Diagnosis/disease identification:
Medical care begins with an accurate diagnosis. Machine learning is already at the forefront, assisting leading research organizations in devising better methods of disease identification.
Image analysis for remote diagnosis:
Extended beyond diagnosis is image analysis, another promising application of ML in the field of medicine and health care. Traditional image analysis (X-rays, MRI scans, CAT scans) is time-consuming. An MIT-led team of researchers has found a solution in the form of a machine-learning algorithm that can conduct image analyses 1,000 times faster than humans, thus saving ample time in the delivery of proper medical care.
AI-powered robots assisting surgical operations:
Imagine a robot conducting a complex eye operation. Imagine a mini-robot that enters a heart patient’s chest and assists in mapping and therapy. You don’t really need to imagine, because these are realities.
Virtual nursing:
Not only can machine language-powered nursing chatbots and robots interact with patients more regularly than human nurses, but also can serve as intelligent gatekeepers of information between patients and doctors.
Automation of workflows and administration tasks:
Medical care is complex; even the most trivial of hospital visits triggers dozens of separate workflows, involving doctors, nurses, chemists, attendants, facilities managers, and more. It’s estimated that the medical industry could save up to $18 billion annually by automatic workflows and administration tasks using AI technologies.
Personalized treatments:
On a global level, there’s a clear demand-supply gap that medical institutions struggle to manage. Personalized medical care is a bit of a luxury, reserved for those who can afford it. The application of predictive analysis in disease assessment and management hardly requires any underscoring. Called supervised learning, this approach enabled physicians and doctors to select the right diagnosis from a limited original set of possibilities, based on the genetic information of the patient.
