AI在医疗健康领域的应用 - AI与前沿科学和医生合作，推动医疗健康领域发展[2] - AI能够分析生活方式、活动、睡眠、空气质量和疾病发生等数据，从而更好地预测癌症、心脏病和2型糖尿病等疾病[6] - AI可以辅助医生进行扫描，帮助发现肿瘤，并协助医生记录，从而实现更人性化的互动[6][7] - AI可以通过面部微表情和视线模式来量化抑郁症，目前正在英国国民健康服务体系中针对产后妇女进行试验[7] - AI驱动的药物发现和个性化治疗正在兴起，这意味着能够更有效地靶向肿瘤，同时减少对患者的伤害[10][11] 癌症诊断与治疗 - 行业专家认为，AI将在癌症的诊断和治疗方面带来最大的变革，因为癌症存在诸多变量，如肿瘤的遗传学、位置、阶段、患者的遗传学，甚至微生物组[8] - 血液检测将越来越多地用于筛查多种癌症，更有针对性的筛查活动将更早地发现更多癌症，从而可以更好地治疗[9] 数据的重要性 - 数据是AI的燃料，需要高质量、适用且统一[14] - 基因组测序的成本已大幅降低，但关键在于如何利用这些数据[14][15] - Genomics England正在对10万名有患罕见疾病风险的婴儿进行基因组测序，及早发现可以避免失明等情况[16] 可穿戴设备与健康 - 可穿戴设备可以收集大量关于个人的数据，帮助人们了解自身健康状况的基线[17][18] - 可穿戴设备虽然不是医疗设备，但可以帮助人们发现身体模式的变化，从而及早发现健康问题，例如心脏病甚至癌症[19] - 睡眠模式的改变可能在痴呆症症状出现前20到30年就已发生，因此，如果能够识别这些数据中的模式，就有机会采取行动[20] 医疗的最终目标 - 医疗的目标不仅是延长寿命，还要提高生活质量，保持身心健康[24] - AI有潜力将医疗体系从“疾病护理”转变为“健康护理”，并为所有人提供服务[24]

Healthcare Paradigm Shift - The healthcare industry is shifting towards a patient-focused, prevention-driven approach termed "Medicine 3.0%" [1] - Medicine 2.0%, which focuses on treating diseases when present, is deemed less effective compared to the proactive approach of Medicine 3.0% [2] - Medicine 3.0% emphasizes a longer-term perspective on chronic disease prevention [2] Diagnostic Tools and Screening - Novel applications of existing diagnostic tools are being explored for preventive care [2] - DEXA scans, typically costing under $300, are used to assess bone density, muscle mass, and body fat [3] - Regular preventive full body MRIs are recommended by some, despite being controversial and not typically covered by insurance [3] Early Detection and Risk - Early detection of cancer is crucial, as it increases the odds of successful treatment due to a smaller tumor burden [4] - Full body screening may lead to false positives, which can be a traumatic experience [4]

Genetic Predisposition and Disease - Genetic variations account for 04% of the differences between individuals, influencing traits and disease susceptibility [3] - Genetics reveals two paths to disease predisposition: monogenic diseases caused by a single gene mutation and polygenic diseases influenced by hundreds or thousands of DNA variations combined with lifestyle factors [5][6] - Approximately 50% of chronic diseases, including type 2 diabetes, obesity, heart disease, and depression, are attributable to genetic factors [6] - Polygenic risk scores can identify individuals with a two-to-threefold increased genetic risk for obesity, hypertension, or heart disease, enabling preventative measures [7] Genetic Technology and Prevention - Genetic guidance helps prevent diseases by enabling personalized prevention strategies based on individual genetic predispositions [10] - Epigenetics demonstrates that environmental factors, diet, stress, exercise, and the microbiome influence gene expression without altering the DNA sequence [12][13] - The completion of the human genome sequencing project cost approximately $3 billion and took over a decade; now, whole-genome DNA analysis from saliva costs around €600 and takes only a few days [15] - Genome editing technologies, like CRISPR, allow for correcting "typos" in the DNA, with applications in treating monogenic diseases such as sickle cell anemia [16] - Artificial intelligence is used to predict disease risks by learning from thousands or millions of genomes, enhancing precision medicine [16][17] Ethical Considerations and Future Implications - The convergence of genetics and technology raises ethical questions regarding access to genetic information, privacy protection, data management, and the right to know or not know [23] - Genetic information empowers individuals to personalize nutrition, treatment, and disease prevention, improving quality and duration of life [22] - Synthetic biology is advancing beyond understanding and correcting DNA to creating new genes, biological systems, and even artificial cells from scratch [17]