Within healthcare and medicine, Artificial Intelligence (AI) research is increasing. Studies have shown that healthcare AI projects attracted more investments and funding in the last few years than other global economic sectors.

Furthermore, although there is much excitement, several individuals are still quite skeptical, and some are even urging caution due to the excessively inflated expectations. Hence, this article will look closely at trends in artificial medical intelligence and its general use and practice.

So, what is artificial medical intelligence?

Informed clinical decision-making through the use of insights derived from previously gathered data is the essence of what can be termed evidence-based medicine. Traditionally, most statistical methods have approached this task solely by characterizing available patterns within the data as mathematical equations. For instance, in the past, linear regression would suggest a line of best fit.

However, through machine learning (ML) algorithms, artificial intelligence has provided some techniques to uncover the most complex associations we cannot reduce to an equation. For instance, data is represented by neural networks through the use of vast interconnected neurons similar to the human brain.

Through this, machine learning systems can approach problem-solving by weighing evidence to reach a reasonable and logical conclusion. These systems differ because they can simultaneously process an end number of inputs. For instance, a smartphone powered by artificial intelligence can automatically triage 1.2 million people in a specific geographical location to Accident and Emergency rooms.

Additionally, most of these systems or machines have the innate capability to learn from each specific incremental case and can analyze more possibilities than a clinician can experience in several lifetimes. Hence, an AI-driven application can accurately outperform most dermatologists when required to classify suspicious skin lesions. Similarly, AI is entrusted with tasks where field experts often disagree, like when needed to identify pulmonary tuberculosis on a radiograph.

Although artificial intelligence is a vast field, this article will focus on machine learning techniques because of their widespread use in some of the most important clinical applications available today.

Top trends in Medical Artificial Intelligence

Technologies delving into the medical field must demonstrate their efficacy and integrate with existing practices, receive the necessary regulatory approvals, and inspire the medical staff and patients to embrace a new paradigm. These challenges have resulted in the following trends in artificial intelligence research and adoption.

Artificial intelligence can excel at clearly defined tasks:

According to research, artificial intelligence can effectively showcase its performance compared to a human doctor. Some of these tasks are equipped with clearly defined inputs and a binary output that can be easily validated. When classifying some of the suspicious-looking skin lesions, the input provided can be a digital copy of a photograph. At the same time, the output generated is often a simple binary classification—benign or malignant.

With the mentioned conditions, researchers were tasked with demonstrating how artificial intelligence was superior in its sensitivity and specificity compared to dermatologists, especially in cases where they were required to classify previously unseen digital images of other biopsy-validated lesions.

Artificial intelligence is not replacing doctors, it is supporting them:

Since machines lack the qualities that are inherent in humans, like empathy and compassion, therefore, most patients would perceive and prefer if human doctors led their medical consultations. Additionally, it will take some time before the commoner can trust artificial intelligence immediately.

Hence, AI deals with the tasks that are critical but limited in their scope to leave the patient management responsibility in the hands of a human doctor. Several clinical trials using AI can compute the target area for both head and neck radiotherapy better than when done by a human doctor. Although a human radiologist will ultimately be required to deliver the therapy to the patient, artificial intelligence plays a critical role as it can protect the patient from radiation that is harmful to them.

Artificial intelligence can support services with minimal resources:

Artificial intelligence can be used in situations and areas where human intervention or expertise is scarce, as a single system can support many people. In countries where tuberculosis is common, medical professionals lack experience in radiology.

Hence, in some cases, radiographs are taken and uploaded from these areas and interpreted by an artificial intelligence system. Additionally, there are under-resourced tasks where patients experience unreasonable wait times, and in such cases, AI is used as a triage system.

Conclusion:

Although rapidly developing and advancing, artificial medical intelligence has experienced challenges with time. Furthermore, even though its value and ability remain to be seen, the onus lies in the hands of healthcare providers and organizations to embrace this technology.

AI is not going anywhere and is in it for the long haul. More data needs to be collected so that these newly developed solutions can provide people with a much superior level of care worldwide.