AI in Healthcare: Revolutionary or Reserved for the Elite
One of the most significant impacts AI can contribute to our lives is in the health care system. AI offers several advantages over traditional analytics and clinical decision-making techniques. Learning algorithms can become more precise and accurate as they interact with training data, allowing humans to gain unprecedented insights into diagnostics, care processes, treatment variability, and patient outcomes. Often, a patient can present multiple symptoms that can correlate with various genetic and physical characteristics, which can delay a diagnosis. So, not only does AI benefit a practitioner in terms of efficiency, it provides both quantitative and qualitative data based on input feedback, improving accuracy in early detection, diagnosis, treatment plan, and outcome prediction. AI’s ability to “learn” from the data provides the opportunity for improved accuracy based on feedback responses. This feedback includes many back-end database sources, input from practitioners, doctors, and research institutions. The AI systems in healthcare are always working in real-time, which means the data is continually updating, thus increasing accuracy and relevance. Assembled data is a compilation of different medical notes, electronic recordings from medical devices, laboratory images, physical examinations, and various demographics. With this compilation of endlessly updating information, practitioners have almost unlimited resources to improve their treatment capabilities. AI systems could free overworked doctors and reduce the risk of medical errors that may kill tens of millions of patients each year. Furthermore, in many countries with national physician shortages, such as China, AI can significantly reduce workload.
However, many people fear the benefits garnered from AI will solely help the wealthy. Unfortunately, this fear is not unfounded; if the status quo perpetuates, it is safe to assume that only the high socioeconomic status people will access the full range of possibilities for AI. A prerequisite to gaining the decision-making advantages created with AI in medicine would be to have a healthcare plan that allows for that. Accounting for the fact that 28 million Americans do not have medical insurance, around 9 percent of the population will not have access to AI benefits in healthcare. Russ Altman, professor of bioengineering, genetics, medicine, and computer science, Stanford University, writes, “AI technologies could exacerbate existing healthcare disparities and create new ones unless they are implemented to allow all patients to benefit.” In the United States, for example, people without jobs experience diverse levels of care. A two-tiered system in which only special groups or those who can pay — and not the poor — receive the benefits of advanced decision-making systems would be unjust and unfair. It is the government’s joint responsibility and those who develop the technology and support the research to ensure that AI technologies are distributed equally. Furthermore, in order for AI to be effective, it needs to draw from a large sample of data, and currently, the majority of that data will come from those who are wealthier, thus negatively impacting AI’s potential for specific communities. According to an article on STAT, results of a recent study on an algorithm designed to conclude which patients needed medical care showed that “only 18% of the patients identified by the algorithm as needing more care were black, compared to about 82% of white patients… those figures should have been about 46% and 53%, respectively.” Fortunately, this is not an unsolvable problem; if the government were to allocate a specific amount of the healthcare budget to ensuring data was collected evenly amongst all communities, the scope of AI in healthcare would be widened exponentially. This plan is one of many that have the potential to expand access to AI in healthcare, which could save millions of lives per year and revolutionize the way we live.
