Patient satisfaction with the United States healthcare system is diminishing, particularly in terms of affordability, accessibility and overall health outcomes. According to a survey from The Harris Poll, over 50% of adults would grade the overall system as a “C” or lower.
Artificial intelligence (AI) programs have been steadily integrated into the medical field since the late 1970s when an early consultant tool was introduced to help medical professionals with disease management planning. This is most visible today with the use of remote patient monitoring via wearable medical devices, such as smartwatches and glucose monitors, and telehealth chatbot assessments, which saw a rise during the COVID-19 pandemic. Further developments and open integration of more sophisticated resources could drastically transform the practice.
Small, simple steps toward greater adoption of AI in healthcare today have begun with the automation of clerical and administrative tasks, which greatly boost productivity. When AI is used, the time it takes to fill out a patient chart goes from sixteen to just four minutes, and the technology is able to capture a wider scope of information.
AI being used for basic telehealth and clerical purposes has profoundly affected overall operations and accessibility. Medical staff can allocate more time to direct patient care tasks and can now reach a wider breadth of individuals. This ranges from being able to see more patients in a day to expanding access to those in different areas of the country without close medical availability.
AI is currently also being used to better decipher medical imaging for diagnosing and preparing for surgical planning. UCLA Medical Center oncologist Wayne Brisbane is a proponent of such, illustrating how he frequently uses it to assist with more effective prostate cancer treatment. He notes, “Prostate cancer is a tricky disease. It’s like an octopus or crab. There’s a big body, but it’s got these little tentacles that extend out. And so figuring out the margins of where you’re going to do this lumpectomy or treatment can be difficult.” This is just one of many examples of how AI’s precision skills have been applied to facilitate greater health outcomes.
Furthermore, once AI systems become more widely utilized and their databases are able to accrue more patient data, the medical field will be able to greatly expand early detection rates and provide risk prevention models to patients demonstrating the greatest need.
In an American Medical Association survey of over 1,000 practicing physicians, Al was found to be already effectively “enhancing diagnostic ability (72%), workflow efficiency (69%) and clinical outcomes (61%).” Continued adoption of this evolving technology will not come to replace medical providers, as many individuals fear, but rather serve as smart assisting tools that amplify the industry’s life-saving capabilities.
The American Medical Association found that AI is already effectively “enhancing diagnostic ability (72%), workflow efficiency (69%) and clinical outcomes (61%).”
There are legitimate concerns about the continued development and adoption of AI tools, which can be alleviated through the continued allowance of experimental industry development and the creation of issue-specific rules addressing harms that have arisen with a demonstrable need for intervention. There are several methods to keep patients safe without the need for overly restrictive government intervention, including through the promotion of adequate training.
There are clearly illustrative, real-world applications of AI that have already successfully left the healthcare sector in a better position over the last several decades. Generations prior could have never foreseen the commonplace usage rates of practices like basic telehealth and AI medical image analysis today. By promoting greater innovation to further cultivate this relationship between healthcare and technology, the possibilities for vast improvement over the next several decades to come are limitless.
Image generated by NetChoice using ChatGPT’s DALL-E.