Artificial Intelligence (AI) in Healthcare Market Hits US$ 107,797.82 Mn in 2027 to grow at a CAGR of 49.8%

The artificial intelligence in healthcare market was valued at US$ 3,991.23 million in 2019 and it is projected to reach US$ 107,797.82 million by 2027; it is expected to grow at a CAGR of 49.8% from 2020 to 2027. 

Artificial intelligence in healthcare is the use of machine-learning algorithms and software to analyze, process and present complex medical and health care data. It has been widely used to support clinical decisions, improve workflows and predict health outcomes. Thus, wide application of AI in the healthcare sector is likely to propel the growth of the market. The growth of the artificial intelligence in healthcare market is attributed to the rising application of artificial intelligence in healthcare, growing investment in AI healthcare start-ups, and increasing cross-industry partnerships and collaborations. However, dearth of skilled AI workforce and imprecise regulatory guidelines for medical software is the major factor hindering the market growth.

The artificial intelligence in healthcare market is expected to witness substantial growth post-pandemic. The global healthcare infrastructure has observed that, in order to develop and maintain sustainable healthcare setup, utilization of computational technologies such as artificial intelligence becomes crucial. Moreover, majority of the market players have focused on development of AI-powered models to fight against coronavirus pandemic. In addition, several number of research centers and governments have actively participated in the building of robust AI technologies which are assisting the healthcare professionals to work efficiently even under shortage of resources. These factors will eventually drive the market growth.

The complexities related to interpreting images and conducting analysis result in the adoption of AI-based applications. With deep learning programs and expertise categorization, AI-based imaging systems are equipped with quick image reading algorithms, which further includes MRIs and CT scans. The system helps doctors and healthcare professionals to improve performance with better diagnostics and proves to be a vital tool in combating the shortage of radiologists in the hospital. As per the latest paper published in the Future Healthcare Journal in March 2020, AI will be integrated in the healthcare field for diagnosis and treatment approvals, patient engagement and adherence, and the healthcare workforce's administrative events. For instance, AI-Rad Companion Chest CT, developed by Siemens Healthineers, is an AI-powered healthcare solution that can read the chest CT images, accomplish automatic measurements, and formulate the medical report with valuable clinical images and quantifications.

AI is applied to aid cancer diagnosis and prognosis, given its unparalleled accuracy level, which is even sophisticated than that of the general statistical expert. For instance, scientists in National Cancer Institute (NCI's) intramural research program, support AI's abilities to recover cancer screening in cervical and prostate cancer. NCI investigators have designed a deep learning approach for the automatic detection of precancerous cervical lesions from digital images. With increased funds, NCI's efforts to comprehend AI's potential will lead to precise and rapid analyses, enhanced clinical decision-making, and improved health outcomes for cancer patients. Corporate giants like Google and IBM are already concentrating on making innovations in oncology, using cutting-edge AI algorithms for early detection and personalized cancer treatment. For instance, in August 2016, Google DeepMind launched a research partnership with the University College London Hospitals' radiotherapy department to use machine learning for minimizing the time taken for radiotherapy treatment applied for hard-to-treat head and neck cancer.

Pathologists are implementing artificial intelligence (AI) to precisely identify cancer cells amidst healthy cells in a human body. In particular, deep learning-based pattern recognition methods can develop pathology by integrating radiologic, clinical, and genomic data to diagnose diseases and predict patient prognoses accurately. After the Food and Drug Administration (FDA) approvals for primary disease diagnosis applications, digital pathology is becoming the new standard of care. For instance, Google's AI, Lymph Node Assistant (LYNA), a deep-learning tool, can accurately differentiate metastatic cancer with a 99% greater accuracy rate than human pathologists. Moreover, pathologists who used LYNA reported that it gave time-saving outcomes during tasks.

Several healthcare providers are starting to offload certain parts of the care-pathways to artificial intelligence (AI) based automation. For instance, in January 2018, Biotricity implemented device-level AI to improve its remote patient monitoring platform. Similarly, CarePredict uses AI for the uninterrupted detection of changes in activity and behavior patterns for initial recognition of health issues. Further, voice-based virtual assistants, such as Amazon Echo and Orbita Health, utilize AI to permit medication adherence and care coordination for aging. Several Companies such as Careangel further augment the voice-based virtual assistants as nurses and caregivers for target patient populations.

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