In the field of medicine, artificial intelligence (AI) is most useful in assisting physicians and care providers in drawing conclusions from data collected in the past. When dealing with large sets of data, statistical methods of analyzing patterns have traditionally been used to pull useful information from the data. Modern AI uses techniques involving machine learning to find associations and relationships inside data that have not been apparent through previous methods.
Through computing power, AI can make intuitive statements about data similar to those made by clinicians; however, AI systems can analyze large sets of inputs as opposed to a single patient at a time. AI also provides a method by which the analytic process is improved with each application of new data sets to the process. In this article, Dr. Joel Arun Sursas discusses some of the current trends in the use of artificial intelligence technology in medicine.
The Power of AI to Support Physicians is Increasing
It is crucial that when meeting with patients, physicians reinforce that AI is not a replacement for human physicians. Instead, it is a tool designed to support the medical decision-making process for both doctors and patients.
Patients are often suspicious of computers or machines that lack compassion or the ability to relate to individuals; however, providers can show the power of medical AI tools to narrow down variables and improve human decision making through careful and detailed communication. When patients understand that the final medical opinion is the conclusion reached by the doctor, trust in the underlying technology will increase.
AI Will Continue to Use Computational Power to Perform Well-Defined Routine Tasks
Some medical tasks have clearly defined inputs and outputs that are readily subject to validation. Output decisions made by physicians are typically binary in nature and are based on routine and predictable analysis. For example, cancer classification of specific samples involves a binary output–benign or malignant. When the input is a digital image, studies have shown that AI has far superior sensory and discrimination power than physicians. Furthermore, with AI, results are consistent and provided quickly.
Many routine tasks are being adapted to AI processing for more efficient and reliable testing. AI will also improve routine record keeping and preparation of clinical summaries and supporting documents. Doctors can and should use AI to enhance the bonds they have with patients while improving patient trust in rapidly evolving technology.
AI is Bringing Better Healthcare Outcomes to Poorly Served Communities
Since AI reduces the time spent on routine clerical work and eliminates many simple errors, the technology is of great benefit in communities where human resources are most limited.
Particularly in developing countries, the availability of care providers and other services is a significant problem. AI can support medical services that are likely to be delayed or denied otherwise. For example, a single AI system in a poor area with high rates of tuberculosis can manage radiographic interpretations with high rates of sensitivity and specificity for large populations. AI can also support triage systems in understaffed locations and can significantly reduce waiting times for basic care.
Modeling AI Processes Will Continue to Grow and Adapt
AI and machine learning will always require modifications of inputs and models to remain responsive to human needs. Models that understand how to manage and classify data will continue to evolve through human ingenuity and modifications. The process of raw data collection for inputs into AI processes will also continue to improve. The data that is captured and digitized immediately is more reliable and useful in machine learning development.
About Dr. Joel Arun Sursas
Dr. Joel Arun Sursas is a Medical Doctor and Health Informatician and is dedicated to solving technological and administrative problems in healthcare. He is most passionate about Medical Informatics, working to bridge the gap between doctors and engineers to improve patient care. His interest in the field emerged when he began working as a Project Officer for PACES – the Patient Care Enhancement System for Singapore Armed Forces (SAF). He is intensely interested and involved in the application of technological advances to improve patient outcomes.
References
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- Guo J, Li B. The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Health Equity. 2018;2(1):174-181. Published 2018 Aug
