As a venture capitalist, Jim Breyer has invested in many breakthrough technology ideas in recent decades, names we all know and interact with on a daily basis like Meta and Spotify. But the biggest one of all may be next, he says, through the combination of artificial intelligence and branches of science involved in medicine. Since 2017, Breyer says his No. 1 task as a venture investor has focused on finding the best disease and medical data from leading research hospitals such as Memorial Sloan Kettering, MD Anderson, and Johns Hopkins -- highly proprietary, significant data to license into startups Breyer Capital is backing. "AI and medicine is perhaps the most attractive new investment opportunity I've ever seen," Breyer, founder and CEO of Breyer Capital, said at last week's CNBC Healthy Returns virtual summit. Breyer says he is not alone among tech leaders holding this view, citing a fireside chat he recently conducted with Michael Dell, during which the PC pioneer agreed, and private conversations he has had with tech CEOs.

Improving the speed and accuracy of clinical diagnosis, augmenting clinical decision-making, reducing human error in clinical care, individualizing therapies based on a patient's genomic and metabolomic profiles, differentiating benign from cancerous lesions with impeccable accuracy, identifying likely conditions a person may develop years down the road, spotting early tell-tale signs of an ultrarare disease, intercepting dangerous drug interactions before a patient is given a new medication, yielding real-time insights amidst a raging pandemic to inform optimal treatment of patients infected with a novel human pathogen. These are some of the promises that physicians and researchers look to fulfill using artificial intelligence -- promises poised to transform clinical care, lead to better patient outcomes, and, ultimately, improve human lives. Yet, AI is no silver bullet. It can fall prey to the cognitive fallibilities and blind spots of the humans who design it. AI models can be as imperfect as the data and clinical practices that the machine-learning algorithms are trained on, propagating the very same biases AI was designed to eliminate in the first place. Beyond conceptual and design pitfalls, realizing the potential of AI also requires overcoming systemic hurdles that stand in the way of integrating AI-based technologies into clinical practice.

This blog is a summary of the discussions that took place at the DataKind UK ethics book club on AI and medicine, on 22nd April 2020. Views represented here are those of attendees at the book club. By the time DataKind UK's ethics book club rolled around, our topic -- AI and medicine -- felt pretty timely. In groups, we discussed the contact-tracing methods being used by public health authorities around the world, and concerns that privacy might be a casualty of the public health response. While there might be legitimate arguments to pry into people's personal lives in the midst of a pandemic, we also wondered what happens after the crisis is over.

Kai-Fu Lee became a legend in artificial intelligence research and the tech world because of his groundbreaking work the past three decades with Apple, Microsoft, and Google. But Lee says cancer has radically changed the way he views technology, his life, and the world of medicine. In September 2013, the former head of Google China was given a diagnosis of stage IV follicular non-Hodgkin's lymphoma. The cancer diagnosis put his career and life on the line. Then, it put his career and life in a new light.

For centuries, physicians and healers focused primarily on treating acute problems such as broken bones, wounds, and infections. "If you had an infectious disease, you went to the doctor, the doctor treated you, and then you went home," says Balaji Krishnapuram, director and distinguished engineer at IBM Watson Health. Today, the majority of healthcare revolves around treating chronic conditions such as heart disease, diabetes, and asthma. Treating chronic ailments often requires multiple visits to healthcare providers, over extended periods of time. In modern societies, "the old ways of delivering care will not work," says Krishnapuram. "We need to enable patients to take care of themselves to a far greater degree than before, and we need to move more treatment from the doctor's office or hospital to an outpatient setting or to the patient's home." Unlike traditional healthcare, which tends to be labor-intensive, emerging models of healthcare are knowledge-driven and data-intensive. Many of the newer healthcare delivery models will depend on a new generation of user-friendly, real-time big data analytics and artificial intelligence/machine learning (AI/ML) tools. Identifying risks, determining who is at risk, and identifying interventions that will reduce risk. Supporting and enabling customized self-care treatment plans for individual patients, monitoring patient health in real time, adjusting doses of medication, and providing incentives for behavioral changes leading to improved health. Optimizing healthcare processes (everything from medical treatment itself to the various ways insurers reimburse providers) through rigorous data analysis to improve outcomes and quality of care while reducing costs.

Data-driven techniques have improved decision-making processes for people in industries such as finance and real estate. Yet, despite promising solutions that data analytics and artificial intelligence/machine learning (ML) tools can bring to healthcare, the industry remains largely unconvinced. In this O'Reilly report, you'll explore the potential of--and impediments to--widespread adoption of AI and ML in the medical field. You'll also learn how extensive government regulation and resistance from the medical community have so far stymied full-scale acceptance of sophisticated data analytics in healthcare. Mike Barlow is an award-winning journalist, author, and communications strategy consultant.