The writer is co-founder and head of research and development at Qure.ai, an AI developer for medical images In a medical artificial intelligence business, the quality of your algorithms -- and therefore the value of your company -- depends on your access to data. In this, the health tech sector is in some ways similar to advertising and internet search industries: it has quickly learnt that data is immensely valuable. However, on the internet, most user-generated data is used to train algorithms that encourage consumption, commerce or engagement. Health data is vastly different -- it can be used for the global public good. It can help us track epidemics and prevent their spread, discover new drugs and diagnostics, and advance medical research that can help us live healthier, longer lives.

Artificial intelligence (AI) applications have attracted considerable ethical attention for good reasons. Although AI models might advance human welfare in unprecedented ways, progress will not occur without substantial risks. This article considers 3 such risks: system malfunctions, privacy protections, and consent to data repurposing. To meet these challenges, traditional risk managers will likely need to collaborate intensively with computer scientists, bioinformaticists, information technologists, and data privacy and security experts. This essay will speculate on the degree to which these AI risks might be embraced or dismissed by risk management.

Check out this StatNews piece addressing the question: should patients consent for use of artificial intelligence in the clinic setting? It builds the case for an emerging crisis in healthcare where patients are the victims of a failure to disclose the use of AI in the clinical setting. The concerns expressed reflect the a false dichotomy of man or machine. We like to see something as done by the doctor or done by the machine -- with a clear boundary separating where the computer stops and we begin. But given our relationship with technology things aren't shaping up this way.

Google DeepMind has extended its controversial partnership with the Royal Free London NHS Foundation Trust, signing a new five-year deal. The London trust will work with the British machine learning company, which was acquired by Google in 2014, on further developing the Streams clinical app, which has so far used algorithms to detect acute kidney injury. In a statement, Royal Free said that app will be used as a diagnostic support tool for a far wider range of illness, alerting doctors earlier of patients at risk of getting ill. "Like breaking news alerts on a mobile phone, the technology will notify nurses and doctors immediately when test results show a patient is at risk of becoming seriously ill, and provide all the information they need to take action. "Streams will be extended beyond AKI to help care for patients with other serious conditions including sepsis and organ failure." The expanded Streams will alert doctors to patient in need "within seconds", rather than hours, it added. It should also free up doctors from paperwork, creating more than half a million hours of extra direct care, the trust claimed. Royal Free medical director Stephen Powis said: "This is about bringing information to doctors and nurses, much in the way we get news alerts on our phones.

For more than a decade the company formerly known as Google, latterly rebranded Alphabet to illustrate the full breadth of its A to Z business ambitions, has engineered an annually increasing revenue generating empire which last year pulled in 75 billion. And it's done this mostly by mining user data for ad targeting intel. Slice it and dice it how you like but Google's business engine needs data like the human body needs oxygen. Most of its products are thus designed to remove friction to accessing more user data; whether it's free search, free email, free cloud storage, free document editing tools, free messaging apps, a fuzzy social network that no one loves but which is somehow still hanging around, free maps, a mobile OS platform that OEMs can load onto smartphone hardware without paying a license fee… Most of what Google builds it opens to all comers to keep the data pouring in. The bits and bytes must flow.