How Machine Learning and AI is Transforming the Future of Healthcare

Featured article by Edward Roddy, Independent Technology Author

Artificial intelligence has been vexing us all for years. And it only continues to get better and better as time goes on. Sure, you will have some people who believe AI is a bad thing, or that it knows too much. However, whether we like it or not, AI is here to stay.

Imagine a machine learning technology that can bring with it the knowledge of THOUSANDS of doctors-and bringing valuable insight to a single physician who can then make a sound treatment decision.

We can change medicine for the better. And instead of replacing a trusted doctor, they will instead bring valued insight for the best care we have had in centuries. Indeed, the future looks bright with AI. Let’s examine some instances of AI and the way it is changing healthcare as we know it.

Revolutionizing Diagnostics

Have you ever felt misdiagnosed by a physician only to seek a second opinion? Misdiagnosis is real. Many famous people and celebrities died because of misdiagnosis among thousands of other people who we might not hear of. The Estelle Getty, who played the beloved character Sophie Petrillo in television’s Golden Girls, was misdiagnosed with Parkinson’s back in the mid-90s. Another misdiagnosis occurred, this time it was Alzheimer’s disease. It was not until years later she would be correctly diagnosed with Lewy Body dementia. Her death occurred in 2008.

With AI now on the scene, things will be better. Even though the field of AI-based tools for diagnosing illnesses is emerging, they are still providing valuable information that can alter a patient’s course of treatment.

For instance, did you know that these technologies are assisting pathologists and radiologists to spot lesions, aid in the diagnosis of skin cancer, and retinal ailments? The magic lays in science. Machine vision and transfer learning is taking off like a rocket. Transfer learning will enable an AI to learn another skill at a rapid rate.

Machine learning also learns by experience. For example, machine learning is geared to closely examine data that is gathered during routine healthcare as a means of identifying issues that may crop up going forward.

This is huge. This could be the ounce of prevention that helps people stay out of the healthcare system and live better lives. Medical costs could see a decrease. And when the AI is given quality data in good quantity, it can use medical data to form an accurate and helpful prognostic model.

The key would be training doctors to handle the task of collecting and giving pertinent info to AI engines. These engines would also have to be carefully vetted to ensure they are not taking monetary advantage of patients (ordering more tests, for instance) or failing to suggest conditions that do not have symptoms.

AI’s First Wave: Knowledge Engineering

The first wave of AI as we knew it was “knowledge engineering”. This was more or less programs that focused on optimization or finding solutions for issues that plagued most of us in the real world every day.

For instance, the filing of taxes was made easier by internet-based tax programs, and scheduling programs that help you create a timetable that maximizes productivity.

Even in the medical field, first wave AI has a place. You may have heard of the Framingham Risk Score Calculator, which uses AI as a means of estimating a patient’s risk of heart disease.

AI’s Second Wave: Machine Learning Programs

Machine learning programs make use of statistical probability analysis, says Dr. Bhardwaj. They use these data to conduct pattern recognition of a very advanced nature. Compared to the first-wave AI, second-wave AI is perceptive and learns, sometimes in ways that are more effective than humans.

This level of AI may be found in applications such as clinical decision support programs, and other programs that are in place to evaluate and analyze tests like echocardiograms, genetic tests and more.

However, these programs do show lots of areas in which they can improve: for instance, these programs cannot perform interpretations of deep data as well as humans can. The technology of second-wave AI is dependent upon data that is coded correctly and clean.

And while the learning ability of this wave of AI is a marvel, it remains limited in its capability to solve new problems that are lacking in comprehensive and clean datasets.

AI’s Third Wave: Putting Data into Context to Create Novel Hypothesis

We are now entering the third wave of AI. These technologies can examine data sets that are huge, can identify statistical patterns, and generate algorithms to provide explanation about the patterns.

Indeed, the potential these AI can reach is unspeakable. The true potential of these programs rests in their ability to increase the amount of data they can analyze in a meaningful way.

The programs form connections between data points that before had been unassociated and normalize the contexts of these data. This creates the chance to generate and test new hypotheses in several care scenarios.

Some companies have already begun making use of programs like this, for example Johnson & Johnson have invested serious capital into AI programs. This tech is a game changer-it can automate a task that is otherwise repetitive, teach humans and while it has a long way to go, the technology is already proving to be extremely useful.

AI Presents Challenges

The virtues of AI are easy to extoll, but there are heaps of other challenges we should be aware of as we make the leap from human-powered healthcare to a blend of AI and human.

After all, AI is only successful if it has a huge amount of data from which to learn and therefore optimize the algorithms contained inside. Gaining these data in the healthcare field presents some serious issues concerning privacy.

The privacy of the patient and data ownership ethics come into play when AI is involved. If you have ever tried to access a friend or family member’s medical records, even if it was just to help them out, you likely ran into mountains of forms that had to be filled out or perhaps you were denied access altogether.

The same issues arise when we examine the use of AI in hospitals and other healthcare facilities. There are a few questions that have to be asked and evaluated as this tech becomes more prominent in our healthcare:

– Should healthcare facilities be permitted to sell or provide large amounts of patient data to AI companies?

– What happens when a security breach occurs?

– Who owns/controls the patient data that is needed to create new AI tech?

– How will regulations like General Data Protection Regulation in Europe be impacted? This regulation allows people to delete their personal data if desired, and if not complied with, could be the result of penalties priced in the millions of dollars.

– Most importantly, how can patients still maintain privacy?

Data quality presents another bump in the road for AI in the healthcare field. For other fields, data is mostly reliable and measured in a very accurate way.

For instance, vehicles may use location and data regarding velocity as a means of predicting traffic on a certain highway.

When it comes to healthcare, however, data may be subjective and sometimes inaccurate. Some major issues include the following:

– Inaccurate data: Suppose a patient listed that they did not smoke, but was too embarrassed to admit the truth? Suppose a patient had to undergo psychiatric evaluation for a job and did not list depression as one of their symptoms?

– Sources of these data are housed among heaps of service providers. This makes it hard to get a full range and profile for the health of a patient.

– Doctor’s notations in electronic medical records are hard to process and understand at times, not to mention unstructured.

Aside from all this, we must look at the human element of all this technology. Will patients be willing to adopt this type of care?

Many of the things we do in our daily lives are becoming automated and not everybody cares for it. Will doctors themselves adopt these treatment methods? The future is bright for medical AI, but only if the world decides to move with it and integrate it into our current healthcare practices.

Conclusion

While there are a wide range of issues that must be overcome, and a higher rate of chronic diseases than before, the need is present for innovations in healthcare.

AI has made healthcare much easier in some ways, but still has some issues that must be worked out as it is implemented. If we can find a way to use this treatment ethically and get doctors and patients on board, the future will surely look brigh