The last few years, there’s been a real boom when it comes to machine learning in healthcare applications.
AI in healthcare totaled nearly $1.44 billion in the first six months of 2019, revealing a need, demand, and great promise in the beneficial link between machine learning and healthcare. In fact, McKinsey has estimated that AI in the health and wellness sphere doesn’t just make money, it saves money—as much as $269.4 billion annually. (The largest savings is found in service operations, marketing/sales as well as risks.)
When it comes to artificial intelligence, its exciting future often takes the spotlight with astounding predictions of advanced technological developments and nearly science fiction-esque solutions. However, a spotlight should also be shined on the current practices of ML in healthcare, many of which we encounter every day at the doctors office. Specifically, this is evidenced via smart health records, drug discovery and manufacturing as well as medical imaging diagnosis.
Let’s explore how these applications work and why they’re important to further understand what’s on the horizon for AI and ML in healthcare.
Smart Health Records
Electronic health records, otherwise known as EHR, is a means of maintaining up-to-date records that saves time, energy and money. Smart records aid in diagnosis, treatment, the release and storing of lab results, in addition to helping you keep track of appointments. Healow is a common EHR application with other platforms like eClinicalWorks, Epic, McKesson, Allscripts, Care360, almost all of which are Cloud-based.
Recent breakthroughs at institutions like MIT have come up with classification methods through vendor machines and ML-based OCR recognition. At the end of the day, EHRs are becoming a new standard in the medical field, as they eliminate the need for paper files and make life much easier for doctors, administrators and patients. EHRs are especially useful when seeing multiple practitioners and specialists so everyone can be on the “same virtual page” regarding treatment, medication and diagnosis.
Smart health records are collaborative tools that over 90% of doctors utilize to save time and money. Their use has more than doubled with doctors’ offices since 2008, concluding they are practically a commonplace a staple in the healthcare field.
Drug Discovery & Manufacturing
Machine learning in healthcare has also been evidenced in drug discovery and manufacturing. When it comes to the early stages of the drug discovery process, particularly in areas such as target validation, identification of prognostic biomarkers and analysis of digital pathology data in clinical trials.
BioSymetrics, for example, utilizes AI when processing raw phenotypic, imaging, drug and genomic data sets because it enhances care and facilitates faster data-driven decisions. Another company, Data2Discovery, harnesses AI to find hard-to-see connections as well as new insights in diverse, linked datasets. By connecting data in innovative ways, this helps treat illnesses and better understand disease.
Ultimately, machine learning facilitates biochemical and pharmaceutical companies to access large quantities of data that lead to better processes as well as outcomes. This is particularly true when it comes to collective data on chemistry and drug effectiveness.
The growth of machine learning with pharmaceuticals and medicine is so promising that McKinsey actually predicted it could generate up to $100 billion annually.
Medical Imaging & Diagnosis
Medical imaging consists of non-invasive methods of taking a look in the body to help determine causes of injury or disease with the goal of confirming a diagnosis. Medical imaging can also be used to track how well someone is responding to an illness. Machine learning comes into play in that it helps facilitate a better comprehension of medical image interpretation, specifically in AI applications. A large use of machine learning in medical imaging is seen through radiology.
Radiology has even seen a boost in AI applications. One study revealed that when put against 101 radiologists in identifying and rating cancers from mammograms, the AI application outperformed all except the most experienced radiologists. Even in the dementia and Alzheimer’s space AI is making great strides. A study in San Francisco created an algorithm to make a highly probable predication through PET scans as to which patients have dementia, Alzheimer’s or mild cognitive impairment. Clearly, sophisticated machine learning algorithms can improve and enrich the likelihood of an accurate diagnosis.
In the first half of 2019, the FDA approved 26 AI applications in healthcare alone, many of which are intended to assist radiologists in their work. Mount Sinai has even launched a first-of-its-kind medical imaging and artificial intelligence center called the Biomedical Engineering and Imaging Institute, solely committed to AI, imaging, robotics and nano medicine.
With the current use of ML and AI in healthcare, many patients might feel that computers and smart technology might replace the role of doctors. However, it’s apparent that these applications are skillfully aiding and assisting doctors in treating chronic diseases, not to mention support a better handling of patient data. With rising insurance and healthcare costs, institutions are looking to make cost-cutting measures, proving that AI and ML can not only get you closer to an accurate diagnosis—but also save you money.
This year at DATAx we’ll hone in on AI and machine learning, specifically how smart technology and elements of artificial intelligence can give way to optimal results. Featuring an impressive lineup of accomplished executive speakers from Mount Sinai Health, GlaxoSmithKline, Memorial Sloan Kettering Cancer Center, Rally Health and more you’ll hear firsthand how all of this affects your practice and business. You won’t just leave inspired, you’ll leave catapulted forward in your focus and career. Please use code HEALTH500 when you register to get $500 off your ticket to attend.
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