According to a report by MarketsandMarkets, the implementation of AI in the Healthcare Market is forecasted to increase from USD 14.6 Billion in 2023 to USD 102.7 Billion by 2028, accompanied by a CAGR growth of 47.6%. Several factors have caused the increase, including enormous and complex healthcare datasets, declining hardware expenses, rising demand for reduction of healthcare costs, and imbalance between the healthcare workforce and patients. Market growth is forecasted based on the increasing potential of AI in drug discovery, genomics, and diagnostics. However, limitations due to regulatory guidelines for medical software are not excluded.
According to the report, the market for services is expected to grow considerably at CAGR during the forecast period. The software can be coupled with the analytics side using installation services, enabling data retrieval and generating desired results through calculation. The work required for installation increases when computer systems are used for AI. The system requirements, including OS compatibility, required applications, sufficient RAM, and hard disk space, are examined prior to installation. IBM offers a specific installation program for the ICA studio, which is used to develop and deploy unique text analytics for Watson Content Analytics applications.
Further, the report outlines that the market for deep learning, a class of machine learning, is forecasted to maintain its market dominance in 2028. Deep learning is a neural network with three or more data layers, including texts, images, and sounds. These neural networks are helpful in identifying patterns from large amounts of data. In the healthcare market, deep learning consists excellent potential for application in diagnostics, drug discovery, and imaging, especially in X-rays, MRIs, and CT scans.
Additionally, the market for patients is expected to grow at the highest CAGR until 2028. Implementing AI in the healthcare market has become a well-known fact familiar to professionals in the field as well as patients. Consequently, through smartphones and homecare systems ML, cognitive computing, NLP, context-aware computing, and computer vision are increasingly being used. Through these smartphone apps, personalized medical AI consultations result in identifying patients’ health conditions and biomedical parameters.
As the demand for healthcare solutions increases, AI continues to respond with innovative and efficient results, constantly extending its importance to the market.
