AI is transforming the practice of medicine. It's helping doctors diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine. Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. You'll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you'll learn how to handle missing data, a key real-world challenge.

Thousands of financial investors and Wall Street pundits advise people daily about what economic trends are coming, what stocks are hot and what is on the upswing. Those same investors and banks benefit from financial success by using a bevy of information and data analysis to predict financial risks and steer their clients to recommended strategies. But while big data and research have been the forerunners to competent investment advice, the healthcare industry is seemingly jumping into the mix and using data to predict a person's future health. In its simplest terms, predictive medicine relies on the study and analysis of large quantities of data to determine a patient's future health and the likelihood they may get a disease. A Harvard Business Review report lauded the advent of predictive medicine when it said, "Predictive tools are helping providers -- both doctors' groups and hospitals -- assess patients' risk of contracting a whole host of diseases and conditions. For the volume-to-value paradigm shift in healthcare, predictive analytics, though rarely visible, is the essential enabler."

As we gain access to a greater depth and range of health-related information about individuals, three questions arise: (1) Can we build better models to predict individual-level risk of ill health? (2) How much data do we need to effectively predict ill health? (3) Are new methods required to process the added complexity that new forms of data bring? The aim of the study is to apply a machine learning approach to identify the relative contribution of personal, social, health-related, biomarker and genetic data as predictors of future health in individuals. Using longitudinal data from 6830 individuals in the UK from Understanding Society (2010-12 to 2015-17), the study compares the predictive performance of five types of measures: personal (e.g. age, sex), social (e.g. occupation, education), health-related (e.g. body weight, grip strength), biomarker (e.g. cholesterol, hormones) and genetic single nucleotide polymorphisms (SNPs). The predicted outcome variable was limiting long-term illness one and five years from baseline. Two machine learning approaches were used to build predictive models: deep learning via neural networks and XGBoost (gradient boosting decision trees). Model fit was compared to traditional logistic regression models. Results found that health-related measures had the strongest prediction of future health status, with genetic data performing poorly. Machine learning models only offered marginal improvements in model accuracy when compared to logistic regression models, but also performed well on other metrics e.g. neural networks were best on AUC and XGBoost on precision. The study suggests that increasing complexity of data and methods does not necessarily translate to improved understanding of the determinants of health or performance of predictive models of ill health.

It was a sunny day outside, with a hint of spring in the air. I followed Angela, whose name has been changed to protect her identity, down the corridor towards my consulting room in Melbourne. She'd been my patient for several years, but that morning I noticed her shuffling her feet a little as she walked. Her facial expression seemed a bit flat and I noticed she had a mild tremor. I referred her to a neurologist and within a week she was commenced on treatment for Parkinson's disease, but I kicked myself for not picking up on her symptoms sooner.

Google is banning the development of artificial-intelligence (AI) software that can be used in weapons, chief executive Sundar Mr Pichai said, setting strict new ethical guidelines for how the tech giant should conduct business in an age of increasingly powerful AI. The new rules could set the tone for the deployment of AI far beyond Google, as rivals in Silicon Valley and around the world compete for supremacy in self-driving cars, automated assistants, robotics, military AI and other industries. "We recognise that such powerful technology raises equally powerful questions about its use," Mr Mr Pichai wrote in a blog post. "As a leader in AI, we feel a special responsibility to get this right." Google reveals'terrifying' bot to call people and pretend to be human The ethical principles are a response to a firestorm of employee resignations and public criticism over a Google contract with the US Defence Department for software that could help analyse drone video, which critics argued had nudged the company one step closer to the "business of war." Google executives said last week that they would not renew the deal for the military's AI endeavour, known as Project Maven, when it expires next year.

Artificial intelligence may still be in its infancy, but it's moving fast. Nowhere is this more apparent than in the data-rich health sector. AI has the potential to provide more precise, personalised care, as well as help us to shift our focus from treatment to prevention and tackle some of the world's biggest global health issues. The WHO estimates that achieving the health-related targets under the Sustainable Development Goals – from ending tuberculosis to ensuring universal access to sexual and reproductive healthcare services by 2030 – will cost between $134bn-$371bn (£97bn-£270bn) a year over current health spending. AI startups raised $15.2bn last year alone, adding to investments made by tech giants like Google, Facebook, and Alibaba and a host of research institutions.