The Current State Of The Healthcare AI Revolution

David Talby for Forbes

Artificial intelligence (AI) is poised to change the healthcare and life sciences industry in ways we couldn’t have imagined only years ago. We’re already seeing it in vaccine development, patient care and research in important fields. From telemedicine to strides in detecting new Covid-19 variants, we’re already living in the age of healthcare AI. But getting to these breakthrough developments starts smaller than that. 

The technologies, tools, triumphs and failures are the less-talked-about aspects of creating accurate, effective and responsible AI solutions, but understanding those parts of the equation is vital to success and progress. The new 2021 Healthcare AI Survey from Gradient Flow, sponsored by my company, aims to do just that: unearth these areas to provide a better overview of where we actually stand when it comes to AI in healthcare.

One of the most telling findings here is the shift of AI technologies that organizations are currently using or plan to implement in 2021. Respondents to the survey said they wanted to have natural language processing (NLP) (36%), data integration (45%), and business intelligence (BI) (33%) as the three most widely applied technologies in their businesses by the close of 2021. These aren’t just lofty goals — they’re backed by money. The 2020 NLP Industry Survey, published by the same group in Fall 2020, reported that more than half of technology leaders — the people overseeing AI investment — have increased the budget allocated to NLP between 2019 to 2020.

Paired with data integration and BI, it’s clear that healthcare systems are getting more serious about the value of unlocking their data — structured and unstructured. NLP, BI and data integration solve some of the biggest problems the healthcare industry faces, from serving as connective tissue between siloed data sources (in electronic health records, free text, imaging and more) to safeguarding personally identifiable information (PII) and making sure it stays private. For highly regulated industries, such as healthcare and pharma, AI-powered technologies like the aforementioned will be critical to operations and safety. 

Another encouraging finding is the criteria most important to healthcare users when evaluating which AI technologies to explore further. The top three criteria for technical leaders when evaluating such technologies and tools were providing extreme accuracy (48%), ensuring no data is shared with their software providers and vendors whatsoever (44%) and having the ability to train and tune the models to match their own datasets and use cases. Privacy, trainability and accuracy are important for any AI solution, but especially when dealing with medical information that can impact the delivery of care. Access to data and ownership of specialized models are also a primary source of intellectual property that AI organizations build.

Accuracy, in particular, is a big topic of interest in clinical applications. Here’s an example of why this is so important: According to a report from the Journal of General Internal Medicine, “Collection of data on race, ethnicity, and language preference is required as part of the ‘meaningful use’ of electronic health records (EHRs). These data serve as a foundation for interventions to reduce health disparities.” The paper found important inaccuracies in what was recorded in EHRs and what patients reported. For example, “30% of whites self-reported identification with at least one other racial or ethnic group than was reflected in the EHR, as did 37% of Hispanics, and 41% of African Americans.” This is a problem when you consider patients from certain backgrounds and ethnicities may have a greater risk for developing certain comorbidities or lack access to appropriate care. This isn’t necessarily an AI problem but a data problem — and data needs to be accurate in order for AI to work its magic.

This emphasis on accuracy also feeds into what technical leaders are looking for when evaluating software libraries or SaaS solutions to fuel their AI initiatives. Per the 2021 Healthcare AI Survey, healthcare-specific models and algorithms (42%) and a production-ready codebase (40%) topped the list when considering a solution. Healthcare-specific models are familiar with the nuances of medical data, from clinical jargon and language to billing codes and other data from nontext entities, such as x-rays. Additionally, production-grade products empower users from data scientists to clinicians to integrate AI technologies into their daily workflows with a reduced risk of problems or inaccuracies — after all, they’ve already been tested and proven and are being updated over time. 

As AI begins to trickle down to use by patients with the advent of chatbots, automated appointment scheduling, or obtaining access to their medical records, it’s important to be aware of both the value and challenges this technology can bring. A chatbot not being able to connect a person to the correct department may not seem like a big deal — unless the patient is experiencing an acute medical event that needs immediate care. The varying levels of severity in medical settings make it obvious why factors like accuracy, healthcare-specific models and production-ready code bases could be the difference not just between a successful AI deployment and a failed one but, in some cases, between life and death.

With the global AI in healthcare market size expected to grow from just under $5 billion in 2020 to $45.2 billion by 2026, the investments and recent use cases for this technology are proof that AI is here to stay. But with many of these cutting-edge technologies still in their infancy and many challenges ahead, the jury is still out on what the next few years hold for AI adoption, key players and clinical advances for the healthcare industry. Thankfully, with research at our fingertips, we’re a bit closer to getting there.

Meanwhile, stay up to date on the latest case studies, innovations and lessons learned — and don’t wait too long to jump in and help build the future.

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