AI in Medical Diagnosis and Prognosis: Current State and Challenges

Abstract

The COVID-19 pandemic led to physical distancing, thus increasing the use of digital health programs such as artificial intelligence (AI) platforms. Throughout this paper, we will be using AI to describe a system that performs actions that typically require human thinking skills. AI has the potential to transform into a healthcare organization using machine learning, deep learning, and natural language processing. This review will include AI-based diagnostic and prediction tools, and their potential in changing the detection and management of disease in various fields of medicine. AI algorithms are needed to analyze complex medical data, such as X-ray images and heart signals, and often exceed the accuracy of early human detection. Additionally, there are now AI-based wearable devices and supported systems available for real-time detection and personal management, which could also advance a persons capability toward earlier detection and prevention of disease. Of course, data privacy challenges, constant data access issues, and algorithmic inequity are still present. Collaboration in addressing data collection, algorithm design, and constant monitoring or evaluation will be needed across disciplines. As institutions of health care, it is critical to ensure that the data collected, and the algorithm designed are transparent for AI to be applied in the real-world healthcare field.

Ähnliche Arbeiten