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Earlier this month the University of Nottingham published a study in PloSOne about a new artificial intelligence model that uses machine learning to predict the risk of premature death, using banked health data (on age and lifestyle factors) from Brits aged 40 to 69. This study comes months after a joint study between UC San Francisco, Stanford, and Google, which reported results of machine-learning-based data mining of electronic health records to assess the likelihood that a patient would die in hospital. One goal of both studies was to assess how this information might help clinicians decide which patients might most benefit from intervention.
Amitha Kalaichandran, M.H.S., M.D., is a resident physician based in Ottawa, Canada. Follow her on Twitter at @DrAmithaMD.
FDA - AI - Health - Care - Call
The FDA is also looking at how AI will be used in health care and posted a call earlier this month for a regulatory framework for AI in medical care. As the conversation around artificial intelligence and medicine progresses, it is clear we must have specific oversight around the role of AI in determining and predicting death.
There are a few reasons for this. To start, researchers and scientists have flagged concerns about bias creeping into AI. As Eric Topol, physician and author of the book Deep Medicine: Artificial Intelligence in Healthcare, puts it, the challenge of biases in machine learning originate from the "neural inputs" embedded within the algorithm, which may include human biases. And even though researchers are talking about the problem, issues remain. Case in point: The launch of a new Stanford institute for AI a few weeks ago came under scrutiny for its lack of ethnic diversity.
Study - Impact - Biases - Physicians - Patients
In 2017 a study challenged the impact of these biases, finding that while physicians may implicitly prefer white patients, it may not affect their clinical decision-making. However it was an outlier in...
(Excerpt) Read more at: WIRED
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