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AI: Shaping the Future of Healthcare

Healthcare stands out as one of the least digitized economic sectors and a bona fide latecomer to the opportunity of automation. While other industries have made great strides leveraging the power of automation everywhere from the factory floor to the living room floor, healthcare had seemed content to remain in a state of complacency, with extremely low productivity gains in the past decade. Even as robot-assisted surgery was making headlines, the rest of the healthcare supply chain seemed stuck in a pre-AI (artificial intelligence) world. Today, all that is changing fast. With advancements in machine learning, sensing, vision, and computing power has come a new era of healthcare—one in which AI touches every point of the patient journey to literally transform healthcare as we know it. From therapeutic and diagnostic tools, to patient treatment support, drug discovery, and more, this new wave of change will completely transform the healthcare industry and ultimately achieve its greatest goals: better patient care and better patient outcomes.

For investors, this is a massive opportunity. Here’s just a snapshot of the advancements that are either here today or just around the corner:

Medical Imaging

Lung cancer is the leading cause of cancer-related deaths in the US, where more than 150,000 people die from the disease every year. While there are many types of diagnostic imaging tests in use today, including X-rays, CT scans, MRIs, and echocardiograms, improving the effectiveness of early detection and treatment of the disease is imperative. In recent years, radiologists have begun using CT and MRI scans as early screening methods, but both tests are less than perfect, delivering both too many false positives and too many false negatives, which can delay the proper treatment of the disease.

Advancements in AI-enabled medical imaging are changing that scenario. High-speed computer processing has driven a massive leap forward in the capabilities and quality of medical imaging. Rather than relying on radiologists alone to view and analyze hundreds of scans to try to detect patterns and subtle changes in tissue imaging with the naked eye, deep learning algorithms now support their efforts, quickly comparing each 3D image with hundreds of thousands of images in a vast database to help detect and categorize even the smallest lesions. Recent research has shown that when AI and pathologists reviewed images of lymph node cells in tandem, error rates dropped from 3.5% to just 0.5%. It is this type of AI assistance that can not only support better patient outcomes, but also free up the practitioners’ time to focus on the task of interpreting findings and guiding effective treatment options for patients. It’s a change that has the power to change outcomes and save lives.

Cancer

Curing cancer of all types is clearly a top priority, and advancements in machine learning and gene editing may well be the key to finding the answers researchers have been seeking for decades. Our genetics determine nearly everything about our physical bodies, including any predispositions to illnesses such as cancer, cystic fibrosis, Huntington’s disease, sickle cell anemia, and myriad rare diseases. Machine learning and the knowledge of genetics it provides is helping researchers better comprehend, predict, and even change the function of our genes.

Machine learning is used to identify patterns within high-volume data sets of genetic information. AI tools are used to translate these patterns into computer models that may help predict an individual’s probability of developing certain diseases, as well as identify which targeted therapies are likely to deliver the best outcome for the individual. It may also hold the key to—finally—curing cancer with the help of gene editing.

Gene editing is an emerging area that involves making specific alterations to DNA at the cellular or organism level. One promising gene-editing technology, CRISPR, is a faster and less expensive way of conducting gene editing. However, CRISPR requires researchers to first select the appropriate target sequence—a task that currently involves a long process with many choices and unpredictable outcomes. Machine learning solutions by companies like Illumina promise to address that challenge by significantly reducing the time, cost, and effort necessary to identify an appropriate target sequence. The hope is that this capability will ultimately lead to the discovery of a breakthrough treatment for various types of cancer. Research firm Frost & Sullivan predicts that AI applications in healthcare and gene editing will be responsible for $6.7B in revenue opportunity by 2021.

Using AI to analyze genetic data is also being used in direct-to-consumer genomic sequencing services. Services like 23andme and Ancestry use machine learning to offer consumers in-depth interpretation of genetic information to help identify how an individual’s genes may impact fertility, health, weight, and other areas of their health.

Creating a Smarter Healthcare System with AI

Across the healthcare landscape, practitioners, administrators, and patients alike are looking to AI to improve the overall efficiency of the clinical workflow process. Long delays at doctors’ offices, hospitals, and urgent care centers are often caused by inefficiencies in gathering and sharing patient histories and other important data. Once again, machine learning may hold the answers. Veeva is designing an advanced data analytic platform that integrates AI capabilities into the clinical workflow process. Elekta has a solution that enables oncologists to offer remote cancer monitoring treatment and patient care. Teladoc makes it possible for patients to enjoy a “virtual doctor’s visit” via a mobile phone app. Cyrcadia Health provides AI-enabled bras for early detection of breast cancer. Other wearable monitoring devices meld telemedicine with AI and predictive analytics to connect patients and doctors from anywhere.

These providers (and many more) are beginning to offer solutions designed to leverage advanced technologies to improve medical care—a need that is becoming more urgent every day as the healthcare industry struggles to care for a growing aging population, as well as for disabled patients or those with chronic disease who may not have access to a physician nearby. And the more integrated these solutions become, the greater the promise for a truly efficient healthcare ecosystem in the future. Imagine a world in which patient health data is fed directly into an Electronic Health Record (EHR) that is connected to a centralized database of genomic and other clinical data. In this scenario, physicians will not only be able to diagnose and treat patients more effectively, but also have the time to provide higher quality care for each person at every visit—whether that takes place in the office or via a mobile connection.

As the healthcare system becomes smarter with the help of AI, the net result will be higher quality care, lower costs, and better value. The future of healthcare is here—and AI sits at the core of almost everything it touches.

By Lisa Chai, Senior Research Analyst, ROBO Global

 

 

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