision medicine. We discuss key components including predictive model construction, personalised diagnosis, treatment strategies, and intelligent monitoring. Research indicates that the core value of AI in TS precision medicine lies in its predictiveness, individualisation, and intelligence. Predictive models using multimodal data enable early identification and prognostic assessment. Furthermore, personalised approaches tailor diagnosis and treatment to individual patient characteristics, thereby improving outcomes. Intelligent systems enable automated monitoring and real-time adjustments, optimising clinical workflows. Substantial clinical evidence demonstrates that AI-driven precision medicine improves diagnostic accuracy, optimises treatments, and enhances patient prognosis. Despite this potential, challenges remain in data quality, algorithm interpretability, and clinical translation. Future efforts should focus on enhancing interdisciplinary collaboration, promoting standardisation, and facilitating clinical adoption to deliver more precise, effective, and accessible care for TS patients.
