According to a recent survey, 94% of healthcare companies1 claim to use Artificial Intelligence and Machine Learning (AI/ML) in some capacity. The key to leveraging AI/ML in healthcare is data. Human health deals with vast amounts of complex datasets, from images and test results to medical history and genomic data. AI/ML can not only process a lot of complex data, but can analyze it and provide predictions or recommendations at far faster speeds than any human can. The four primary ways AI/ML is utilized in the healthcare industry are for drug discovery, detecting and diagnosing diseases, at-home care and health monitoring, and increased personalization and efficiency.
It is estimated that it takes eight years and $2 billion2 to develop one drug, which then only has a one in ten chance of being approved. AI can reduce the cost and timeline while helping to discover safer and more effective drugs with a higher likelihood of acceptance. The more drug discovery is improved, the more drugs can be created, and the more revenue a company can potentially make, adding to the valuation1 of the industry as a whole. Since an AI method for predicting a protein structure was discovered, AI/ML has been used to accelerate the drug discovery process, most notably with the development of the COVID-19 mRNA vaccine2.
AI has also been used to detect COVID-193, along with other diseases and disorders. One study4 found that AI was able to identify skin cancer better than a group of 58 dermatologists. Another group of researchers5 fed images and health data into a machine learning algorithm to diagnose autism spectrum disorder with 95% accuracy. And in 2019, a deep learning algorithm3 outperformed radiologists in detecting lung cancer. A Harvard study4 showed that using AI for diagnostics can reduce costs by 50% and improve health outcomes by 40%.
AI can also be used at home to help patients self-administer treatments and medications more effectively. One study found that 70% of patients4 don’t take insulin as prescribed. AI also makes health monitoring more convenient and accurate through various Internet of Things devices. While the first AI/ML medical device2 was approved by the FDA in 1995, 24% of medical devices1 today employ some level of AI/ML.
More than anything, AI can be used to unlock more efficient solutions for patients and doctors alike. For patients, AI can be used to optimize the search for pharmacies, insurance plans, and doctors or otherwise remove barriers1 to navigating the complicated healthcare system. For doctors, AI can help with administrative tasks, freeing up the doctor to spend more time with patients flexing the empathy5 that AI sorely lacks.
There is significant potential for the breadth of AI technologies to be adopted throughout the industry. The AI healthcare market is projected to grow4 from $11 billion in 2021 to $180 billion by 2030. Those in the best position1 to leverage AI are larger companies with the capital to procure large proprietary datasets and invest in developing and training AI models.
The TrueShares Technology, Deep Learning, and AI ETF (LRNZ) is designed to provide thematic exposure to a concentrated portfolio or technology companies, including those in the biotechnology and healthcare sectors that are particularly apt at developing or deploying AI technology in the industry. Healthcare and biotech company holdings in LRNZ utilize AI/ML for innovative drug discovery and preventative healthcare, all with far-reaching implications for the future of human health.