Big Data

We All Know about AI in Medicine By Now. Here’s Why It Really Matters.


In this special guest feature, Eran Atlas, Co-Founder & CEO of DreaMed Diabetes, discusses how the rapid incorporation of artificial intelligence in medicine is no longer a novel trend, with more fields than ever developing improved solutions and protocols. Eran earned his M.Sc in Biomedical Engineering and MBA degrees at Tel Aviv University after which he was a lecturer at the Afeka College of Engineering. As an expert in biomedical engineering and algorithm development, he is responsible for commercial partnerships, leading the R&D and regulatory activities. After accomplishing the artificial pancreas closed-loop software, Eran’s focus was shifted to developing the Advisor Pro- an insulin management system enabling healthcare professionals to analyze patient data within seconds.

With more fields than ever developing improved solutions and protocols, the rapid incorporation of artificial intelligence in medicine is no longer a novel trend. Yet the intricacies of AI have often made it a difficult success story to explain to the average Joe. While AI’s huge leap forward has provided much needed clarity and assistance to medical decision-makers, its positive effects have not been as crystal clear for the general public, meaning patients and prospective patients have little clue how it helps them.

The future of standard medical practice might be here sooner than anticipated, a future in which patients could see a computer before seeing a doctor. Major strides in the field of artificial intelligence are propelling us toward days in which doctors will be treating the causes of diseases, rather than their symptoms.

Many perceptions of AI exist in our general discourse, some of them involve apocalyptic visions of the “the machines taking over,” while others are grounded a little more firmly in reality. So let’s clarify: In healthcare, artificial intelligence refers to the use of complex algorithms and software to carry out manual actions in processing complicated medical data. In plain English, AI lets machines come to conclusions without the help of humans.

It’s worth noting, however, that while AI lets machines come to conclusions, it doesn’t make medical decisions. That task is reserved for human doctors and medical experts.

Taking human error out of the equation

The evolution of artificial intelligence is still at it’s infant stages, with new technological opportunities consistently discovered, yet its ability to improve medical diagnosis and minimize error is already being displayed. Medical error in the U.S. was the third leading cause of death as of 2016, according to a BMJ report. Incomplete medical histories and large caseloads can lead to human errors that literally result in the death of patients. AI’s infrastructure keeps it somewhat immune from such variables, meaning it can predict and diagnose disease at a faster rate than most qualified medical professionals.

In one study, an AI model using algorithms and deep learning diagnosed breast cancer at a higher rate than 11 pathologists. Companies such as PathAI are developing machine learning technologies to assist pathologists in making more accurate diagnoses. The company’s current goals include reducing error in cancer diagnosis and developing methods for individualized medical treatment.

This refinement not only helps us optimize the diagnostic side of medicine, but it frees up medical professionals, otherwise engaged in mind-numbing data analysis, to refocus their efforts on other areas of treatment.

It frees things up

A 2016 study of 35,000 physician reviews revealed 96 percent of patient complaints revolve around a lack of customer service, confusion over paperwork, and negative general experiences. New innovations in AI healthcare technology are streamlining the patient experience, helping hospitals process millions of data points much more efficiently. For example, Olive’s AI platform is designed to automate the healthcare industry’s most repetitive tasks, freeing up administrators to work on higher-level ones.

The platform automates eligibility checks, un-adjudicated claims, and data migrations, so staff can focus on patient service. It integrates within a hospital’s existing software, eliminating the need for costly integrations. Providing a seamless patient experience is not just confined to ensuring the patient is effectively looked after, it also means allowing hospitals, clinics, and physicians to treat a greater number of patients every day. This streamlining of the patient experience will play a significant part in elevating a medical institution’s capacity, helping it avoid a potential backlog of deferred appointments.

Taking the tedious out of data

While it might not be the most glamorous side to AI’s repertoire, its ability to process medical data will act as one of medicine’s most immediate advantages. Valuable information can, and occasionally does, get lost among a sea of data points, losing the industry around $100 billion a year. Some in healthcare have started turning to artificial intelligence as a way to stop the data hemorrhaging. That’s because AI helps break down data silos to connect information in minutes, something that used to take years.

For instance, KenSci combines big data with AI to predict clinical, financial, and operational risk by taking data from existing sources to foretell everything from who might get sick to what’s driving up a hospital’s healthcare costs. Companies such as Proscia are using AI to detect patterns in cancer cells. The company’s software helps pathology labs eliminate bottlenecks in data management and uses AI-powered image analysis to connect data points that support cancer discovery and treatment. The pattern is clear: AI will eventually move past simply managing data to a point at which it can be actionably re-utilized.

It would be easy for the average person to underestimate what can truly be achieved through AI. The more we digitize and unify our medical data, the more we can utilize AI to help us discover valuable patterns used to make accurate, cost-effective decisions in the complex and diverse field of medical processes. What’s more, the more AI is utilized consistently in medicine, the more our medical processes will depend on them to maintain an increasingly optimized patient experience. As the renowned 20th century visionary Richard Buckminster Fuller once proclaimed, “the more we learn, the more we realize how little we know.” With AI, the more we develop, the more we recognize how much more there is to come.

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