Several aspects of the health care system involve prediction, including diagnosis, treatment, administration, and operations. So it’s not surprising that artificial intelligence (AI) is a growing force in the Healthcare & Life Sciences industries. AI is aiding in earlier detection of diseases, more consistent analysis of medical data, and increasing access to care, particularly for underserved populations.
From improving medical diagnoses to AI powered wearables, the adoption of AI in healthcare is advancing medical treatment, patient experiences, and helping clinics and hospitals run more efficiently. In this article, we’ll look at some of the applications and companies leading innovation in healthcare and life sciences.
Artificial Intelligence in Improving Medical Diagnosis
Medical errors are one of the leading causes of death in the United States. From incomplete medical records to natural human error, one of AI’s most exciting healthcare applications is helping to improve the diagnostic process. In fact, according to an MIT study, 75% of medical staff who have AI agree that it has enabled better predictions in the treatment of disease. Here are a couple of application examples that are able to predict and diagnose diseases quickly using the power of AI.
The healthtech startup Regard uses AI technology to diagnose patients. The company describes its automated system to be the “clinical co-pilot” to electronic medical records (EMRs). The data from EMRs is synthesized to discover a diagnosis. Additionally, healthcare providers receive specific recommendations about patient care. The system also updates patient documents automatically to reduce burnout among healthcare workers. For a look into how Regard’s AI co-pilot works, check out this demo video.
Enlitic develops deep learning medical tools to streamline radiology diagnoses. The company’s deep learning platform analyzes unstructured medical data — radiology images, blood tests, EKGs, genomics, patient medical history — to give doctors better insight into a patient’s real-time needs. Checkout this video that dives a little deeper.
Did you know that there are over 100 different types of cancer and the biological signals indicating the presence of those cancers can vary widely? Freenome uses AI in screenings, diagnostic tests and blood work to test for cancer. The Freenome platform consists of three powerful components (below). By deploying AI at general screenings, Freenome aims to detect cancer in its earliest stages and subsequently develop new treatments.
Artificial Intelligence in Clinical Trials Pharmaceuticals
Testing new drugs is a slow and expensive process. Around 80% of clinical trials fail to meet enrollment timelines, and around one-third of Phase III clinical studies are terminated because of enrollment difficulties. Artificial intelligence-powered technology has the potential to change every stage of the clinical trials process, from finding a trial to patient enrollment to medication adherence. Here are three companies applying AI to revolutionize the fragmented clinical trials process.
Deep 6 AI uses artificial intelligence and natural language processing on clinical data to find eligible patients for clinical trials. Its software accelerates patient recruitment and getting life-saving treatment to patients. Their technology creates dynamic, holistic patient views — what they call patient graphs. These graphs provide broader clinical context and truly allow patient characteristics to be precision matched to trial criteria, resulting in fewer false positives and less wasted time.
Owkin leverages AI technology for drug discovery and diagnostics with the goal of enhancing cancer treatment. The company’s AI tools help identify new drug targets, recommend possible drug combinations and suggest additional diseases that a drug can be repurposed to treat. Owkin also produces RlapsRisk, a diagnostic tool for assessing a breast cancer patient’s risk of relapse, and MSIntuit, a tool that assists with screening for colorectal cancer. Here’s a video with the Owkin Chief R&D Officer, Jean-Philippe Vert, explaining how Owkin uses cutting-edge causal AI and patient data to advance precision medicine for patients.
RlapsRisk™ BC from Owkin, is an AI diagnostic to help pathologists and oncologists determine the right treatment pathway for early breast cancer patients. Above is an example of a report produced by RlapsRisk™ BC.
Artificial Intelligence In Improving the Patient Experience
There’s no question that the COVID-19 pandemic greatly accelerated the use of digital health in areas such as public health surveillance and virtual care, highlighting some of the many ways in which digital health can strengthen and enhance health care planning and delivery. The ability to provide an efficient patient experience, especially online, enables clinics and hospitals to treat as many patients as they can. Here are two examples of how AI is helping better manage patient flow and care.
Walgreens
Walgreens leverages artificial intelligence (AI) in both internal- and external-facing areas of its enterprise operations. Their Digitate AIOps solution helped to streamline both the COVID-19 vaccine scheduling process for customers and helped manage the tech support tickets from its IT department. One of the challenges for Walgreens during the height of the pandemic was making sure they could provide the right information to their customers as to which pharmacies would be open. “Predicting or telling our customers when a store would be open and when the pharmacy would be available in the store suddenly became more difficult. The usage of the AIOps solution was really about making that information available in real-time. As we were learning the situation on the ground across all 9,000 stores, this information about stores and hours of operation could be updated in a timely fashion,” Yael Gomez, VP, Global IT, Integration and Intelligent Automation at Walgreens.
Mayo Clinic
The Mayo Clinic is applying AI to electrocardiogram (ECG) data captured by smartwatches. They developed an iPhone app that uses ECG data to identify patients with weak heart pumps. “The ongoing AI research in cardiology is part of Mayo’s commitment to bringing a digital transformation to health care. Advanced diagnostics that once required travel to a clinic can be accurately done, as this Apple Watch ECG study demonstrates, from a patient’s wrist whether they live in Brazil or Baton Rouge. App-based access to a medical center can help address health disparities by making high-level diagnostics accessible to more people in real time,” says Bradley Leibovich, M.D., medical director of Mayo Clinic’s Center for Digital Health.
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