5. A MUSICAL MILESTONE
And if we want to collect health data when no one’s wearing a device? Engineers at MIT have modified a WiFi-like box so it can capture vital signs and sleep patterns of several people in the same residence.
As new sensing technologies emerge, they’ll yield more biomedical data and insights—and these can be paired with growing stores of genomic data. In combination, they’ll lead us to new ways to optimize wellness, understand disease, and select the most patient-specific preventives and interventions.
The widening array of digital tools paired with AI analytics almost certainly will boost diagnosticians’ accuracy and speed, improving disease detection at early stages and thus raising the odds of successful treatment or cure. Many likely will be phone-based.
With smartphone otoscopes, parents can look in kids’ ears and share the view with a pediatrician. Apps and sensors can enable a phone to take electrocardiograms to check for dangerous arrhythmias; software and a microphone can equip it to “listen” to a cough and diagnose pneumonia. To improve treatment of hypertension—a leading risk factor associated with early death—sensors now in development would take continuous blood pressure readings (no cuff needed).
6. A PATCH THAT READS DEEP
Some technologies dramatically enhance the accuracy and speed of clinicians’ efforts. Identifying a bacterial or viral infection, and the best drugs to treat it, can mean long waits for blood cultures. But scientists have developed biochips that can do a complete microbial scan in a couple of hours, without culturing—and in the process may identify mutations that make some microbes antibiotic resistant.
The boom in research into the human microbiome—the trillions of bacteria on and in each individual’s body—is encouraging new modes of diagnosis and increasing understanding. Genetic analysis could help unlock the many secrets of the gut microbiome, believed to play a role in the risk and development of obesity, inflammatory bowel disease, cardiovascular disease, and even neurologic conditions.
Thanks to artificial intelligence and machine learning, diagnostic tools can be trained to read tissue samples and radiologic scans. Google researchers fed more than a quarter-million patients’ retinal scans into algorithms that recognize patterns—and the technology “learned” to spot which patterns predict a patient has high blood pressure or is at increased risk for heart attack or stroke. In some comparisons, digital tools produced more accurate analyses than did human pathologists, dermatologists, or radiologists.