Background: Social determinants of health (SDOH) are critical contributors to cancer disparities, influencing prevention, early detection, treatment access, and survival outcomes. Addressing these disparities is essential in achieving equitable oncology care. Artificial intelligence (AI) is revolutionizing oncology by leveraging advanced computational methods to address SDOH-driven disparities through predictive analytics, data integration, and precision medicine. Methods: This review synthesizes findings from systematic reviews and original research on AI applications in cancer-focused SDOH research. Key methodologies include machine learning (ML), natural language processing (NLP), deep learning-based medical imaging, and explainable AI (XAI). Special emphasis is placed on AI’s ability to analyze large-scale oncology datasets, including electronic health records (EHRs), geographic information systems (GIS), and real-world clinical trial data, to enhance cancer risk stratification, optimize screening programs, and improve resource allocation. Results: AI has demonstrated significant advancements in cancer diagnostics, treatment planning, and survival prediction by integrating SDOH data. AI-driven radiomics and histopathology have enhanced early detection, particularly in underserved populations. Predictive modeling has improved personalized oncology care, enabling stratification based on socioeconomic and environmental factors. However, challenges remain, including AI bias in screening, trial underrepresentation, and treatment recommendation disparities. Conclusions: AI holds substantial potential to reduce cancer disparities by integrating SDOH into risk prediction, screening, and treatment personalization. Ethical deployment, bias mitigation, and robust regulatory frameworks are essential in ensuring fairness in AI-driven oncology. Integrating AI into precision oncology and public health strategies can bridge cancer care gaps, enhance early detection, and improve treatment outcomes for vulnerable populations.
背景：健康的社会决定因素是导致癌症差异的关键因素，其影响癌症预防、早期发现、治疗可及性及生存结局。解决这些差异对于实现公平的肿瘤诊疗至关重要。人工智能正通过先进的计算方法，利用预测分析、数据整合和精准医疗应对社会决定因素驱动的差异，从而推动肿瘤学领域的变革。 方法：本综述整合了关于人工智能在癌症相关社会决定因素研究中应用的系统综述与原始研究结果。核心方法包括机器学习、自然语言处理、基于深度学习的医学影像分析以及可解释人工智能。研究重点聚焦于人工智能分析大规模肿瘤学数据集的能力，包括电子健康记录、地理信息系统和真实世界临床试验数据，以提升癌症风险分层、优化筛查项目并改善资源配置。 结果：通过整合社会决定因素数据，人工智能在癌症诊断、治疗规划和生存预测方面取得显著进展。人工智能驱动的影像组学与病理学技术强化了早期检测能力，尤其在医疗服务不足人群中表现突出。预测模型优化了个体化肿瘤诊疗，实现了基于社会经济与环境因素的分层管理。然而，该领域仍面临挑战，包括筛查中的人工智能偏倚、临床试验代表性不足以及治疗建议差异等问题。 结论：人工智能通过将社会决定因素整合至风险预测、筛查和治疗个体化中，展现出减少癌症差异的巨大潜力。伦理部署、偏倚缓解和健全的监管框架对于确保人工智能驱动肿瘤学的公平性至关重要。将人工智能融入精准肿瘤学与公共卫生策略，有助于弥合癌症诊疗差距，提升早期检测水平，并改善弱势群体的治疗结局。
肿瘤(癌症)患者之家