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Four Ways How AI Will Change Healthcare

AI’s biggest changes to the healthcare industry will likely be related to situations that involve verification and statistical analysis. The current AI systems that have been proven to function are generally designed to solve problems through the use of algorithms. New AI systems being created by technology companies are now operating on machine learning principles. Machine learning is a bit different because these machines can adapt to new sets of rules as they appear.

Disease Identification

New AI machines use visual and mathematical identification systems. The Obama administration created an initiative called the Cancer Moonshot. AI systems within this initiative are drastically changing the way researchers identify cancer. These AI systems recognize diversity within cancer identification. This allows the machine to create a better predictive model.

Diagnostic Errors

Studies have shown that AI systems diagnose cancer at a rate that is 80% better than a human doctor. Diagnostic errors cause many patient deaths. Hospital complications can also occur. Pathologists have to examine microscopic cells. The scanning systems that AI-controlled devices use are generally more accurate at predicting patterns that determine whether a diagnosis is correct. AI systems may scan an area hundreds of times than a convention scan to provide accurate data for the physician to examine.

Dermatology

Biology is a data-intensive field. Machine learning already exists in the field of dermatology. Dermatologists use AI imaging techniques to predict the onset of skin cancer. AI systems can also personalize patient treatment. Dermatology is a difficult field because diseases like metastatic cancers may spread from one part of the body to the skin. AI systems are now equipped to deal with some exogenous variables that would normally be ignored by an algorithmic system.

Genomics

The human genome was completely mapped in 2003. Diseases like cancer are caused by cell mutation. Doctors have difficulty determining the error rate for many diseases because errors also develop during the sequencing process. A sequencing error and genetic mutation may look identical. Artificial intelligences in medicine are correctly distinguishing between the two. New AI systems can use their own statistical controls to improve the quality of the diagnostic system.

AI will eventually change the way every worker solves problems. Doctors will be able to harness the power of these AI systems, although physicians can look at the big picture in ways that machines currently cannot. Machines are already better at diagnostic analysis, so physicians and computers will eventually develop a symbiotic relationship that focuses on overall patient care.

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