How do clinicians set rules that allow professionals "to make good use of technology to find patterns in complex data" but also "stop companies from extracting unethical value from those data?" Geis, from the American College of Radiology (ACR) Data Science Institute, is one of the authors of a joint statement that addresses the potential for the unethical use of data, the bias inherent in datasets, and the limits of algorithmic learning, and was the moderator of a session on the topic at the Radiological Society of North America (RSNA) 2019 Annual Meeting in Chicago. There's a very big grey area between an absolute ethical approach to data use and decisions that are profit-driven, he told Medscape Medical News. "Sitting on the sainthood side, I can stick to doing only what I see as good for my patients, maybe even taking vows of poverty," he said. "On the extreme other side, I'm doing things that put me in prison."

Artificial intelligence (AI) software can help radiologists perform their jobs better. But the ethical use of the technology in the field should promote well-being and minimize harm resulting from potential biases, according to a multi-society statement on the ethical use of AI in radiology. The statement, which includes views from the American College of Radiology (ACR) and the European Society of Radiology, aims to set expectations about the use of AI in the field of radiology and inform a common interpretation of the ethical issues related to the use of the technology. "This international multi-society statement is one step to help the radiology build an ethical framework to steer technological development, influence how stakeholders respond to and use AI and implement these tools to do right for patients," Raymond Geis, M.D., a senior scientist at the ACR Data Science Institute, said in a statement to Inside Digital Health . The societies focused on three major areas while creating the statement: data, algorithms and practice.

Machine learning is emerging as one of the key hopes to change the practice of radiology--the opportunity seems ripe, with rising calls for radiologists to demonstrate increased quality and more value, even as technology yields bigger datasets and more complexity. But exactly how machine learning will impact the radiology profession--and healthcare in general--remains to be seen. It will just take time and experimentation with machine learning, some say. Keith Dreyer, DO, likens the machine learning revolution to the promulgation of electricity, which originally was used simply for lighting, but eventually ushered in a host of helpful inventions--washing machines, dishwashers, air conditioners, televisions, computers--that were previously unimaginable. "Once you start to make machines think, taking data and performing predictive analytics, things will happen that are beyond human capability and current imagination," said Dreyer, vice chairman of radiology at Massachusetts General Hospital and associate professor of Radiology at Harvard Medical School. "So if you could predict a group of patients that are likely to have a positive CT of the brain before it was performed, think of the advantage that would be."

Take a look at a list of Google's 144 latest acquisitions and you'll notice most seem to have little in common. The seemingly random pattern of buyouts has a number of technology experts wondering just what the company is up to. This week Google bought SlickLogin, an Israeli startup that uses high-frequency sound waves as the basis of a smart-ID login system. A few weeks ago Google bought Nest, a company that develops thermostats and smoke alarms that are connected to the internet. In December its target was a slew of robotics companies, leading many to jovially question whether or not the company plans to develop an army of robots.