Brahma Neha, Vimal S
Department of Biochemistry, Saveetha Medical College & Hospital, Saveetha Institute of Medical and Technical Sciences (SIMATS), Thandalam, Chennai, 602105, Tamil Nadu, India.
Brain Spine. 2024 Aug 8;4:102919. doi: 10.1016/j.bas.2024.102919. eCollection 2024.
The integration of artificial intelligence (AI) into neuroimaging represents a transformative shift in the diagnosis and treatment of neurodegenerative diseases. AI algorithms, particularly deep learning models, have demonstrated remarkable capabilities in analyzing complex neuroimaging data, leading to enhanced diagnostic accuracy and personalized treatment strategies. This letter discusses the opportunities AI presents in neuroimaging, including improved disease detection, predictive modeling, and treatment planning. However, the rapid adoption of AI technologies also raises significant ethical challenges. Issues such as algorithmic bias, data privacy, and the interpretability of AI-driven insights must be addressed to ensure that these technologies are used responsibly and equitably. As neuroimaging continues to evolve, a collaborative approach involving researchers, clinicians, and ethicists is essential to navigate these challenges and maximize the benefits of AI in improving patient outcomes in neurodegenerative diseases.
将人工智能(AI)整合到神经影像学中,代表着神经退行性疾病诊断和治疗的变革性转变。人工智能算法,特别是深度学习模型,在分析复杂的神经影像学数据方面展现出卓越能力,从而提高了诊断准确性并生成个性化治疗策略。本文探讨了人工智能在神经影像学中带来的机遇,包括疾病检测的改善、预测建模和治疗规划。然而,人工智能技术的迅速采用也引发了重大的伦理挑战。必须解决算法偏见、数据隐私以及人工智能驱动见解的可解释性等问题,以确保这些技术得到负责任且公平的使用。随着神经影像学不断发展,研究人员、临床医生和伦理学家共同协作的方法对于应对这些挑战以及最大限度地发挥人工智能在改善神经退行性疾病患者预后方面的益处至关重要。