PUBLISHER: Coherent Market Insights | PRODUCT CODE: 1741351
PUBLISHER: Coherent Market Insights | PRODUCT CODE: 1741351
Artificial Intelligence Diagnostics Market is estimated to be valued at USD 2,207.8 Mn in 2025 and is expected to reach USD 8,481.6 Mn by 2032, growing at a compound annual growth rate (CAGR) of 21.2% from 2025 to 2032.
Report Coverage | Report Details | ||
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Base Year: | 2024 | Market Size in 2025: | USD 2,207.8 Mn |
Historical Data for: | 2020 To 2024 | Forecast Period: | 2025 To 2032 |
Forecast Period 2025 to 2032 CAGR: | 21.20% | 2032 Value Projection: | USD 8,481.6 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.