Petrušić Igor, Chiang Chia-Chun, Garcia-Azorin David, Ha Woo-Seok, Ornello Raffaele, Pellesi Lanfranco, Rubio-Beltrán Eloisa, Ruscheweyh Ruth, Waliszewska-Prosół Marta, Wells-Gatnik William
Laboratory for Advanced Analysis of Neuroimages, Faculty of Physical Chemistry, University of Belgrade, Belgrade, Serbia.
Department of Neurology, Mayo Clinic, Rochester, MN, USA.
J Headache Pain. 2025 Jan 2;26(1):2. doi: 10.1186/s10194-024-01944-7.
Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions. Furthermore, AI-driven advances in drug discovery leverage machine learning and generative AI to accelerate the identification of novel therapeutic targets and optimize treatment strategies for migraine and other headache disorders. Despite these advances, challenges such as data standardization, model explainability, and ethical considerations remain pivotal. Collaborative efforts between clinicians, biomedical and biotechnological engineers, AI scientists, legal representatives and bioethics experts are essential to overcoming these barriers and unlocking AI's full potential in transforming headache research and healthcare. This is a call to action in proposing novel frameworks for integrating AI-based technologies into headache care.
第二部分探讨了人工智能(AI)通过创新方法应对头痛疾病复杂性的变革潜力,这些方法包括数字孪生模型、可穿戴医疗技术和生物传感器,以及人工智能驱动的药物研发。数字孪生作为患者的动态数字表示,通过整合神经影像学、多组学和可穿戴传感器数据等各种数据集,为个性化头痛管理提供了机会,以推进头痛研究、优化治疗并实现虚拟试验。此外,配备下一代生物传感器并结合多智能体聊天机器人的人工智能驱动的可穿戴设备可以实现实时生理和生化监测、诊断,促进头痛发作的早期预测和预防、疾病跟踪以及个性化干预。此外,人工智能驱动的药物研发进展利用机器学习和生成式人工智能来加速新型治疗靶点的识别,并优化偏头痛和其他头痛疾病的治疗策略。尽管取得了这些进展,但数据标准化、模型可解释性和伦理考量等挑战仍然至关重要。临床医生、生物医学和生物技术工程师、人工智能科学家、法律代表和生物伦理专家之间的合作对于克服这些障碍并释放人工智能在变革头痛研究和医疗保健方面的全部潜力至关重要。这是一个行动呼吁,旨在提出将基于人工智能的技术整合到头痛护理中的新框架。