【AI详解】Current AI technologies in cancer diagnostics and treatment.

文献信息

一句话小结

本综述探讨了人工智能在癌症诊断和治疗中的应用，强调其在早期检测、个性化治疗及临床决策支持系统中的重要性。研究显示，AI技术不仅提高了癌症护理的精确性和效率，还面临数据隐私和可解释性等挑战，未来需关注更先进的研究方向。

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人工智能（AI） · 癌症 · 癌症诊断 · 深度学习（DL） · 机器学习（ML）

摘要

癌症仍然是一个重大国际健康问题，这要求我们发明新的方法以实现早期检测、精确诊断和个性化治疗。人工智能（AI）迅速成为现代肿瘤学中的一个突破性组成部分，为癌症护理提供了先进的工具。在本综述中，我们对当前用于癌症诊断和治疗方法的AI技术进行了系统调查。我们讨论了利用多种成像技术（如计算机断层扫描、磁共振成像、正电子发射断层扫描、超声和数字病理学）进行的AI辅助成像诊断，强调了深度学习在早期癌症检测中的日益重要性。我们还探讨了AI在基因组学和生物标志物发现、液体活检以及非侵入性诊断中的应用。在治疗干预方面，基于AI的临床决策支持系统、个性化治疗规划以及AI辅助药物发现正改变精确癌症治疗的格局。该综述还评估了AI对放射治疗、机器人手术和患者管理的影响，包括生存预测、远程监测和AI辅助临床试验。最后，我们讨论了数据隐私、可解释性和监管等重要挑战，并建议未来的研究方向应涉及联邦学习、合成生物学和量子增强的AI。本综述强调了AI在革命性改变癌症护理方面的巨大潜力，使诊断、治疗和患者管理变得更加精确、高效和个性化。

英文摘要

Cancer continues to be a significant international health issue, which demands the invention of new methods for early detection, precise diagnoses, and personalized treatments. Artificial intelligence (AI) has rapidly become a groundbreaking component in the modern era of oncology, offering sophisticated tools across the range of cancer care. In this review, we performed a systematic survey of the current status of AI technologies used for cancer diagnoses and therapeutic approaches. We discuss AI-facilitated imaging diagnostics using a range of modalities such as computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and digital pathology, highlighting the growing role of deep learning in detecting early-stage cancers. We also explore applications of AI in genomics and biomarker discovery, liquid biopsies, and non-invasive diagnoses. In therapeutic interventions, AI-based clinical decision support systems, individualized treatment planning, and AI-facilitated drug discovery are transforming precision cancer therapies. The review also evaluates the effects of AI on radiation therapy, robotic surgery, and patient management, including survival predictions, remote monitoring, and AI-facilitated clinical trials. Finally, we discuss important challenges such as data privacy, interpretability, and regulatory issues, and recommend future directions that involve the use of federated learning, synthetic biology, and quantum-boosted AI. This review highlights the groundbreaking potential of AI to revolutionize cancer care by making diagnostics, treatments, and patient management more precise, efficient, and personalized.

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主要研究问题

1. AI在癌症早期检测中的具体应用有哪些？
2. 如何评估AI技术在个性化癌症治疗中的有效性和安全性？
3. 在AI辅助的影像诊断中，哪些深度学习模型被广泛应用？
4. 当前AI技术在癌症治疗中的挑战主要集中在哪些方面？
5. AI如何改变癌症患者的管理和随访策略？

核心洞察

研究主题和范围

本文综述了人工智能（AI）在癌症诊断和治疗中的当前应用和发展，强调了AI在提高早期检测、精确诊断和个性化治疗方面的潜力。文献通过系统性调查，涵盖了不同类型癌症的AI技术应用，包括影像学诊断、基因组学、液体活检、药物发现及治疗干预等。

主要发现和观点

1. AI在影像学诊断中的应用：AI技术（如深度学习）在CT、MRI、PET和超声等影像学诊断中表现出色，能够更早期地检测癌症，并提高诊断的准确性。
2. 基因组学和生物标志物发现：AI在分析基因组数据方面的能力帮助识别新的生物标志物，促进了液体活检和非侵入性诊断的进展。
3. 个性化治疗的支持：AI驱动的临床决策支持系统和个体化治疗计划的制定正在改变癌症治疗的方式，帮助医生制定更为精准的治疗方案。
4. 药物发现和再利用：AI技术加速了药物发现的过程，利用大数据和机器学习模型优化药物组合，提高治疗效果。
5. 患者管理和远程监控：AI在患者管理中起到重要作用，通过可穿戴设备和数字健康应用实现实时监控，提高患者的生活质量。

研究进展

• 技术发展：AI在癌症治疗中的应用从影像学、基因组学到临床决策支持系统，逐步成熟，展现出广泛的临床应用前景。
• 临床试验：AI在优化临床试验设计和患者招募方面表现出色，能够通过实时数据分析提高试验的效率和效果。

争议与不足

• 数据隐私和安全：AI在处理患者数据时面临隐私保护和数据安全的挑战。
• 模型透明性和可解释性：AI模型的决策过程往往不够透明，影响了临床医生对其建议的信任。
• 算法偏见：由于训练数据的多样性不足，AI模型可能存在偏见，导致在某些人群中的应用效果不佳。

未来研究方向

• 多模态数据整合：未来的研究应集中在整合影像学、基因组学和临床数据，以实现更为精准的个性化治疗。
• 伦理与法规：随着AI技术的不断发展，需建立健全的伦理和法规框架，以保障AI在医疗中的安全应用。
• 教育与培训：提升医疗专业人员对AI技术的理解和应用能力，以便更好地融入临床实践。

主要结论

AI在癌症护理中展现出革命性的潜力，通过提升诊断的准确性和治疗的个性化，能够显著改善患者的治疗效果和生活质量。尽管面临诸多挑战，AI的进一步发展和应用将为癌症治疗带来新的机遇，推动肿瘤学向更精准、个性化的方向发展。

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