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Nicole Petalidou

Aristotle University of Thessaloniki, Greece

Presentation Title:

The use of artificial intelligence for advancing patient-centered care in eating disorders

Abstract

Introduction: Eating Disorders (EDs) represent a significant challenge in mental health care, requiring accurate diagnosis and personalized treatment approaches. The integration of artificial intelligence (AI) in healthcare has opened new avenues for enhancing the diagnosis and treatment of these disorders. 

Aim: This study aims to explore the potential of AI in advancing patient-centered care for individuals with EDs.

Methodology: The study employed a multi-faceted approach combining a systematic literature review of AI applications in ED care, case study analyses of implemented AI solutions, and expert interviews with healthcare professionals and AI researchers. This comprehensive methodology allowed for a thorough evaluation of current AI applications and their effectiveness in real-world scenarios.

Results: AI technologies, particularly behavior analysis algorithms and natural language processing techniques, have demonstrated remarkable accuracy in recognizing early signs of EDs. AI-based clinical decision support systems can integrate multiple data sources, leading to improvements in both the accuracy and speed of diagnosis. This enhanced diagnostic ability promises earlier intervention and more personalized treatment plans.

Discussion: AI has the potential to significantly improve patient-centered care in EDs. AI-driven tools provide continuous monitoring and adaptive interventions, allowing for timely and targeted care. This approach aligns with the patient-centered model by offering individualized and responsive treatment options. However, several challenges need to be addressed: data privacy concerns, particularly given the sensitive nature of mental health information; the need for large datasets to effectively train AI systems; and the complex ethical landscape. Looking ahead, for seamless integration of AI into existing clinical practices to take place, collaboration between AI developers, healthcare providers, and policymakers is paramount. Moreover, long-term evaluation studies are required to assess the sustained impact of AI interventions on patient outcomes. Medical education should incorporate AI literacy, including training on the ethical use of AI in healthcare and fostering critical thinking on its applications in mental health. As we move forward, it will be crucial to balance technological advancements with ethical considerations, keeping the patient's wellbeing at the center of our efforts.

Biography

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