Artificial Intelligence (AI) in Medical Diagnostics Market in 2021 – Detailed Analysis Report – BioSpace

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Published: Oct 11, 2021
Artificial intelligence (AI) has shown transformative potential in medical and healthcare industry. In particular, the effects of AI penetration in medical diagnostics has expanded the canvas for offering high-quality patient care. Early disease detection is one of the key application where AI players are exploring game-changing potential and are zealously investing in deploying AI in radiology to complement the conventional procedures. The image recognition is perhaps the most compelling consumer proposition in the AI in medical diagnostics market.
Algorithms have been learning and unlearning and relearning signs of diseases in millions of images clinicians extract from the target population. A key trend has been outsourcing these to developing economies to save cost.
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Artificial Intelligence (AI) in Medical Diagnostics Key Trends
There has been flurry of activities in deep learning algorithms to advance the access to quality diagnostics and at low cost. This might be lifesaving in case of chronic conditions that may become fatal if not diagnosed timely. Patients that are at the receiving end are those with significant cardiovascular diseases risks. Further, another dimension that spurs research in AI in medical diagnostics market is need for improving the accuracy of diagnosis, either through single or several images.
The internet of medical things is making vast strides, as is evident in the proliferation of AI-based apps. AI companies are keen on meeting the vast gap between the demand and supply of radiologists on one hand and on the other bringing down the cost of radiological examinations. On the whole, the key clinical proposition of AI in healthcare is use of AI apps in the detection of diseases and advance patient-centred care.
Artificial Intelligence (AI) in Medical Diagnostics Competitive Dynamics and Key Developments
Several recent research and development projects including pioneered by stalwart technology companies have underscored the trend of huge dollar flowing in the Ai in medical diagnostics market. A case in point is the advent of AI-based apps that improve breast cancer detection accuracy. Google, the globally prominent tech company, has demonstrated how a neural network-powered technology can allow easy and early detection of lung cancer. The prospect of AI-driven software in cancer detection presents a vast possibility. Another area which is showing vast prospect of detecting skin problems from the images of skin lesions. Such developments are expected to open new avenues for various industry stakeholders in the AI in medical diagnostics market. Companies are intensifying R&D for leveraging the potential of AI in multiple retinal diseases.
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Some of the emerging incumbent players in the AI in medical diagnostics market are;
Artificial Intelligence (AI) in Medical Diagnostics Regional Assessment
On the regional front, North America has been a highly lucrative region. The vast prospects has been fuelled largely from rise in research and development of developing AI-apps for reducing the burden and prevalence of various chronic diseases. The regional market is expected to witnessed vast revenue streams from the relentless efforts by players in the ecosystem to bring AI and deep learning systems from bench to bedsite, notably in diseases that have substantial prevalence in countries such as the U.S. Asia Pacific is also a promising regional market, and is anticipated to make rapid strides in the near future. A key trend fueling growth is the growing penetration of AI in healthcare.
5-Point Growth Formula
The 5-point growth formula developed by TMR Research provides an insight to the stakeholders and CXOs about the current situation in the market. The growth formula makes the report a perfect companion for the stakeholders and CXOs.
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The 5-point growth formula includes the following points:
The reports offer answers to the top 7 questions that revolve around the growth of the market
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