Medical A.I.

Advanced artificial intelligence is widely regarded to be a thing of the future. Pop culture and media have contributed to the futurism of state-of-the-art AI, but what if you were told it could be found (and is commonly used) today, especially in the medical world?

Artificial intelligence has numerous applications throughout the field of medicine, making the work of medical professionals more efficient. Four of its common uses include:

1. Diagnosing diseases: Artificial intelligence can observe and analyze patients’ vital signs, noting patterns and alerting doctors/nurses when their attention is required.

2. Medical imaging: AI can detect diseases (cancer, for example) using algorithms to find things like abnormal cell growth. For instance, researchers at Google once created an algorithm called LYNA to identify metastatic cancer tumors and accurately classify them as cancerous or noncancerous.

3. In clinical trials: Identifying cohorts and recruiting volunteers for clinical trials, among other things, can be done by AI, eliminating tedious aspects of researchers’ work.

4. Robot-assisted surgeries: Though they do not function on their own, robots can help surgeons increase their precision when performing more complex procedures on patients, therefore enhancing their abilities.

AI has so many uses because of the multiple benefits it brings to the table, the most significant ones being outperforming doctors and minimizing human error, which can be critical when a patient’s life is at stake.

In clinical trials and in diagnosing patients’ conditions, shortened review time for doctors and increased accuracy of diagnoses are vital. What’s more, the efficiency of machine learning in comparison to humans and reduction of errors could be more cost-effective to healthcare providers, decreasing cost of care for patients.

However, besides the advantages, AI has certain limitations that prevent it from being used more widely.

Some applications of artificial intelligence in healthcare have particular drawbacks: take, for example, algorithms used in clinical trials for drugs and other research. Many of them have not been approved by the FDA, which is known to have strict acceptance criteria.

It may also be hard for patients and the public to trust algorithms without knowing the details of how they work and being confident in the algorithm’s decision making. Thus, the problem of transparency arises: the programming behind the choices of AI deep learning algorithms is intricate, sometimes obscure, and complicated to explain if a patient wants to know the reason behind a particular diagnosis.

In order to prevent unintentional medical malpractice, clinicians would also need to learn how to present data so as to not make it misleading to the algorithm and prevent it from making the wrong choices, which can be potentially difficult based on their level of expertise.

Further, in procedures like surgery, AI cannot be used by itself, as doctors have to analyze their patients and change their approaches on the spot. The human mind is very complex - it would be challenging to make an artificial intelligence mimic human cognition, but this could be possible in the future.

Though it isn’t perfect, the various applications of AI in the medical field make it a versatile tool. And who knows? It just may soon become powerful enough to save countless lives.