Artificial Intelligence (AI) in the healthcare market is expected to reach the market value of US$ 26.6 billion by 2025, growing at a CAGR of 41% during the forecast period (2018-2025). This is the conclusion of UnivDatos Market Insights in their report Artificial Intelligence (AI) in Healthcare Market.
Artificial Intelligence (AI) is expected to contribute up to US$ 15.7 trillion to global GDP by 2030. AI applications, powered by an influx of big data and advancements in computing power, are positioned to transform major sectors, including healthcare.
Expenditure
Global expenditures on healthcare increased to 9.9% of the total GDP in 2017, up from 8.6% in 2000. The United States witnessed the highest expenditure on healthcare, 17% of total GDP, in 2017. The world’s population, aged 60 years and above, is likely to grow by 56% from 2015 to 2030. The healthcare industry is expected to benefit from US$ 45 billion in annual cost savings by 2025.
Research
Healthcare is one of the largest and most rapidly growing segments of AI, driven predominantly by innovation in clinical research, robotic personal assistants and big data analytics. Healthcare is poised to accelerate investments in AI over the next few years. On an estimate, artificial intelligence in healthcare raised a record US$ 4 billion in 2019, up from nearly US$ 2.7 billion in 2018 with a total of 367 and 264 investment deals respectively.
Diseases
To tackle and look forward to emerging diseases, healthcare delivery includes the assistance of new technology such as Artificial Intelligence (AI), Internet of Things ( IoT), Big Data, and machine learning. Some of the major applications of AI during the COVID-19 pandemic include early detection and diagnosis of the infection, treatment monitoring, contact tracing, projection of cases and mortality, development of drugs and vaccines, reducing the workload of healthcare workers, among others. For instance: In China, Alibaba has developed an AI algorithm that can diagnose suspected COVID-19 cases within 20 seconds (almost 45 times faster than the conventional approach) with 96% accuracy.
