# Towards precision medicine in Tourette syndrome: a perspective on AI-driven predictive modelling and personalised care

## TL;DR

This paper explores how AI can improve Tourette Syndrome care by enabling personalized diagnosis, treatment, and monitoring.

## Contribution

The paper introduces a conceptual framework for AI-driven precision medicine in Tourette Syndrome.

## Key findings

- AI-driven predictive models can improve early identification and prognosis of Tourette Syndrome.
- Personalized AI approaches enhance diagnostic accuracy and treatment outcomes.
- Intelligent systems allow for real-time monitoring and optimization of clinical workflows.

## Abstract

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.