两家制药公司已经进入了一个崭新的世界，这是首次针对人工智能设计的强迫症药物进行人体测试。
英国初创公司Exscientia和日本住友大日本制药公司(Sumitomo Dainippon Pharma)使用人工智能在不到12个月的时间内就发明了这种药物，比普通人开发药物的平均时间缩短了四年。
Exscientia首席执行官安德鲁·霍普金斯(Andrew Hopkins)将这种药物(称为DSP-1181的一种分子)的临床人体试验描述为“药物发现中的关键里程碑”。
我们的驱动动力是加快创新药物的范围，从尖端科学进入临床以增加患者的治疗选择。这意味着减少制造和测试药物的时间。消费者应该从诊所的进步中受益，”霍普金斯先生告诉《华盛顿时报》。
人工智能已被用于快速，准确地诊断疾病和分析患者数据，但这次在药物的创建中它具有更加机器人化的“动手”作用。
AI通过对化合物进行筛选和分类的算法来确定最安全，最有效的治疗特定疾病的算法，从而创造出了这种药物。
“分子的不同特性会产生不同的副作用，因此，使用计算机AI来帮助预测是一个非常有趣的想法，”犹他州的放射科医生Wendell Gibby博士说。影像公司。“人工智能是一项非常强大的技术，已在医学的许多领域中使用，尤其是在放射学中。”
据新闻网站Tech Acrobat称，Exscientia使用了一个名为Centaur Chemist的机器学习平台，据报道，该平台通过将AI方法与有关药物如何与人体相互作用的知识配对，缩短了研究新化合物所需的时间。
这家科技新闻公司写道，该工具可以研究数百万种分子组合，这是“在现实世界中运作的”人类科学家的快速替代品。
由于具有人工智能，因此在搜索350种合成化合物而不是平均2500种化合物的过程中，发现了用于治疗强迫症的候选化合物。
1月底，DSP-1181在日本进入了1期试验。如果试验成功，将在全球范围内(包括在美国)开发这种药物的计划。
制造一种通过第一阶段测试批准的药物可能要花费数百万美元，但是使用AI更快地开发候选药物可以节省大量成本。
Companies have used artificial intelligence for an array of health care services, including detection of breast cancer, eye diseases and acute kidney injuries. It also can complete administrative tasks for physicians.
Daniel Faggella, founder and CEO of Emerj, an AI research company, said artificial intelligence could help extend specialized health expertise in diagnostics and other areas.
“Whether [AI] be good, bad or ugly, it’s going to be slow in health care,” Mr. Faggella said. “On the aggregate, maybe that slowness will help because nothing’s going to hit the scene too quickly and disrupt things too quickly.”
He said he expects to see gradual improvements across the board in health care — including with diagnoses, genetics and efficiency in treating patients — thanks to AI.
As artificial intelligence plays a more active role in health care, some question the way this technology operates and its impact on the human labor force.
Dr. Jennifer Joe, an urgent care physician and founder of MedTech Boston, expressed concerns about the algorithms AI uses and the data available.
“Our biggest concern is bias in data,” said Dr. Joe. “I think we’ve seen many examples of that. A lot of the data we’ve collected, especially from clinical trials, has been from men, traditionally white men, so how much of that data are you going to be able to extrapolate out to women or people of color or to pregnant women or to older people?
“We want to make sure the algorithm is fair, is unbiased, is regularly vetted and checked against best practices using clinical data and with clinicians,” she said.
Although AI can outperform humans in many tasks, researchers concluded that jobs won’t be automated on a large scale for a while, according to an article published last year in the Future Healthcare Journal.
The researchers, who hail from Babson College and Deloitte Consulting, pointed out ethical implications around the use of AI and noted that many AI systems are built for single tasks whereas health care professionals do more than read and interpret images.
Mr.Hopkinsof Exscientia acknowledged that AI has its limitations. He said it cannot predict all the possible ways a drug will act in an “incredibly complex” human body, but it can help researchers learn faster and adapt their experiments.
“If we can learn faster from fewer experiments, we can move drugs forward to the patient faster than we have before,” he said. “Drug discovery is about embracing uncertainty, and AI is helping us learn faster in the face of that uncertainty.”
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