Decoding the Booming Adoption of AI in Healthcare Market

Owing to the COVID 19 pandemic, healthcare institutes are readily adopting artificial intelligence-based solutions for an effective outcome. It may seem that COVID 19 has initiated this adoption, but over the past few years, healthcare institutes have taken cognizance of this nascent technology. Due to its ability to analyse and process large datasets, healthcare institutes are deploying AI-models to make data-driven decisions.

Big techs like IBM and Google are leveraging their respective products towards proactive data-driven healthcare advancements. For example, IBM Watson Health is a comprehensive product created to solve major health challenges using data, analytics and AI. Similarly, Google's Deepmind Health is integrated with AI and helps in the diagnosis of diseases, predicts patient outcomes and improves patient's healthcare. Facebook this year has launched an AI-solution so that accurate and clear image of the organs is produced with limited MRI cycles.

A report by Deloitte states that in the year 2019 75% of the large healthcare organizations have invested over US$ 50 million in AI projects or technologies, while approximately 95% of the mid-sized organization with revenue of US$5 billion to US$10 billion made an investment below US$50 million in AI tools and services. The report also points out that by implementing AI tools, 34% of healthcare institutes are aiming for efficiency, 27% are aiming to enhance products and services and 26% are lowering the cost.

Another report by PwC indicates that over the past decade, the AI investments in healthcare institutes have heated up. The report estimates that by the year 2021, the AI investment in healthcare will be 40% more compared to the year 2014. Research firm Frost and Sullivan have predicted the AI investment in healthcare to increase from US$634 million in 2014 to US$ 6.7 billion in 2021.

The use of AI-based solutions is not only confined to disease diagnosis or drug development, but these solutions have also permeated in patient's healthcare and training of healthcare professionals.

Additionally, electronic health record platforms such as Quest diagnostics' Quanum, utilizes predictive analysis for analyzing the patient's test data to aid primary healthcare professionals for identifying patient's having the early onset of dementia. Other predictive analysis solutions help healthcare professionals to categorize patients based on the deterioration of health conditions.

Many other AI-based solutions are also involved in delivering personalized healthcare services to patients in remote areas. Other AI-based solutions are trying to find solutions for the feasible diagnosis of disease such as cancer. For example, Google's Deepmind has partnered with the University of London to deliver treatment for the patients suffering in the head and neck region.

Owing to COVID 19 outbreak, many healthcare institutes have personalized their approach towards the development of AI. For example, Researchers at Mount Sinai hospital have independently developed an AI algorithm that detects COVID 19 based on the chest X-ray and blood reports. This AI model mimics the workflow a physician uses to diagnose COVID-19 and gives a final prediction of positive or negative diagnosis. The AI model produces separate probabilities of being COVID-19-positive based on CT images, clinical data, and both combined.

Additionally, AI-based solutions are deployed to lessen the burden of redundant tasks. For example, many institutes are deploying natural language processing, deep learning and machine learning models to extract relevant information from the patient's report and to arrange this information over the hospitals' database.

The scope of AI in healthcare has expanded, beyond disease diagnosis. The global AI in healthcare market size is expected to grow from US$4.9 billion in 2020 to US$45.2 billion by 2026 at a CAGR of 44.9%.