11. MAKING SMARTPHONE SELFIES INTO POWERFUL DIAGNOSTIC TOOLS
Continuing the theme of harnessing the power of portable devices, experts believe that images taken from smartphones and other consumer-grade sources will be an important supplement to clinical quality imaging – especially in underserved populations or developing nations.
The quality of cell phone cameras is increasing every year, and can produce images that are viable for analysis by artificial intelligence algorithms. Dermatology and ophthalmology are early beneficiaries of this trend.
Researchers in the United Kingdom have even developed a tool that identifies developmental diseases by analyzing images of a child’s face. The algorithm can detect discrete features, such as a child’s jaw line, eye and nose placement, and other attributes that might indicate a craniofacial abnormality. Currently, the tool can match the ordinary images to more than 90 disorders to provide clinical decision support.
“The majority of the population is equipped with pocket-sized, powerful devices that have a lot of different sensors built in,” said Hadi Shafiee, PhD, Director of the Laboratory of Micro/Nanomedicine and Digital Health at BWH.
“This is a great opportunity for us. Almost every major player in the industry has started to build AI software and hardware into their devices. That’s not a coincidence. Every day in our digital world, we generate more than 2.5 million terabytes of data. In cell phones, the manufacturers believe they can use that data with AI to provide much more personalized and faster and smarter services.”
Using smartphones to collect images of eyes, skin lesions, wounds, infections, medications, or other subjects may be able to help underserved areas cope with a shortage of specialists while reducing the time-to-diagnosis for certain complaints.
“There is something big happening,” said Shafiee. “We can leverage that opportunity to address some of the important problems with have in disease management at the point of care.”
12. REVOLUTIONIZING CLINICAL DECISION MAKING WITH ARTIFICIAL INTELLIGENCE AT THE BEDSIDE
As the healthcare industry shifts away from fee-for-service, so too is it moving further and further from reactive care. Getting ahead of chronic diseases, costly acute events, and sudden deterioration is the goal of every provider – and reimbursement structures are finally allowing them to develop the processes that will enable proactive, predictive interventions.
Artificial intelligence will provide much of the bedrock for that evolution by powering predictive analytics and clinical decision support tools that clue providers in to problems long before they might otherwise recognize the need to act.
AI can provide earlier warnings for conditions like seizures or sepsis, which often require intensive analysis of highly complex datasets.
Machine learning can also help support decisions around whether or not to continue care for critically ill patients, such as those who have entered a coma after cardiac arrest, says Brandon Westover, MD, PhD, Director of the MGH Clinical Data Animation Center.
Typically, providers must visually inspect EEG data from these patients, he explained. The process is time-consuming and subjective, and the results may vary with the skill and experience of the individual clinician.
“In these patients, trends might be slowly evolving,” he said. “Sometimes when we’re looking to see if someone is recovering, we take the data from ten seconds of monitoring at a time. But trying to see if it changed from ten seconds of data taken 24 hours ago is like trying to look if your hair is growing longer.”
“But if you have an AI algorithm and lots and lots of data from many patients, it’s easier to match up what you’re seeing to long term patterns and maybe detect subtle improvements that would impact your decisions around care.”
Leveraging AI for clinical decision support, risk scoring, and early alerting is one of the most promising areas of development for this revolutionary approach to data analysis.
By powering a new generation of tools and systems that make clinicians more aware of nuances, more efficient when delivering care, and more likely to get ahead of developing problems, AI will usher in a new era of clinical quality and exciting breakthroughs in patient care.