Artificial Intelligence and the Future of Medicine

After all, we have come to an age in which you can order some medications right from the Internet and have them delivered to your door. You can Skype call real live doctors to ask serious medical questions and get advice. Those who have intense anxiety and are apprehensive about leaving their home can video-chat with their therapist and help themselves.

Indeed, It's Come A Long Way, And the Best Part Is, We're Just Getting Started.

Artificial Intelligence, or AI, has several advantages over traditional methods of decision making in a clinical setting. Algorithms of the learning variety can become more refined and precise-and also more accurate-as they interact with data for training, allowing human healthcare providers to gain huge insight into care processes, diagnostics, treatment variability, and patient outcomes.

In this article, we will take a look at some ways that medicine is changing for the better thanks to AI. After all, these technological breakthroughs could help somebody avoid illness and certain causes of death.

A New Gen: Radiology Tools

Radiological images that are taken via MRI, CT, and x-ray give us a non-invasive look at how the human body works on the inside. However, many of these still rely upon taking tissue samples that are done through biopsy, which carry many risks such as the potential of infection.

But new AI will enable the next generation of radiology equipment that are accurate and detailed enough to negate the need for tissue samples in some instances, say experts.

Succeeding in this difficult venture may allow clinicians to create a more accurate picture of how tumors behave on the whole, instead of basing a treatment decision or decisions on the properties of a small portion of the malignant tumor.

Providers may also be able to define more accurately the aggressive nature of cancer and target treatment methods in a more appropriate way.

AI is helping to make possible "virtual biopsies" and make advances in the field of radiomics. These focus on taking image-based algorithms to characterize the genetic properties and the phenotypes of tumors.

Knowing how to better treat tumors can help reduce deaths. Imagine celebrities such as Paul Newman or Patrick Swayze whose deaths were cancer's fault.

Helping Underprivileged Regions

In underprivileged areas, there are shortages of trained healthcare workers, such as ultrasound techs and radiologists. This limits access to care that can save lives in developing countries.

There are more radiologists in one area of Boston, MA than there are in all of West Africa. However, AI can come to the rescue and prevent death. It can do this by lessening the impacts of these deficits of qualified medical workers by taking over some of these diagnostic duties that are usually performed by humans.

To provide an example, AI imaging devices can examine chest x-rays for signs of disease like tuberculosis, and their accuracy is comparable to humans. This could be done through an app that would be made available to providers in underprivileged and low-resourced regions, reducing the need for a diagnostic radiologist.

This could be a huge game-changer in helping those who need it most. But at the same time, developers of this algorithm need to be careful about taking into account the physiological and environmental factors that certain ethnic groups possess. How a disease affects people in Egypt may be different than the way it affects people in Russia, for example.

Data should represent a diversity of disease presentations and populations-not focus its attention on one ethnic group or region, says one doctor.

Making Medical Devices Smart

Certainly, you are impressed when you visit a hospital at the machinery and how it keeps everything running smoothly, and how your medical professionals handle it with ease.

However, what if that tech could be improved with some of the same magic that you find in your tablets and smartphones?

Smart devices have already taken over our lives, whether we like it or not. And even though the debate about whether they enhance our lives has been done to death, they may just be your doctor's next helper.

Smart devices are critical to monitor patients who are in ICU and elsewhere. Using AI could enhance the ability to sense deterioration in a patient, indicate whether or not sepsis is taking over, or even detect developments of complications that must be addressed early on.

On the issue of sepsis, renowned American radio DJ Casey Kasem's death was because of this affliction-could having this technology have saved him?

Integrating disparate data from across the entire healthcare system is hard to do as a human. Now try generating an alert based upon all that data you've processed. It's hard as a human-but an algorithm inside a smart device can reduce the amount of cognitive work that doctors have to do, leaving them to focus more on other aspects of patient care.

Your Health Records-As A Predictor

EHRs are a golden resource for doctors and other healthcare professionals as a place where patient data can be analyzed. However, extracting and making sense of that info in a manner that is quick, accurate, and reliable is a challenge.

Issues like the quality of data and integrity, plus the mixture of data formats, inputs that are structured and unstructured, and records that are incomplete make it hard to understand just how to partake in meaningful risk stratification, clinical decision support, and predictive analytics.

It will be hard, however, to integrate the data into one place. One doctor reported that while an algorithm might be able to predict a stroke or even depression, a closer look might reveal it is actually predicting a billing code for such an affliction.

Strides have been made. EHR analytics have actually produced a good number of successful risk scoring and stratification items, especially when researchers can use deep learning techniques to find novel connections between datasets that are not related.

However, the biggest obstacle will be making sure those algorithms do not confirm biases that are hidden the data. This will be crucial for sending out took that will really improve clinical care.

Diagnose Your...Selfie?

Earlier, we talked about the use of smart devices and how they may help us. And now, experts believe that images that are taken right from our smartphones and other consumer-grade devices will be a big helper in clinical-level imaging-especially in the underprivileged regions that need it most.

The quality of cell phone cameras is huge nowadays and continues to improve. The images that are produced are good enough to be analyzed by AI algorithms. Fields that are already benefiting from this are dermatology and ophthalmology.

Researchers in the UK have even created a tool that helps identify developmental ailments by analyzing the images of a child's face. The algorithm can detect features that might otherwise go unnoticed, like a jawline, eye, and nose placement, and other features that might point to a craniofacial abnormality. As of right now, the tool can match the ordinary images to over 90 different disorders to provide support to doctors and other healthcare professionals.

After all, the vast majority of the population owns a smartphone, and they often feature a lot of sensors. The opportunities these devices present are great, and every major cell phone manufacturer has already integrated AI into their devices.

Using smartphones to gather images of eyes, wounds, infections, skin lesions, or other ailments may be able to help those in underprivileged areas deal with a shortage of medical professionals while reducing the time it takes to be diagnosed.

Assisting with Antibiotic Resistance

Antibiotic resistance presents a growing threat to populations everywhere due to their overuse. These organisms, or superbugs as they are called, wreak havoc on hospital and result in thousands of deaths.

Just in the USA alone, the bacterial infection Clostridium difficile is responsible for $5 billion in annual costs and claims more than 30,000 lives per year. It is easy to spread once a person is infected, and usually comes as a result of people with dirty hands. It is also caused by not washing one's hands before preparing food and eating. The death rate of this particular disease increased from the years 2000-2007 due to its growing resistance to antibiotics.

Electronic health record information can aid in identifying infection patterns and highlight patients who might be at risk before they even show symptoms. Utilizing machine learning and AI tools to produce these analytics increases their accuracy and creates faster, more reliable alerts for healthcare professionals.

Many hospitals have a wealth of EHR data they can use in a very helpful way. To not do so could mean even more deaths that take place due to these antibiotic resistant superbugs.

In Closing

While some of us view AI as a scary product often found in science-fiction movies, the truth of the matter is that it may very well end up saving somebody we care about, even ourselves.

Thanks to these advances in technology, modern medicine is made much more effective and even can help reduce the already heavy cognitive burden that doctors face.