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AI to healthcare aid: Improving patient care and healing lives

Healthcare is an area that promises a great potential and is witnessing the growth of AI-based and machine learning technologies. Patient care and medical research and diagnosis are two areas that are ripe for such solutions.

Artificial intelligence to the aid of patient care

Patient care forms an integral and important part of hospitals. Apart from good doctors and facilities, how well a patient is taken care of becomes a huge differentiator. It is, however, a costly proportion for the hospitals as it involves dedicated time from nurses and other medical staff.

According to Bret Greenstein, vice president of Watson IoT platform at IBM, medical staff invests around 10% of their time answering basic questions from patients. The questions could be about lunch, visiting hours, hospital rules, doctors’ details and so on. On top of this, mundane activities such as lowering the lights, adjusting the room temperature, opening or drawing the curtains, adjusting the bed and probably a thousand other things, all add up pretty quickly to make the job of healthcare staff pretty demanding.

AI is stepping in just to take away the mundane-yet-important tasks and to free up some of the medical staff’s time. The staff could use this time more productively elsewhere.

IBM is using its Watson IoT platform to collaborate with Philadelphia’s Thomas Jefferson University Hospitals and Harman to develop smart speakers that respond to a patient’s commands. Upon hearing the patient say, “Watson, dim the lights,” the lights or other features of the room will adjust based on the command. Through voice commands, patients can also control the thermostat, ask the speakers to play soothing music and so on.

AI healthcare systems are evolving to help patients even when they have been released and went home. AI can tell much more accurately things such as how long is the patient going to recover and what is the best course of treatment for a specific patient, and can also prompt patients to walk and exercise more, and so on.

Diagnosing medical conditions with AI

It’s not just the healthcare; AI can also bring major benefits in other associated medical areas as well. Studying the vast information abundance and diagnosing specific cases quickly and as good as a human doctor does is one such area.

Still under the works, IBM’s Watson is a great effort in that direction. Watson’s intelligence hinges on the data it absorbs. It’s a giant with an insatiable appetite for data and information. Watson began as an AI that was learning to read and write, and then was making educated guesses in Jeopardy. It is now gaining a better understanding on cancer, training under 20 top cancer institutes to learn more about oncology and genomics. Watson may be an AI in training, but it can read as much as 25 million papers in a week. Around 8,000 new research papers are published every day — something impossible for doctors to go through even over several months.

AI doesn’t just picks up the information fast, it also learns to make the appropriate analysis based on the information it has absorbed, just like a medical student. A test was done to see if Watson would conclude the same genetic mutations as the Molecular Tumor Board. Based on an analysis of 1,000 patients, the AI could provide the same recommendations in 99% of cases. In 30% of the patients, Watson introduced a new insight that the other physicians didn’t conclude.

IBM’s Watson isn’t the only upcoming major healthcare AI innovation. Big companies such as Apple and Google and healthcare giants like GE Healthcare have made investments in the industry, and their technologies will further bring innovative disruptions to healthcare.

Challenges and future outlook

Everything, however, is not rosy. The leading healthcare platform, IBM’s Watson, still has a long way to go before establishing unquestionable credibility. The prime reason is unavailability of data that can be fed to train Watson. Specific diseases need specific types of data along with thousands of other contextual variables to consider. Gathering such data, which is both credible and exhaustive, is a huge challenge for any machine learning technology.

The challenges are plenty, but the rewards are going to be exponential. Patients could really rely on smart AI-based assistants so that they do not need to depend on anyone to carry out basic tasks. Medical diagnosis, similarly, could help doctors a great deal by churning out useful, quality advice in time.

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