How Artificial Intelligence (AI) will cause an Evolution in Classical Medicine and Healthcare?
A major breakthrough of Artificial Intelligence aka AI in medicine and healthcare is the next big thing to happen (or, rather happening already) that is capable of taking medical prediction, prognosis, diagnosis, and treatment to the next level, and help us shift towards a more efficient preventive care approach in the long run.
Before we jump into outlining the prospects of AI in medicine, let me talk a bit about AI in general and how are these algorithms considered to be intelligent in the first place!
What is Artificial Intelligence (AI)?
Artificial Intelligence refers to the capability inferred to a computer program to mimic human behavior in some way which aims at making our lives easier by achieving higher precision and speed in massive tiresome computational tasks, and by incorporating smart automation in the workflows.
But, just hard-coded algorithms or fixed, rule-based systems don’t work very well for achieving such goals. The solution turned out to be not just mimicking human behavior but mimicking how humans learn. Here comes the terms “machine learning” and “deep learning”.
Machine learning is a subset of AI, and it consists of the techniques that enable computers to figure out patterns from training datasets and deliver AI applications, capable of analyzing test datasets. Basically, machine learning allows machines to go through a learning process. It does this by developing foundational models to solve problems. The machine learning algorithm alters the model every time it combs through the data and finds new patterns. This approach enables learning and provides increasingly accurate outputs. Machine learning can be achieved through several ways, like supervised, unsupervised, semi-supervised or reinforced learning. Maybe I’ll be covering these topics in more details in some of my future blogs.
You might have come across another term called “deep learning”. Now what the heck is that? Deep learning, meanwhile, is a subset of machine learning that enables computers to solve more complex problems, imitating the workings of the human brain in processing data and creating patterns for use in decision making. The deep learning algorithms are capable of reacting to input data by modifying and adapting themselves gradually over the time. In simple terms, deep learning is all about using neural networks with more neurons, layers, and inter-connectivity. We’re still a long way off from mimicking the human brain in all its complexity, but we’re moving in that direction quite fast.
It’s worth mentioning that AI is not something new. Artificial intelligence as an academic discipline was founded in 1956. The goal then, as now, was to get computers to perform tasks regarded as uniquely human: things that required intelligence. It’s the remarkable progress in the field of AI in the last couple of years that has made these terms more popular recently. Also, research on AI in the field of medicine had started quite a few decades back. Stanford has been doing AI in medicine since the early 80’s and it was one of the first sites to have a supercomputer for AI in medicine. But some factors over the last couple of years has accelerated the advancement in this field quite a lot. Advances in computational power paired with massive amounts of healthcare data in electronic form being generated in healthcare systems has given an incentive and has made it imperative that we have to do something with these data to improve the quality and value of care that we provide.
What makes an Algorithm Intelligent?
Doctors are educated through years of medical schooling, doing assignments and practical exams, receiving grades, and learning from mistakes. Much in the same way, AI algorithms are fed with data to train them, the trained models are run on test data, their performances are graded, and the algorithms learn from the mistakes.
There are many different algorithms that can learn from data. Most applications of AI in medicine read in some type of data, either numerical (such as heart rate or blood pressure) or image-based (such as MRI scans or Images of Biopsy Tissue Samples) as an input, which is typically structured, meaning that each data point has a label or annotation that is recognizable to the algorithm. The algorithms then learn from the data and churn out either a probability or a classification. For example, the actionable result could be the probability of having an arterial clot given heart rate and blood pressure data, or the labeling of an imaged tissue sample as cancerous or non-cancerous. After the algorithm is exposed to enough sets of data points and their labels, the performance is assessed to ensure accuracy, just like exams are conducted to assess medical students. This process involves the input of test data to which programmers already know the results, allowing them to assess the algorithm’s ability to determine the correct result. Based on the performance, the algorithm can be modified, fed more data, or rolled out to be applied in clinical environments.
Approaching ‘Precision Medicine’ through AI
Classical medical practice puts large groups of people in their focus and tries to develop clinical solutions, and hence the treatment protocols or drugs developed thereof are usually based on the needs of the statistical average person.
As everyone has a different genetic code, a different lifestyle, exposed to different environmental conditions and medications, they may react differently to a particular pharmaceutics or may have a completely opposite reaction to treatment as assumed. Here’s where the concept of ‘Precision medicine’ comes into play.
Now, as we are in an age where masses of data can be collected and analyzed very quickly, personalizing treatment can become more feasible by means of machine learning and deep learning AI algorithms. So, AI will most likely help healthcare move from traditional, ‘one-size-fits-all’ medical solutions towards targeted treatments, personalized therapies, and uniquely composed drugs.
From Reactive Diagnostic Approach to Predictive Prognosis
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. Medical treatment aided by AI technologies may predict, and therefore impact patients differently, based on their existing health conditions.
Using these types of advanced analytics, we can provide better information to doctors at the point of patient care. For an instance, when a doctor sees a patient, having easy access to the blood pressure and other vital signs is routine and expected. Now, imagine how much more useful it would have been if the doctor could be presented with the patient’s risk for stroke, coronary artery disease, and kidney failure based on the last 50 blood pressure readings, lab test results, race, gender, family history, socioeconomic status. We can develop and train such AI models based on clinical findings, trends, and outcomes of other patients.
AI algorithms can handle even those minute and apparently irrelevant clinical/diagnostic aspects of patients that might predict a disease the individual can contract in maybe 10 years of time. All we need is huge sets of databases of patients and development of competent AI algorithms. Already many such AI models have been developed to predict several diseases and healthcare issues with much accuracy, enabling the individual to adopt precautionary treatment/lifestyle early enough to avoid falling victim to it in future. I’ll write in details about some of these technologies in my future blogs. As of now, you can get to know more about these technologies visiting my YouTube playlist containing some insightful videos on this topic: http://bit.ly/AI-in-Medicine-YTPlaylist
The value of machine learning algorithms in healthcare is their ability to process huge datasets beyond the scope of human capability, and then through their analytical capabilities, reliably convert that data into clinical insights that aid physicians in planning and providing care, ultimately leading to better outcomes and increased patient satisfaction.
We need to advance more information to clinicians so that they can make better decisions about patient diagnoses and choose just the right treatment option, while understanding the possible outcomes and cost for each one.
Challenges faced in Developments of AI Solutions in Healthcare
As I’ve said above, predictive analytics powered by AI truly has a vast potential in healthcare, but it lags behind other industries by years; largely because most of these endeavors aimed at developing AI technology are expensive one-off models requiring many data scientists to write and test the algorithms behind the technology.
Another factor limiting the usefulness of machine learning is that healthcare data is far more complex than data in other industries, and difficult to aggregate. Machine learning algorithms are only as good as the quantity and quality of data that we feed them. More well-organized the data is, easier for the algorithms to analyze them; and more voluminous the data, more is the accuracy and precision of the trained model.
Advancements in electronic medical records have been remarkable, but the information they provide is not much better than the old paper charts they replaced. If technology is to improve care in the future, then the electronic information provided to doctors needs to be enhanced by the power of analytics and machine learning. We have to use the EHR (Electronic Health Records) from across the different health institutions and convert them into well organized datasets enabling them to be implemented later in training AI models. But this is by far the biggest challenge in the field of AI in medicine. But thanks to recent EHR standards like FHIR (pronounced “Fire”) being implemented, which can result in better interoperability, availability, and ease of exchange of healthcare data for research purposes in the field of AI.
What does AI mean for the Future of Doctors? Should we be skeptical?
Medicine is inherently a human enterprise, and that empathy of human element of caring for another person is not something that an algorithm can reproduce.
Writing for Stat News, Jack Stockert points out that whilst the use of AI on its own may increase efficiency, pairing with AI also improves human performance. He states, “this hybrid model of humans and machines working together presents a scalable automation paradigm for medicine, one that creates new tasks and roles for essential medical and technology professionals, increasing the capabilities of the entire field as we move forward.”
While it’s quite unlikely that machines will replace or eradicate the need for human doctors any time soon, I think, those who are already pursuing or are considering a medical profession should be willing to adapt, learn and grow alongside technological advancements. Bryan Vartabedian, again writing for Stat News, sums it up, “I think my profession is headed to evolution, not extinction.”
Recent Applications of AI in Medicine
We are in the early stages of applying machine learning to healthcare. AI powered predictive analytics to project whether patients have sepsis earlier as well as predicting breast cancer recurrence based on medical records and imaging are some of the areas where we have already seen the prospects of AI technology. AI has a promising future potential in many other applications involving medical imaging, diagnostics for diabetic retinopathy, and predictive analytics based on medical records data.
Within the next couple of years, AI will most probably analyze big medical data sets, draw conclusions, find new correlations based on existing precedence and support a doctor’s job in decision-making. Several companies are already working in exploring and advancing the immense potential of AI for mining medical records (Google Deepmind and IBM Watson), identifying therapies (Zephyr Health), supporting radiology (Enlitic, Arterys, 3Scan) or genomics (Deep Genomics). Another one worth mentioning is Atomwise, which uses supercomputers that root out therapies from a database of molecular structures. In 2015, Atomwise launched a virtual search for safe, existing medicines that could be redesigned to treat the Ebola virus. They found two drugs predicted by the company’s AI technology which may significantly reduce Ebola infectivity. This analysis, which typically would have taken months or years, was completed in less than one day.
In the near future, Artificial Intelligence is going to emerge as being more useful in predictive analytics, making the diagnostic process more efficient, more accurate and more cost effective, by constructing thorough, yet concise systems of patient interrogation, integrating exam findings, analyzing available diagnostic data, and selecting evidence-based, cost effective treatment solutions.
Although we still seem to be far from algorithms independently operating in clinics, especially given the lack of a clear pathway for clinical approval, in the short term, these algorithms can be used by doctors to assist with double-checking their diagnoses and interpreting patient data faster without sacrificing accuracy. In the long term, however, government approved algorithms could function independently in the clinic, allowing doctors to focus on cases that computers cannot solve. I think, we shouldn’t be skeptical in terms of accepting and implementing AI technologies in healthcare, rather we should aim at achieving a balance between the effective use of technology and AI and the human strengths and judgement of trained medical professionals, thus enabling proper utilization of both human and technical resources.
Thanks for reading through this post. Do let me know what is your opinion regarding the developments and implementation of AI technologies in medicine in the comments section. It’s The OffBeat Doc signing off. ✌
I’ve curated a YouTube playlist adding some of the insightful videos on this topic. Here’s the link to it: http://bit.ly/AI-in-Medicine-YTPlaylist
