New York Medical College, Valhalla.
Foundational Innovations, IBM Watson Health, Yorktown Heights, NY.
Am J Med. 2018 Feb;131(2):129-133. doi: 10.1016/j.amjmed.2017.10.035. Epub 2017 Nov 7.
Computer science advances and ultra-fast computing speeds find artificial intelligence (AI) broadly benefitting modern society-forecasting weather, recognizing faces, detecting fraud, and deciphering genomics. AI's future role in medical practice remains an unanswered question. Machines (computers) learn to detect patterns not decipherable using biostatistics by processing massive datasets (big data) through layered mathematical models (algorithms). Correcting algorithm mistakes (training) adds to AI predictive model confidence. AI is being successfully applied for image analysis in radiology, pathology, and dermatology, with diagnostic speed exceeding, and accuracy paralleling, medical experts. While diagnostic confidence never reaches 100%, combining machines plus physicians reliably enhances system performance. Cognitive programs are impacting medical practice by applying natural language processing to read the rapidly expanding scientific literature and collate years of diverse electronic medical records. In this and other ways, AI may optimize the care trajectory of chronic disease patients, suggest precision therapies for complex illnesses, reduce medical errors, and improve subject enrollment into clinical trials.
计算机科学的进步和超高速计算速度使人工智能（AI）广泛造福现代社会——预测天气、识别人脸、检测欺诈和破译基因组。人工智能在医学实践中的未来作用仍是一个悬而未决的问题。机器（计算机）通过处理大量数据集（大数据）并通过分层数学模型（算法）来学习检测使用生物统计学无法破译的模式。通过纠正算法错误（训练）可以增加 AI 预测模型的信心。人工智能正在放射学、病理学和皮肤病学的图像分析中得到成功应用，其诊断速度超过医学专家，准确性与医学专家相当。虽然诊断信心永远达不到 100%，但将机器与医生相结合可以可靠地提高系统性能。认知程序通过应用自然语言处理来阅读快速扩展的科学文献并整理多年的各种电子病历，正在对医疗实践产生影响。通过这种方式和其他方式，人工智能可以优化慢性病患者的护理轨迹，为复杂疾病提供精准治疗建议，减少医疗错误，并提高临床试验的受试者入组率。
