Despite the significant media attention, AI-powered solutions have only taken tiny efforts toward tackling important concerns and have yet to make a meaningful overall impact on the global healthcare industry.
Fremont, CA: For decades, artificial intelligence has played a crucial role in various industries. Artificial (AI) has just lately begun to play a significant role in healthcare. AI systems are expected to be a $6 billion business by 2021, according to Frost & Sullivan. According to a recent McKinsey report, healthcare will be one of the top five industries, with more than 50 AI use cases and over $1 billion in venture capital already raised. What does this mean for your company with such rapid expansion? What are the best ways to make the most of this game-changing technology?
AI is proving to be a game-changer in the healthcare industry in a variety of ways. Here are a few examples that are currently in use:
Drug Discovery: Artificial intelligence (AI) technologies are being developed to uncover new possible remedies from enormous databases of information on existing drugs that could be altered to combat important dangers like the Ebola virus. This could boost drug development efficiency and success rates, speeding up the process of bringing new treatments to market in response to fatal disease threats.
Radiology: Image processing and diagnosis are being automated using AI solutions in radiology. This can aid a radiologist in highlighting regions of interest on a scan, increasing efficiency and reducing human error. Fully automated methods – which read and interpret a scan without human intervention – may also be possible, allowing for immediate interpretation in underserved areas or after hours. Recent demonstrations of enhanced tumor identification on MRIs and CTs demonstrate progress toward novel cancer prevention prospects. Meanwhile, an AI-powered platform for analyzing and interpreting Cardiac MRI images has already gotten FDA approval in the United States.
Patient Risk Identification: By analyzing large volumes of historical patient data, AI technologies can assist physicians in identifying at-risk patients in real time. Re-admission risks are a current focus, with patients who have a higher possibility of returning to the hospital within 30 days following discharge being highlighted. Currently, a number of companies and health systems are working on solutions based on data from the patient's electronic health record, prompted in part by payers' reluctance to cover the costs of re-admission. Other recent research has shown that a still image of a patient's retina can be used to predict the risk of cardiovascular disease.
Primary Care/Triage: Several organizations are developing direct-to-patient solutions for triaging and providing guidance via voice or chat. This allows for quick and scalable answers to basic questions and medical concerns. This might help people avoid unnecessary visits to the doctor, lessening the strain on primary care doctors – and, for a subset of illnesses, providing basic advice that would otherwise be unavailable to people living in rural or underserved areas. While the principle is evident, independent validation is still required to demonstrate patient safety and efficacy.