Artificial Intelligence: Uses in Healthcare

Artificial Intelligence: Uses in Healthcare

Leveraging artificial intelligence within the healthcare industry can do incredible things. Here are just a few examples.

Artificial intelligence is technology used to replicate or simulate human behavior through a machine. Previously, we took a look at machine learning and it’s applications in healthcare. While similarly related, these two items, AI and machine learning do not mean the same thing. Artificial intelligence is a broader term, in which machine learning is a subset of AI’s capabilities to learn from data sets without explicit programming.

Technical definitions aside, we can’t ignore the advantages of using available technology to assist humans with complex problem-solving and decision-making in the healthcare fields. While we shouldn’t expect artificial intelligence to replace medical professionals, its technological capabilities can do so much to expand and improve healthcare services.

Expanding to Underserved Areas

In areas dealing with shortages of healthcare providers, telehealth and telemedicine help serve these remote communities. Further improving these tools with AI can further increase people’s access to certain health services, such as diagnostics, if trained human providers are unavailable to provide them. Developers should factor in how different ethnic groups, environmental factors, etc., must be considered in the algorithms when trying to identify diseases.

Detailing EHRs

Recording detailed and accurate information in electronic health records has improved patient care, but has led to problems of its own, such as user burnout and endless documentation. Natural language processing tools and video recording capabilities can vastly improve the time-consuming process of recording patient information. Then, these records can be cataloged and indexed by AI for seamless retrieval when one wants to revisit the data.

Predicting Risks from Patient Data

Another potential of using artificial intelligence in conjunction with the wealth of available data from EHRs is creating risk assessment and prediction algorithms. Of course, many challenges need to be overcome before this application is reliable. Being able to predict the medical condition or likelihood of disease is far different from predicting medical billing codes from the relevant course of treatment when the data comes from EHRs.

Analyzing Diagnostic Images

Artificial intelligence can process digital images and make huge advancements for diagnostic imaging. Not only can the technology learn to identify abnormal markers, such as tumors, but it could predict the progress of growth or recovery. This has enormous implications on patient treatment, or even productivity. The software can screen images to bring immediate attention to areas of interest or more importance for further observation.

Enabling Smartphones

Similar to the potential of extending health services and analyzing diagnostic images, AI-enabled smartphones can turn this commonplace device into another accessible and powerful diagnostic tool. Cell phone cameras can capture images for analysis, and can instantly provide the user feedback on image abnormalities. People with these devices can get quick feedback on any physical abnormality from their picture, anything from possible melanoma or attributes hinting to a broken bone.

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This entry was posted on Friday, February 21st, 2020 at 10:32 pm. Responses are currently closed, but you can trackback from your own site.