Bhattacharya Sudip, Saleem Sheikh Mohd, Singh Alok, Singh Sukhpreet, Tripathi Shailesh
Department of Community and Family Medicine, All India Institute of Medical Sciences, (AIIMS Deoghar), Deoghar, India.
Department of Health and Family Welfare, EVTHS, UNICEF, New Delhi, India.
Front Oncol. 2024 Sep 20;14:1465720. doi: 10.3389/fonc.2024.1465720. eCollection 2024.
Regenerative AI is transforming breast cancer diagnosis and treatment through enhanced imaging analysis, personalized medicine, drug discovery, and remote patient monitoring. AI algorithms can detect subtle patterns in mammograms and other imaging modalities with high accuracy, potentially leading to earlier diagnoses. In treatment planning, AI integrates patient-specific data to predict individual responses and optimize therapies. For drug discovery, generative AI models rapidly design and screen novel molecules targeting breast cancer pathways. Remote monitoring tools powered by AI provide real-time insights to guide care. Examples include Google's LYNA for analyzing pathology slides, Kheiron's Mia for mammogram interpretation, and Tempus's platform for integrating clinical and genomic data. While promising, challenges remain, including limited high-quality training data, integration into clinical workflows, interpretability of AI decisions, and regulatory/ethical concerns. Strategies to address these include collaborative data-sharing initiatives, user-centered design, explainable AI techniques, and robust oversight frameworks. In developing countries, AI tools like MammoAssist and Niramai's thermal imaging system are improving access to screening. Overall, regenerative AI offers significant potential to enhance breast cancer care, but judicious implementation with awareness of limitations is crucial. Coordinated efforts across the healthcare ecosystem are needed to fully realize AI's benefits while addressing challenges.
再生人工智能正在通过增强成像分析、个性化医疗、药物研发和远程患者监测来改变乳腺癌的诊断和治疗。人工智能算法能够高精度地检测乳房X光片和其他成像模式中的细微模式，有可能实现更早的诊断。在治疗规划中，人工智能整合患者特定数据以预测个体反应并优化治疗方案。对于药物研发，生成式人工智能模型能够快速设计和筛选针对乳腺癌通路的新型分子。由人工智能驱动的远程监测工具提供实时洞察以指导护理。例子包括谷歌用于分析病理切片的LYNA、凯伦用于解读乳房X光片的Mia以及Tempus用于整合临床和基因组数据的平台。虽然前景广阔，但挑战依然存在，包括高质量训练数据有限、融入临床工作流程、人工智能决策的可解释性以及监管/伦理问题。应对这些问题的策略包括合作数据共享计划、以用户为中心的设计、可解释人工智能技术以及健全的监督框架。在发展中国家，像MammoAssist和Niramai的热成像系统这样的人工智能工具正在改善筛查的可及性。总体而言，再生人工智能在提升乳腺癌护理方面具有巨大潜力，但在实施时明智地意识到其局限性至关重要。需要医疗保健生态系统各方协同努力，以充分实现人工智能的益处，同时应对挑战。
