Artificial Intelligence: A Game Changer in Healthcare World
Artificial intelligence in healthcare is the use of complex algorithms and software in other words artificial intelligence (AI) to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions without direct human input.
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Artificial intelligence has revolutionized the healthcare industry by designing treatment plans, medication management, assisting in repetitive tasks, and drug discovery. Increasing adoption of precision medicine has made enabled simplicity of management and cost reduction. Increasing application in genomics research coupled with incremental innovation in robotic personal digital assistants boost industry growth.
Radiology
The ability to interpret imaging results with radiology may aid clinicians in detecting a minute change in an image that a clinician might accidentally miss. A study at Stanford created an algorithm that could detect pneumonia at that specific site, in those patients involved, with a better average F1 metric (a statistical metric based on accuracy and recall), than the radiologists involved in that trial.
Recent advances have suggested the use of AI to describe and evaluate the outcome of maxillo-facial surgery or the assessment of cleft palate therapy in regard to facial attractiveness or age appearance.
Telehealth
The increase of telemedicine, has shown the rise of possible AI applications. The ability to monitor patients using AI may allow for the communication of information to physicians if possible disease activity may have occurred. A wearable device may allow for constant monitoring of a patient and also allow for the ability to notice changes that may be less distinguishable by humans.
Electronic Health Records
Electronic health records are crucial to the digitalization and information spread of the healthcare industry. However, logging all of this data comes with its own problems like cognitive overload and burnout for users. EHR developers are now automating much of the process and even starting to use natural language processing (NLP) tools to improve this process.
One study conducted by the Centerstone research institute found that predictive modeling of EHR data has achieved 70–72% accuracy in predicting individualized treatment response at baseline. Meaning using an AI tool that scans EHR data. It can pretty accurately predict the course of disease in a person.