Your next doctor could very well be a bot. And bots, or automated programs, are likely to play a key role in finding cures for some of the most difficult-to-treat diseases and conditions.
Artificial intelligence is rapidly moving into healthcare, led by some of the biggest technology companies and emerging startups using it to diagnose and respond to a raft of conditions.
Consider these examples:
California researchers detected cardiac arrhythmia with 97 percent accuracy on wearers of an Apple Watch with the AI-based Cardiogram application, opening up early treatment options to avert strokes.
Scientists from Harvard and the University of Vermont developed a machine learning tool – a type of AI that enables computers to learn without being explicitly programmed – to better identify depression by studying Instagram posts, suggesting “new avenues for early screening and detection of mental illness.”
Researchers from Britain’s University of Nottingham created an algorithm that predicted heart attacks better than doctors using conventional guidelines.
While technology has always played a role in medical care, a wave of investment from Silicon Valley and a flood of data from connected devices appear to be spurring innovation.
AI is better known in the tech field for uses such as autonomous driving, or defeating experts in the board game go.
But it can also be used to glean new insights from existing data such as electronic health records and lab tests, says Narges Razavian, a professor at New York University’s Langone School of Medicine who led a research project on predictive analytics for more than 100 medical conditions.
NYU researchers analyzed medical and lab records to accurately predict the onset of dozens of diseases and conditions including Type 2 diabetes, heart or kidney failure and stroke. The project developed software now used at NYU which may be deployed at other medical facilities.
Google’s DeepMind division is using artificial intelligence to help doctors analyze tissue samples to determine the likelihood that breast and other cancers will spread, and develop the best radiotherapy treatments.
Microsoft, Intel and other tech giants are also working with researchers to sort through data with AI to better understand and treat lung, breast and other types of cancer.
Google parent Alphabet’s life sciences unit Verily has joined Apple in releasing a smartwatch for studies including one to identify patterns in the progression of Parkinson’s disease. Amazon, meanwhile, offers medical advice through applications on its voice-activated artificial assistant Alexa.
IBM has been focusing on these issues with its Watson Health unit, which uses “cognitive computing” to help understand cancer and other diseases.
It is not just major tech companies that are moving into health. Research firm CB Insights this year identified 106 digital health startups applying machine learning and predictive analytics “to reduce drug discovery times, provide virtual assistance to patients, and diagnose ailments by processing medical images.”
Maryland-based startup Insilico Medicine uses “deep learning” to shorten drug testing and approval times, down from the current 10 to 15 years. Insilico is working on drugs for amyotrophic lateral sclerosis (ALS), cancer and age-related diseases, aiming to develop personalized treatments.
Artificial intelligence is also increasingly seen as a means for detecting depression and other mental illnesses, by spotting patterns that may not be obvious, even to professionals.
A research paper by Florida State University’s Jessica Ribeiro found it can predict with 80 to 90 percent accuracy whether someone will attempt suicide as far off as two years into the future.
Facebook uses AI as part of a test project to prevent suicides by analyzing social network posts.
And San Francisco’s Woebot Labs this month debuted on Facebook Messenger what it dubs the first chatbot offering “cognitive behavioral therapy” online – partly as a way to reach people wary of the social stigma of seeking mental healthcare.
Boston-based startup FDNA uses facial recognition technology matched against a database associated with over 8,000 rare diseases and genetic disorders, sharing data and insights with medical centers in 129 countries via its Face2Gene application.