Introduction
In a remote village, an AI-driven tool is diagnosing illnesses, offering hope. Tommy went to the local urgent-care facility for pain that he was experiencing in his abdomen area. The doctor there was unsure what Tommy might have had so he inputted Tommy’s symptoms into the AI tool. The Doctor, with the help of AI, diagnosed Tommy with a digestional infection, prescribed an antibiotic, and sent him on his way. However, the pain persisted, and ended up increasing so much that Tommy had to be rushed to the ER. It turned out that Tommy had appendicitis and needed a life-saving surgery to remove his appendix. The village’s trust is now shaken when it comes to the use of AI.
Explanation
The doctor at the urgent-care did what he thought would be best for Tommy. However, he did not take into account that Tommy’s symptoms could be attributed to a number of things, and instead of going over other possibilities or consulting other sources, he relied on the solution presented by AI. AI, especially language models, are trained on a variety of sources all across the internet and beyond and, unfortunately, this means that they could be learning misinformation or misleading medical advice. Clearly there needed to be human oversight in this diagnosis.
AI in Medical Diagnostics
Artificial Intelligence technologies that are developed for use in the medical field are designed to detect diseases, predict patient outcomes, analyze vast amounts of medical data, and in some instances, assist in the diagnosis and treatment process. Since AI is capable of analyzing data quickly and precisely, scientists hope that it can help with early disease diagnosis and increase access to medical expertise around the world, but the use of AI in medicine still faces many practical and ethical challenges.
The History
One of the earliest and most significant uses of AI in medicine was the development of “expert systems” in the 1970’s. An expert system is a rule-based computer program designed to emulate the knowledge and decision-making processes of human medical experts. A prime example is MYCIN- developed at Stanford University in the early 1970’s. MYCIN used AI to diagnose bacterial infections, such as meningitis and bloodstream Infections, and recommend appropriate antibiotic treatment based on patient symptoms and lab results. While MYCIN and similar systems, like INTERNEST-1 (Developed at the University of Pittsburgh in 1971 for internal medicine) showed impressive capabilities at the time, their clinical use was limited due to computational constraints, a lack of extensive data for training, and concerns about the liability of using limited rule-based logic in complex medical scenarios. There were too many variables that could not be accounted for on a case-by-case basis.
Another milestone for AI in medicine was in 1992 when AI was first used to assist radiology. More commonly known as Computer-Aided Detection (CAD), AI was being used to detect microcalcifications in mammography. This paved the way for future imaging applications. These early efforts, while basic by today’s standards, demonstrated the potential for AI to assist with human expertise.
Many of the limitations preventing AI from being widely accepted in the medical world were overcome in the early 2000’s with the advent of deep learning and major advancements in machine learning. This, coupled with the now large database of Electronic Health Records (EHRs) allowed AI to move past rigid rule-based logic and learn complex patterns from vast amounts of medical data. Increases in computational power and the availability of large datasets were a key development in AI, aiding in its ability to analyze EHRs, help with disease detection, predict patient outcomes, and streamline administrative tasks.
The Future of AI in Medicine
Today, AI is being rapidly integrated into various facets of medical practice. In diagnostics, AI algorithms analyze medical images (X-rays, MRIs, CT scans) with remarkable speed and accuracy, often detecting subtle anomalies that might be missed by the human eye. AI is also revolutionizing drug discovery, accelerating the identification of new drug candidates, predicting drug-target interactions, and optimizing drug development processes. AI is enhancing personalized medicine by analyzing genomic data and patient profiles to tailor treatments, improving clinical trial efficiency, and powering robotic-assisted surgeries for greater precision.
As with most AI applications, human oversight is crucial. AI in healthcare raises important ethical concerns including data privacy, consent, bias training, and the possibility of over-reliance by medical practitioners. Balancing the benefits of AI with ethical considerations will be imperative in ensuring the effectiveness of AI in healthcare as well as the responsibility of doctors everywhere.
