- Healthcare artificial intelligence is expected to grow at a CAGR of more than 60 percent through 2022, according to a recent report by Research and Markets.
The report stated that growing investments and increasing research and development in artificial intelligence will help drive adoption throughout all industries.
The healthcare industry is expected to see a high volume of artificial intelligence adoption over the next several years as organizations are looking to utilize the unstructured clinical data currently residing in repositories.
Artificial intelligence includes machine learning, natural language processing, cognitive computing, image recognition, and speech recognition.
Healthcare natural language processing is also experiencing growth. A Transparency Market Research report predicted the global healthcare NLP market will be worth $4.3 billion by 2024, growing significantly from the $936 million reported in 2015. Between 2016 and 2024, the market is projected to rise at a CAGR of 18.8 percent.
Meaningful comprehension of unstructured data is the main reason why healthcare organizations are interested in NLP, according to the report. The overall adoption of more advanced health IT infrastructure technology is expected to significantly boost the market.
“The demand for NLP technology is expected to surge in the coming years as it is being used as a strategic tool to derive meaningful comprehensive of clinical informatics for effective outcomes by the healthcare industry,” the report stated. “Used as a part of artificial intelligence systems, applications of NLP technologies are being deployed for predictive analysis and clinical decision support systems.”
Cognitive computing is also seeing significant growth in the healthcare sector as organizations are looking to incorporate advanced data analytics into their health IT infrastructure.
Cognitive computing mimics the way the human brain makes connections between data, in addition to recognizing patterns and connections that goes beyond the ability of a human brain.
Organizations need to consider the health IT infrastructure requirements that artificial intelligence demands and ensure they can support it as they look to adopt artificial intelligence solutions.
Network and storage solutions are two infrastructure areas that are strained by artificial intelligence.
Organizations looking to adopt artificial intelligence need to have full visibility and control over their network. A healthcare network’s backend needs to support EHR systems and applications so they can deliver accurate information to clinicians to support future artificial intelligence solutions.
Legacy wireless solutions cannot support the amount of data constantly moving through a network as more applications and systems are added. Wider bandwidths and access points (APs) can support the increase of devices accessing data using applications. Scalability for system connections is also required for a future AI solution.
Clinical data storage is also a challenge and many organizations are faced with migrating data to the cloud for a more flexible storage option. On-premise data storage can get expensive very quickly and entities collecting data from connected medical and IoT devices for analytics may see their physical servers fill up quickly.
IBM Watson has been making steps to help standardize the growing healthcare artificial intelligence market and support organizations that are looking to use their unstructured clinical data. Watson is currently working on making artificial intelligence easier to integrate into health IT infrastructure.
Last month, IBM Watson Health adopted SNOMED CT for use in Watson Health solutions to help standardize Watson deployments.
SNOMED CT is an international standard for clinical terminology. IBM intends to ease the exchange of clinical information and EHRs through a universal codified language by adopting it as a standard for Watson Health.
Health IT systems can recognize and relate clinical terms and link them via a common SNOMED CT code by integrating SNOMED CT into Watson Health. This allows Watson to efficiently and comprehensively identify all references to terms across different types of data from various health systems.
IBM Watson uses natural language processing and machine learning to read through unstructured data to extract and organize information. Watson then refines that information based on what it has been taught.