The 5 Healthcare AI Trends Technologists Need to Know
With the increased investments and interest from a broader range of users, it’s encouraging to see how a highly regulated industry like healthcare is ushering in AI.
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Healthcare has been at the epicenter of everything we do for two years. While the pandemic has been a significant driver of the conversation, healthcare technology—artificial intelligence (AI) specifically—has been experiencing explosive growth. One only needs to look at the funding landscape: more than 40 startups have raised at least $20 million in funding specifically to build AI solutions for healthcare applications.
But what’s driving this growth? The venture capital trail alone won’t help us understand the trends contributing to AI adoption in healthcare. But the “2022 AI in Healthcare Survey” will. For the second year, Gradient Flow and John Snow Labs asked 300 global respondents what they’re experiencing in their AI programs—from the individuals using them to the challenges and the criteria used to build solutions and validate models. These are the top five trends that emerged from the research.
Data annotation is now a foundational AI technology: When asked what technologies they plan to have in place by the end of 2022, technical leaders cited data integration (46%), BI (44%), NLP (43%), and new this year, data annotation (38% ). Text is now the most likely data type used in AI applications, and the prioritization of data annotation indicates an uptick in more sophisticated NLP technologies. With this comes more sophisticated NLP use cases, such as clinical decision support and medical policy assessment. The pandemic has accelerated many areas of medical research, and drug discovery is no exception.
Domain expertise is growing: When asked about intended users for AI tools and technologies, over half of respondents identified clinicians (61%) as target users. Close to half indicated that healthcare providers (45%) are among their target users. Additionally, a higher rate of technical leaders cited healthcare payers and drug development professionals as potential users of AI applications. The shift from data scientist to domain expertise will continue, and it’s a big step for democratizing the use of AI in healthcare and other industries.
Open source solutions vs. public cloud providers: When asked what types of software respondents use to build their AI applications, the most popular selections were locally installed commercial software (37%) and open-source software (35%). With the emphasis on open source software came a 12% decline in the use of cloud services (30%) from last year’s survey results (42%). With these changes come new entry points. As healthcare becomes a bigger target for cyber attacks, practitioners need to be vigilant regardless of whether they use open source or cloud solutions.
In-house evaluation for validating models: Most respondents (53%) rely on their data to validate models rather than third-party or software vendor metrics. Respondents from mature organizations (68%) preferred using in-house evaluation and tuning their models themselves. These data points aren’t surprising, as the healthcare industry is heavily regulated with strict laws around working with data. Protecting health information, especially regarding patients, is essential, and many healthcare organizations are rightfully wary of trusting other entities with it.
Production readiness, training and tuning models, and healthcare-specific models are critical priorities: Production readiness was the top criteria used to evaluate machine learning, NLP, and computer vision solutions among technical leaders, while respondents from mature organizations prioritized the ability to train and tune models. In addition, when evaluating locally installed software libraries or SaaS solutions, both technical leaders and respondents from mature organizations cited the availability of healthcare-specific models and algorithms. When dealing with patients and medications, it’s easy to understand why model specificity and the ability to make tweaks are essential.
With the increased investments and interest from a broader range of users, it’s encouraging to see how a highly-regulated industry like healthcare is ushering in AI. Between balancing responsible practices and safety precautions, while adapting to a new wave of users, it will be interesting to see where AI is in another year from now.
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