Machine learning and artificial intelligence concept. Man suit hand holding Ai chipsets and blue tone of automate wireless Robot arm in smart factory background
Machine learning and artificial intelligence concept. Man suit hand holding Ai chipsets and blue tone of automate wireless Robot arm in smart factory background (Photo Credit: www.shutterstock.com)

by Ben Dickson

As artificial intelligence takes leaps at an increasing pace, there’s fear that robots will conquer the future and put humans out of jobs. While technological unemployment is a worry that has its own merits, there are some fields where there is already a shortage of human experts.

One of those fields is healthcare. It takes years of education and hands-on experience to train a decent doctor. And as the figures show, there’s a shortage of physicians everywhere.

Fortunately, Artificial Intelligence and Machine Learning can help alleviate this problem. AI-powered tools can help healthcare workers and doctors improve precision and efficiency while lowering skill and energy requirements. This can effectively put more people into healthcare jobs while also bringing services to a larger number of people, especially in underserved areas.

Have your own personal doctor

Internet of Things (IoT) is one of the biggest—and least credited—enablers of AI. Wearables are gradually becoming more commonplace. These gadgets produce a wealth of health data about their owners.

Healthcare organizations can use this data—within defined legal boundaries of course—to create rich electronic health records of patients. The same data can power AI-based health assistants applications that can use unsupervised learning to set a baseline for a patient’s vital signs. Whenever they observe outlier or abnormal activity, the assistants can notify the patients or their doctors.

Assistants can also provide users with targeted nutrition and fitness tips, steering them toward a healthier life in small, incremental steps.

However, with health data classified as sensitive information, its storage and security will present challenges. Companies will have to make sure they do not step legal lines when handling user information.

Find patterns in health data

Machine learning algorithms identify patterns across millions of data points, patterns that would take humans forever to find. This can be a boon to the healthcare sector. Using ML algorithms,  doctors and researchers can find health patterns at different levels.

For instance, when a patient visits the doctor with specific symptoms, an ML algorithm can quickly scan his personal and family health records for similar patterns and come up with suggestions. This can help reduce visit times and relieve doctors of having to rely on their own memory and personal experience.

On the broader scale, unsupervised learning algorithms can find secret patterns in health records for millions of patients. Supervised learning, on the other hand can provide a different kind of assistance. Providing tagged resources such as X Rays, MRIs and skin sample images can enable algorithms to perform diagnosis that is on par—or more precise—as top health experts.

The scheme can help in early detection and prevention of dangerous diseases such as cancer as well as rare diseases, and possibly help save lives by sounding the alarm before the patient reaches the point of no return.

Again, we’re walking a fine line here. UK’s Royal Free NHS trust ran into trouble last year after sharing patient record information with Google DeepMind. There’s a lot that we don’t know yet about the legal implication of this practice and how it can impact patient privacy.

Providing better care

Artificial Intelligence can also find patterns in the treatment process to guide doctors in better caring for patients. By looking into the outcome and data from past treatments, machine learning algorithms can provide success guidelines and recommendations that can increase survival rates.

AI algorithms and assistants can also help provide continuous and self-care to patients that need constant attention. This is especially useful in mental healthcare, where constant monitoring and analysis of patient mood can help doctors devise more successful strategies that are specific to patients.

Further down the road, AI-powered applications can help doctors control patient habits such as medication, sleep periods, diet, etc. Healthcare bots can communicate with patients on behalf of doctors and assist them by giving them information and reminders and providing feedback and information to doctors. AI trends such as Natural Language Generation and Processing (NLG/NLP) can help make that experience more natural and organic.

Where are we headed?

We’re still really seeing the first glimmers of AI in healthcare. A lot more lies ahead. But we’re closing in at an accelerating pace. Some believe it takes humans to care for humans, and healthcare will forever remain the domain of human beings. For the moment, they have the right of it. As things look artificial intelligence is proving to be an ingenious complement and assistant to doctors, not a replacement. How long before AI starts making decisions instead of suggestions? The future will show us.

This article was originally published on Tech Talks. Read the original article here.

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