Report ID: SQMIG45F2157
Report ID:
SQMIG45F2157 |
Region:
Global |
Published Date: November, 2024
Pages:
193
|
Tables:
91 |
Figures:
70
Drivers
Deep learning has quickly gained traction within the healthcare sector to improve diagnostic accuracy and provide custom-tailored treatment plans. Similarly, the healthcare industry would trend to implement deep learning algorithms to analyse medical imaging, for example MRI and CT scans, to identify diseases like cancer at the earliest possible time.
Growing demand for intelligent virtual assistants and chat agents is another factor driving the market. Such technologies are also continuously utilized for real-time personalized recommendations and support to customers in customer service, marketing, and sales applications. As a result, the algorithms behind these technologies are constantly evolving leading to a greater efficiency and accuracy of both technologies, which in result is contributing to growth of market.
Restraints
Big data is used as the training dataset for deep learning, which gives it an advantage. The unavailability of a sufficient amount of reliable data can be a drawback for the whole system. For a data model to function successfully, substantial data is required. Due to the lack of available resources, collecting this data can be challenging.
Deep Learning can outperform other techniques, but only if we have sufficient data and a good investment at the start. Due to the complexity of the models over the datasets, training cost a lot of money. In, deep learning requires powerful GPU and hundreds of machines. Thus, the accuracy with the utmost precision is achieved with an increased initial cost. This is expected to be challenging for the growth of the deep learning industry.
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Report ID: SQMIG45F2157