PUBLISHER: Coherent Market Insights | PRODUCT CODE: 1349867
PUBLISHER: Coherent Market Insights | PRODUCT CODE: 1349867
The global artificial intelligence (AI) diagnosis market was valued at US$ 1,110.7 Mn in 2022 and is forecast to reach a value of US$ 5,773.6 Mn by 2030 at a CAGR of 21.2% between 2023 and 2030.
Report Coverage | Report Details | ||
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Base Year: | 2022 | Market Size in 2023: | US$ 1,502.9 Mn |
Historical Data for: | 2018 to 2021 | Forecast Period: | 2023 - 2030 |
Forecast Period 2023 to 2030 CAGR: | 21.20% | 2030 Value Projection: | US$ 5,773.6 Mn |
Artificial intelligence (AI) has powerful potential within healthcare, promising the ability to analyze vast amounts of data quickly and in detail. In a field such as in vitro diagnostics (IVD), which could have transformative implications. Thus, there is an increasing demand for artificial intelligence technology in healthcare sector.
The practice of medical diagnostics is rapidly changing with the development and adoption of Artificial Intelligence (AI). Constant improvements in computer processing have enabled AI-based systems to provide accurate and efficient diagnosis & treatment management plans across various specializations. AI can play a big role in diagnosis and healthcare, with advancements in computing power, learning algorithms, cloud storage, and availability of datasets from electronic health records. It can enable radiologists and pathologists to accurately diagnosis conditions at an early stage to provide adequate & effective treatment.
In a PubMed article on applications of AI in radiology, in December 2017, it was studied that the next breakthrough would not be through an innovative radiology device but through the integration of AI. This integration is likely to take place in stages. AI-based devices are collecting data from imaging technologies, such as ultrasound, MRI, CT, and PET. In its first stage, AI is already performing automatic segmentation of various imaging data and structures to yield significant analytical value and help save the radiologist's time. Over the next decade, AI-based radiology solutions are likely to be used to perform routine tasks such as quantification, segmentation, and pattern recognition prior to the radiologist's analysis, thereby improving patient care delivery.