The Global Artificial Intelligence in Healthcare Market is expected to grow at a CAGR of 49.7% during the forecasting period (2022-2029).
Artificial intelligence (AI), the term broadly refers to computing technologies that resemble processes associated with human knowledge. It is an integration of several techniques such as natural language processing, machine learning, and reasoning. AI is being used for a range of healthcare and research purposes, including detection of disease, management of chronic conditions, delivery of health services, and drug discovery.
The global artificial intelligence in healthcare market growth is driven by the ability of AI to improve patient outcomes increase in adoption of precision medicine, and the need strengthen coordination between the healthcare workforce & patients. With the need for pre-operative planning, high costs associated with healthcare, and rising chronic diseases, the technological advancements are seen in AI are much anticipated.
Improving computing power and declining hardware cost is driving the market growth
The increasing adoption of AI has been a new growth driver for semiconductor chipset manufacturers in recent years. GPU/CPU manufacturers, such as Nvidia, AMD, Intel, Qualcomm, Huawei, and Samsung, have significantly invested in this field for the development of chipsets that are compatible with AI-based technologies and solutions. Apart from CPUs and GPUs, application-specific integrated circuits (ASICs) and field-programmable gate arrays (FPGAs) are being developed for AI applications. For instance, Google has built a new ASIC called tensor processing unit (TPU).
A compute-intensive chipset is one of the critical parameters for processing AI algorithms, the faster the chipset, the quicker it can process the data required to create an AI system. Currently, AI chipsets are mostly deployed in data centres/high-end servers as end computers are currently incapable of handling such huge workloads and do not have enough power and time. Nvidia has a range of GPUs that offer GPU memory bandwidth based on the application. For instance, GeForce GTX Titan X offers a memory bandwidth of 336.5 GB/s and is mostly deployed in desktops, while Tesla V100 16 GB offers a memory bandwidth of 900 GB/s and is used in AI applications. Similarly, Nvidia’s Tesla V100 (32 GB) is used in high computing workloads. It delivers two times higher throughput compared with its previous generation and offers ~300 GB/s to unleash the highest application performance possible on a single server for approximately the same price (USD 8,799).
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By Product type
- Speech recognition
- Natural language processing
- Machine learning
- Imaging & diagnostics
- Drug discovery
- Precision medicine
- Hospital management
- Healthcare assistant robots
Artificial intelligence in the healthcare market is a moderately competitive presence of local as well as global companies. Some of the key players which are contributing to the growth of the market include Intel, Koninklijke Philips, Microsoft, IBM, Siemens Healthineers, Nvidia, Google, General Electric Company, Medtronic, Micron Technology among others. The major players are adopting several growth strategies such as product launches, acquisitions, and collaborations, which are contributing to the growth of artificial intelligence in the healthcare market globally. For instance, in November 2021, Philips extended the artificial intelligence (AI)-enabled CT imaging portfolio at the Radiological Society of North America (RSNA) 2021.
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