AI in Medical Applications: Strategies and Recommendations
Today, healthcare sectors are considered as the largest and rapidly growing segments leveraging AI, driven by numerous innovative clinical research aspects, personal assistance through robotics, and big data predictive analytics. Technological advancement has poised healthcare to invest in AI. Furthermore, with processing towards the industrial revolution, hospitals are gearing to provide advanced treatment to patients through AI-driven applications, which improve patient outcomes, physician productivity and minimize medical errors.
A prediction by Deloitte Touche Tohmatsu, India, states that, with progress towards digitization, the $100 billion worth Indian medical sector will be expanded to $280 billion by 2020. Furthermore, with AI-based technology gaining broader acceptance in the healthcare sector, certain areas are predicted to achieve more success along with the high impact on future healthcare systems.
In medical research, AI tools are mainly used to analyze and determine the critical hidden patterns in complex datasets, which describe the current state of human beings. In the research analysis, the data captured through AI can be used to predict the diseases by combining different data set and aid in drug discovery.
In several parts of the U.S., hospitals are still facing difficulties in recruiting nurses, and there is a shortage of nurses in certain rural regions. This problem is successfully discovered through AI-based virtual assistants—machine learning algorithms and natural language processing tools are deployed to extract the employee database and select the right candidate for the position.
While AI assisted surgery is still at the beginning stage, robot-enabled artificial intelligence techniques are being deployed in numerous microsurgical procedures to minimize surgeon variations and help patients to recover at a faster rate. Moreover, the automated process helps to identify critical insights and achieve best practices for long-term health outcomes.
In traditional medical image analysis, surgeons review the medical images, which are obtained from CT scans, MRIs and X-rays. At certain times, surgeons would find it challenging to analyze the abnormalities due to hidden patterns and dull image clarity. But, AI-based machine learning algorithms have made it easier to analyze scanned images through image processing, deep learning methods, and to detect potential health issues at an early stage.