Covid-19 Impacts on Artificial Intelligence in Healthcare Diagnosis Market Trends, Development, Investment Opportunities 2020 to 2027

Publisher: The Insight Partners

Artificial intelligence (AI) uses algorithms and software to perform certain tasks without human intervention and instructions. AI represents the integration of technologies such as machine learning, natural language processing, reasoning, and perception. It is used in healthcare for approximation of human cognition as well as the analysis of complex medical and diagnostic imaging data. The artificial intelligence in healthcare diagnosis market is driven by the ability of AI to provide improved outcomes; moreover, the growing need to increase coordination between healthcare workforce and patients also supports the market growth. The rise in the importance of Big Data in healthcare, increase in the adoption of precision medicine, and surge in venture capital investments also contribute to the market growth.

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Company Profiles

• General Electric Company
• Aidoc
• Arterys Inc.
• Icometrix
• IDx Technologies Inc
• MaxQ AI Ltd.
• Caption Health, Inc.
• Zebra Medical Vision Inc.
• Siemens Healthineers AG
• Koninklijke Philips N.V

In terms of diagnostic tools, the medical imaging tool segment held the largest share of the artificial intelligence in healthcare diagnosis market in 2019; it is expected to continue to be a leader shareholder during the forecast period. The growth is mainly ascribed to the increasing FDA approvals of AI-based medical imaging tools. However, the automated detection system segment is expected to register the highest CAGR during the forecast period due to increasing adoption of these tools in infectious disease diagnostics.

Based on end user, the global artificial intelligence in healthcare diagnosis market is segmented into hospital and clinic, diagnostic laboratory, and home care. The hospital and clinic segment held the largest share of the market in 2019, and the segment is expected to register the highest CAGR in the market during the forecast period. The adoption of AI has increases in hospitals with the rise in the prevalence of chronic diseases such as cancer and rare diseases as the technology assist in providing better treatments, especially to newborns with complex disorders. In 2018, ~9.6 million people died due to cancer worldwide. With the analysis of over 1.7 million new cancer cases and more than 606,000 expected cancer deaths in 2019, advancements in cancer diagnostic methods and medication options are need of the time. Advanced AI are capable of drastically altering cancer diagnosis landscape and treated through machine  learning to distinguish imagery of tissue containing cancerous cells from pictures of healthy tissue such advantages offered by AI will further increase the adoption in the hospitals and clinics.

A team of researchers from Beth Israel Deaconess Medical Center of Harvard Medical School, led by Dr. Andrew Beck, showed that data analysis through deep-learning decreased the error rate in breast cancer diagnosis by 85%. Machine learning algorithms were also been tested for the examination of melanoma by the researchers of Stanford University. Apart from reviewing the diagnostic skills of the tool, the researchers results were reviewed by certified dermatologists. Also, artificial intelligence can classify skin cancer as accurately as dermatologists. Further, the researchers studied the commercially available tools for the discovery of colorectal cancer, ColonFlag (in Europe), or LGI Flag (in the US, and found that the machine-learning algorithm identified.

Tokyo-based AI Medical Service, the Japanese startup developing the AI-powered technology to detect cancerous lesions from endoscopic footage, announced in October 2019 that it had secured about USD $43 million US (about 4.6 billion yen) in a Series B round. Healthcare analytics firm Tricog has raised USD $10.5 million in a Series B round of funding. Using Artificial Intelligence and Machine Learning, the startup helps in the wellness, screening, and diagnosis of acute as well as chronic heart diseases. The investment comes in from UTEC - The University of Tokyo Edge Capital, Japan; Aflac Ventures, LLC, Japan; TeamFund, USA; and Dream Incubator, Japan, and also saw participation from Inventus Capital and Blume Ventures. Using Artificial Intelligence and Machine Learning, the five-year-old startup has helped 3 million patients globally. It also uses its virtual cardiology services to help in remote clinics.

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