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The healthcare sector utilizes AI technologies as it can reliably offer accurate diagnosis and better treatment.
FREMONT, CA: Artificial Intelligence, computer vision, and machine learning technologies have shown that computers interpret big data better and faster than humans. Corporations today have vast databases of patient knowledge and disease observations through strategies such as Genome-Wide Association Studies (GWAS).
Healthcare professionals can reliably assess and interpret the available patient data for accurate diagnosis and better treatment using AI. Today, by utilizing computer vision and machine learning to detect elevated bilirubin levels in a person's sclera, the white part of the eye, it is possible to tell if a person has the potential to get cancer from a selfie.
There are several existing AI applications as the interest in AI in the healthcare industry continues to expand, and more use cases will arise in the future. Researchers do face some unique healthcare issues, such as data protection and regulations, which need to be addressed when developing AI technology for the healthcare sector.
Will the interest in AI continue to grow in the healthcare industry?
Healthcare is one of the leading industries that can use AI according to various tools such as G2 and Business Insider. The concept is also backed by strong growth in the AI healthcare industry. Developers assume that the healthcare industry needs this expansion, considering the demand and supply for healthcare workers in the future. In narrowing the supply & demand gap, AI may play a critical role.
What is AI use cases in the healthcare industry?
There are several use cases of artificial intelligence in the healthcare industry and organized these use cases around traditional healthcare industry processes. The structure is not yet detailed, but it can still give insights into the operations and use cases. It is also continually improving.
• Assisted or automated diagnosis and prescription: Chatbots will enable patients to diagnose themselves or assist doctors during diagnosis.
• Prescription auditing: AI audit systems can help to reduce errors in prescriptions.
• Pregnancy management: Track mother and fetus to decrease the mother's fears and allow early diagnosis.
• Prioritization and triage in real-time: Prescriptive analytics on patient data enable effective prioritization and triage in real-time.
Medical Imaging and Diagnostic
• Early diagnosis: Examining chronic conditions to facilitate early diagnosis by using laboratory data and other medical data.
• Insights into medical imaging: Advanced medical imaging to interpret and convert pictures and model future scenarios.