The Evolution of Artificial Intelligence in the Medical Field

Artificial intelligence, or the mimicking of human cognitive functions using computer technology, is shifting the way certain industries function and reshaping the future. In medicine, the paradigm shift is commencing with leveraging the power of AI to analyze structured and unstructured data in a way that allows medical professionals to make better decisions regarding treatment options, recently published research, and advanced diagnosis.

Furthermore, doctors can use the predictive power of the technology to improve early detection and evaluation. Although AI has much room for growth and improvement, the medical industry is already using certain mechanisms, including image interpretation in detecting early-stage breast cancer.

How does AI advance medicine?

Any artificial intelligence system is only as good as the quality and quantity of its data. Therefore, these systems must be trained with as much information as possible. As they progress, the machines are fed additional data and their algorithms begin to detect patterns. Essentially, AI continues to learn with presented examples. In medicine, these examples include screenings, treatments, diagnosis, research studies, and other sources of data.

Unstructured sources, including clinical notes, further extend the analytical capabilities. Much of the advancements in this type of technology stem from diagnoses where imaging, genetic testing, and other forms of active knowledge help analyze large volumes of information that are difficult to perform without the use of advanced systems.

Types of AI

Specifically, in the healthcare industry, most experts classify AI devices into two main categories. Machine learning (ML), which is a subset of the technology that helps computers develop learning using repetition and new information, is the first category. The second category is processes and methods that extract information from unstructured data and develop meaning from such supplemental data. This technique is called natural language processing (NPL). Machine learning relies heavily on NPL as these define data in a way that ML can interpret and implement the information.

One of the first artificial intelligence machines that medical professionals used in the industry was IBM’s Watson. The machine has proven worthy in oncology and serves as another opinion that doctors rely on to determine the best course of treatment, among other important decisions. While the technology suffered its fair share of implementation issues, its evolution has demonstrated that when used as one of the many tools in a medical professional’s arsenal, it can improve accuracy and results. Furthermore, other companies such as Ezra AI have developed tools like an MRI for prostate cancer screening that is less invasive and helps detect prostate cancer more accurately and without pain.

One of the potential areas of evolution for AI tools is in empowering the patient. Meaning, with more powerful mechanisms to diagnose, it may be possible for patients to evaluate their symptoms. This is especially important for those suffering from chronic conditions. The sense of independence and improvement in the quality of life can shift the balance of power toward those suffering from the debilitating conditions. This area, however, is still in its infancy and has a long way to go to be considered possible.

Challenges and Future

Much of the challenges that artificial intelligence faces revolve around governance and regulation, including data and privacy protection. Furthermore, questions in the realm of transparency and accountability in the use of the technology also surface. As with most technology, laws will generally fall behind and catch up as the tools become more mainstream. Nevertheless, AI will continue to progress and will evolve into a wider range of tools that doctors and medical practitioners can use to serve patients’ interests. While ethics and philosophical debates remain, there is no question that the impact will expand to other sectors of the economy.