Paul Rosacia
March 28, 2023
Current Use
AI in healthcare can be used for a variety of applications. Claims processing, clinical documentation, revenue cycle management, and medical records management are some of the things that AI aids in patient care. Insurers and providers must verify the millions of claims that are submitted daily. Identifying the correct and incorrect claims saves all parties money and resources. AI can improve healthcare by streamlining diagnoses and improve clinical outcomes. A critical part AI’s power in the healthcare industry is its ability to analyze a vast amount of data sets. Integrating AI into the healthcare ecosystem allows for a multitude of benefits, including automating tasks and analyzing patient data sets to deliver better healthcare faster, and at a lower cost. AI also supports medical imaging analysis. AI supports a clinician that is reviewing images and scans. This allows radiologists and cardiologists to identify necessary insights for prioritizing critical cases, to avoid any potential errors in reading electronic health records and to establish more accurate diagnoses. A clinical study can result in vast amounts of data and images that need to be checked. AI algorithms can analyze these datasets quickly and compare them to other studies to identify patterns. The process allows medical imaging professionals to keep track of information quickly. AI can build affiliated platforms for drug discovery. AI algorithms can identify new drug applications by tracing their toxic potential as well as their purpose for use. This technology led to the foundation of a drug discovery platform that allows the company to repurpose existing drugs and bioactive compounds. With the combination of biology, data science, chemistry, and AI improvements, the founding company of this platform can generate substantial amounts of biological data that is processed by AI tools across about one million experiments weekly. The ML tools are created to draw insights from biological datasets that are too complex for clinicians to interpret. This decreases the risk for human bias. Identifying new uses for known drugs is an intelligent strategy for Big Pharma companies, since it is less expensive to repurpose and reposition existing drugs than to create them from scratch. AI also aids in cancer research treatment and medical assistance. Automatic generation of clinical notes integrated with electronic health records led to a reduction of time spent by clinicians in managing patient documentation, which improves medical operations and health outcomes. With AI being able to establish patterns in cancer detection and treatment, clinicians can act fast on diagnoses and providing treatment plans. With electronic health records being used as a way for AI to predict issues, this allows clinicians to be more effective with their workflows, medical decisions, and treatment plan. They can turn the result into a predictive analytics tool that can catch and treat a disease before it becomes life-threatening. Chronic diseases can be predicted, and their progression rate tracked.