Human health

The ways that AI can be applied in health science is almost limitless. We will discuss a few of them here, but it will by no means be an exhaustive list.

Drug discovery

AI can assist in generating drug candidates (that is, molecules to be tested for medical application) and then quickly eliminating some of them using constraint satisfaction or experiment simulation. We will learn more about constraint satisfaction programming in later chapters. In a nutshell, this approach allows us to speed up drug discovery by quickly generating millions of possible drug candidates and just as quickly rejecting them if the candidates do not satisfy certain predetermined constraints.

In addition, in some cases we can simulate experiments in the computer that otherwise would be much more expensive to perform in real life.

Furthermore, in some instances researchers still conduct real-world experiments but rely on robots to perform the experiments and speed up the process with them. These emerging fields are dubbed high throughput screening (HTS) and virtual high throughput screening (VHTS).

Machine learning is starting to be used more and more to enhance clinical trials. The consulting company of Accenture has developed a tool called intelligent clinical trials (ITP). It is used to predict the length of clinical trials.

Another approach that can surprisingly be used is to apply to drug discovery is Natural Language Processing (NLP). Genomic data can be represented using a string of letters and the NLP techniques can be used to process or "understand" what the genomic sequences mean.

Insurance pricing

Machine learning algorithms can be used to better price insurance by more accurately predicting how much will be spent on a patient, how good a driver an individual is, or how long a person will live.

As an example, the young.ai project from Insilico Medicine can predict with some accuracy how long someone will live from a blood sample and a photograph. The blood sample provides 21 biomarkers such as cholesterol level, inflammation markers, hemoglobin counts and albumin level that are used as input to a machine learning model. Other inputs into the model are ethnicity and age, as well as a photograph of the person.

Interestingly, as of now, anyone can use this service for free by visiting young.ai (https://young.ai) and providing the required information.

Patient diagnosis

Doctors can make better diagnosis on their patients and be more productive in their practice by using sophisticated rules engines and machine learning. As an example, in a recent study at the University of California in San Diego conducted by Kang Zhang [1], one system could diagnose children's illnesses with a higher degree of accuracy than junior pediatricians. The system was able to diagnose the following diseases with a degree of accuracy of between 90% and 97%:

• Glandular fever
• Roseola
• Influenza
• Chicken pox
• Hand, foot, and mouth disease

The input dataset consisted of medical records from 1.3 million children visits to the doctor from the Guangzhou region in China between 2016 and 2017.

Medical imaging interpretation

Medical imaging data is a complex and rich source of information about patients. CAT scans, MRIs, and X-rays contain information that is otherwise unavailable. There is a shortage of radiologists and clinicians that can interpret them. Getting results from these images can sometimes take days and can sometimes be misinterpreted. Recent studies have found that machine learning models can perform just as well, if not better, than their human counterparts.

Data scientists have developed AI enabled platforms that can interpret MRI scans and radiological images in a matter of minutes instead of days and with a higher degree of accuracy when compared with traditional methods.

Perhaps surprisingly, far from being concerned, leaders from the American College for Radiology see the advent of AI as a valuable tool for physicians. In order to foster further development in the field, the American College for Radiology Data Science Institute (ACR DSI) released several AI use cases in medical imaging and plans to continue releasing more.

Psychiatric analysis

An hour-long session with a psychiatrist can costs hundreds of dollars. We are on the cusp of being able to simulate the behavior with AI chatbots. At the very least, these bots will be able to offer follow-up care from the sessions with the psychiatrist and help with a patient's care between doctor's visits.

One early example of an automated counselor is Eliza. It was developed in 1966 by Joseph Weizenbaum. It allows users to have a "conversation" with the computer mimicking a Rogerian psychotherapist. Remarkably, Eliza feels natural, but its code is only a few hundred lines and it doesn't really use much AI at its core.

A more recent and advanced example is Ellie. Ellie was created by the Institute for Creative Technologies at the University of Southern California. It helps with the treatment of people with depression or post-traumatic stress disorder. Ellie is a virtual therapist (she appears on screen), responds to emotional cues, nods affirmatively when appropriate and shifts in her seat. She can sense 66 points on a person's face and use these inputs to read a person's emotional state. One of Ellie's secrets is that she is obviously not human and that makes people feel less judged and more comfortable opening up to her.

Smart health records

Medicine is notorious for being a laggard in moving to electronic records. Data science provides a variety of methods to streamline the capture of patient data including OCR, handwriting recognition, voice to text capture, and real-time reading and analysis of patient's vital signs. It is not hard to imagine a future coming soon where this information can be analyzed in real-time by AI engines to take decisions such as adjusting body glucose levels, administering a medicine, or summoning medical help because a health problem is imminent.

Disease detection and prediction

The human genome is the ultimate dataset. At some point soon, we will be able to use the human genome as input to machine learning models and be able to detect and predict a wide variety of diseases and conditions using this vast dataset.

Using genomic datasets as an input in machine learning is an exciting area that is evolving rapidly and will revolutionize medicine and health care.

The human genome contains over 3 billion base pairs. We are making progress on two fronts that will accelerate progress:

• Continuous advancements in the understanding of genome biology
• Advances in big data computing to process vast amounts of data faster

There is much research applying deep learning to the field of genomics. Although it is still in early stages, deep learning in genomics has the potential to inform fields including:

• Functional genomics
• Oncology
• Population genetics
• Clinical genetics
• Crop yield improvement
• Epidemiology and public health
• Evolutionary and phylogenetic analysis