Of all the domains AI is being used on, healthcare is the one where a visible difference will make a lot of difference.
AI, given the data it is being fed and with help of machine learning can recognise patterns much early on.
For hospitals, AI can bring in the much needed cost saving and they can deploy the budget to other areas.
But then, how real is AI’s impact on healthcare? Let’s check out this collection.
Looking beyond images, however, even today’s best AI struggles to make sense of complex medical information. And encoding a human doctor’s expertise in software turns out to be a very tricky proposition.
IBM has learned these painful lessons in the marketplace, as the world watched.
While the company isn’t giving up on its moon shot, its launch failures have shown technologists and physicians alike just how difficult it is to build an AI doctor.
While it is a well established fact that movement in ICU patients can accelerate their healing and reduce muscle atrophy and delirium, keeping a track their movements at a scale was not possible this far.
The exercise was limited to human monitoring by Nurse's who were already burdened with high-pressure job of an IUE attendant.
Now with the help of depth sensors and the three-dimensional silhouette data which they generate 24 hours, putting a vision algorithm to work on it, the progress of patients can be monitored on large scale.
While accuracy of algorithm currently varies between 68 to 87%, it can be improved with installation of additional sensors.
Technology once promised that world would be more open and connected, but the truth of the matter is, 15 years hence, people are living lives which have never been more lonelier.
Real life interactions are drastically limited and face time happens only through five inches screens.
This is increasing the loneliness among all genre of people, and age is not a filter either.
In a surprise study, it has been found that people are more open to AI chatbots, to reveal their darkest secrets, rather than their close human beings and not even doctors. And this helping them overcome their anxieties and survive depression.
“Some think it’s a breakthrough. Others are skeptical it’ll help. But there’s such an absurd mismatch between what we need to support people’s mental health conditions and what’s available. So if this does work — and it looks promising this could be a vital step forward to helping more people.”
The name Mabu is short for mabutaki, a Japanese word meaning to blink and mabudachi, or best friend.
Mabu is a personal healthcare companion, an intelligent and socially interactive robot, which could be programmed to the needs of individual patients needs.
Though she isn’t mobile, but she can make eye contact while carrying on a conversation with someone and is also capable of simple gestures with her head and eyes. She holds a tablet-like screen in front of her that she uses during conversations to convey additional information.
She can reside with the patient and at routine intervals interact to determine the health status, prompt reminders to take medicines and even exercises.
She is designed to converse like a human, which is very essential in post-operative health care management, where the patient might feel lonely and desires more care and attention.
Without the need of doctor being present on site, Mabu helps the doctors keep track on the patients well being, through her.
Greg Corrado, Co-founder of Google Brain and Principal Scientist at Google has been in his earlier avatar, was a neuroscientist.
He explains how essentially, the Machine Learning algorithms essentially play the 'imitation game' in order to learn and better themselves and how they have the power to increase the healing powers of doctors manifold, which has not happened in last hundred of years.
And that should come, as doctors embrace AI.
Machine Learning algorithms devoid of any prejudices and biases, are proving to be not only to be 'early detectors', but far better than what humans are at it.
These algorithms are changing the paradigms of 'preventive medicine'.
Patterns are everywhere in our lives, and so do they in diseases as well. But sometimes due to inherent human biases and prejudices, while so readily evident, they are often ignored or missed by even the finest of the medical professionals.
An intervention at later stages, often means that even best of efforts might prove futile in saving the precious life.
A great lecture / talk which starts from describing AI in a layman's language and then gravitates towards, how the recent advances in the field is taking the discipline of medicine to a radically new dimension.
AI applied to field of medicine has the most promising potential to be 'good'.