NLP Top AI Priority for Technical Leaders, New Research Finds
Second annual AI in Healthcare survey uncovers industry trends, challenges, and best practices in artificial intelligence among healthcare and life sciences practitioners.
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John Snow Labs, an AI and NLP for healthcare company and developer of the Spark NLP library, today released the findings of its annual AI in Healthcare Survey Report. Conducted by Gradient Flow, the research explores the trends, tools, and behaviors around artificial intelligence (AI) use among healthcare and life sciences practitioners.
The shift from use by data scientists to domain experts is continuing to grow. More than half (61 percent) of respondents identified clinicians as their target users, followed by healthcare payers (45 percent) and health IT companies (38 percent). This paired with significant developments and investments in healthcare-specific AI applications and availability of open source technologies are indicative of wider industry adoption.
“As survey results have indicated over the past two years, we’re seeing AI applications transition from exclusively tools for data scientists to medical professionals and even patients, greatly broadening access to AI,” said David Talby, CTO, John Snow Labs. “With technologies such as NLP making it possible to bridge the gaps between structured data, unstructured clinical and biomedical text, and other data modalities such as imaging and voice, not only can we enable delivery of better patient care, but we can put that power directly in the hands of healthcare practitioners.”
Other survey findings include:
- When asked what technologies they plan to have in place by the end of 2022, technical leaders cited data integration (46 percent), BI (44 percent), NLP (43 percent), and new this year, data annotation (38 percent).
- Locally installed commercial software (37 percent) and open source software (35 percent) were the most popular forms of software being used to build healthcare AI applications. This shows a 12 percent decline in use of cloud services (30 percent) from last year’s survey (42 percent).
- A majority of respondents (53 percent) chose to rely on their own data to validate models rather than on third-party or software vendor metrics. Mature organizations (68 percent) relied even more heavily on using in-house evaluation and tuning models themselves.
- Production readiness was the top criteria used to evaluate machine learning, NLP, and computer vision solutions among technical leaders; respondents from mature organizations prioritized the ability to train and tune models.
- 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 as the most important requirement.
The full 2022 AI in Healthcare Survey Report can be downloaded here.