Artificial Intelligence is a duplication of human intelligence in machines through programming and coding to mimic human thinking. Artificial Intelligence takes the best possible action on their own to achieve a specific goal. AI recognizes the behavioral and procedural pattern and then creates its own logic to achieve the defined goal. AI continuously evolving across industries such as healthcare, statistics, logistics, computer science, telecommunication, etc. AI in healthcare gain the information on its own, process it to give simplified output by applying machine learning capabilities. AI in healthcare has a variety of applications such as assistance during surgical procedures, medication, and dosing, patient admissions, personalized medicine, patient monitoring, chronic disease management, medical image recognition, etc. Technological advancements in the healthcare sector, such as the Internet of Things (IoT), big data, Cloud Computing, and others have transformed the healthcare industry. The availability of big data in healthcare has led to the requirement for Healthcare Artificial Intelligence Market.
Image recognition is playing a huge part in driving the adoption of AI in medical diagnostics. For instance, in July 2016, DeepMind collaborated with Moorfields Eye Hospital to develop AI applications for the analysis of anonymized eye scans, detection of early signs of diseases leading to blindness. Also, in August 2016, DeepMind collaborated with University College London Hospital to develop an algorithm that can automatically differentiate between cancerous and healthy tissues in neck and head regions. DeepMind Technologies were acquired by Google in 2014. Whereas, other pharmaceutical companies are experimenting with deep learning (machine learning) to design and develop new drugs. For instance, Merck partnered with Atomwise to develop new drugs. GlaxoSmithKline has partnered with Insilico Medicine for the same purpose. Whereas, Eli Lilly has entered in $560 million partnership with Atomwise to develop up to 10 drug targets. The partnership aimed to speed up target identification and drug development.
However, the lack of standard format and systematic healthcare data storage are restricting this new technology to get adopted early. To date, patient’s files are available in raw format such as fax, email, handwritten prescription notes, x-ray films, CT scan reports, PDF, MRI reports, JPEG format, etc. Extracting the information from such a huge variety of formats and storing it in the EHR platform, is time-consuming and tedious. The protection of sensitive health data is the most important reason obstructing the adoption of AI in healthcare. The patients are concerned about the confidentiality of their private health data as the AI tech companies can sell such sensitive personal data to third parties. AI-enabled medical devices access and transfer the patients’ most sensitive biological data, and any susceptibilities in the cloud or shared network which carries the obvious privacy risks. For instance, according to the Office of Civil Rights under Health and Human Services, US, in 2015, there were 253 healthcare breaches reported that affected more than 500 individuals with a combined loss of 112 million records. Also, according to PwC’s Global State of Information Security Survey 2015, there was a 60% increase in the information security breaches that were reported by healthcare providers from 2013 to 2014. However, the AI solution providers along with medical devices manufacturers are increasing their investments to improve security protection; the consumers are expected to take some more time in entirely relying on such technologies to give access to their private data.
Application of AI in healthcare can help the doctors and physicians to better diagnose the patient at risk of getting infection or disease and also assist in learning the everyday patterns of their patients to keep them healthy and fit. AI in healthcare helps the providers and patients, to reduce healthcare expenses, reduce patient recovery time, and better disease management. Hospitals & clinics, healthcare research institutions, government healthcare institutions, healthcare professionals, and diagnostic laboratories are few of the end-users where AI can implement. The adoption rate of AI in healthcare has been rising owing to factors such as increased adoption of smart medical devices and wearables, rising initiatives from government and private companies to expand healthcare IT infrastructure, rising demand for quicker drug development, and demand for reduced healthcare cost.
Studies reveal, more and more healthcare facilities across the globe are adopting the AI to offer better care and benefits to a larger patient population, hence leading to the reduction of healthcare expenditures.