Report: 68% of organizations rely on internal assessment to adjust AI models

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According to a new report from Gradient Flow, natural language processing (NLP), business intelligence (BI), data integration and annotation were considered foundational AI technologies. The addition of data annotations to AI technologies that respondents plan to implement by the end of 2022 points to more sophisticated use cases as the technology matures.

As AI use cases progress, another interesting trend is emerging: practitioners are shifting exclusively from data scientists to domain experts. More than half (61%) of respondents identified clinicians as their target users, followed by healthcare providers (45%) and health IT companies (38%).

As low-code and no-code solutions gain traction, this trend is likely to continue in healthcare and beyond. Take building a website, for example – what was once a major software engineering effort is now primarily a graphic design project. This will be an important step in democratizing important technologies in different industries.

For which of these functions do you have a software package that you currently use or plan to use before the end of 2022?  Bar chart.  See the full copy of the article for the results.

There are several other factors at play in lowering the barriers to entry for AI; one is the availability and interest in open source technologies. Locally installed commercial software (37%) and open-source software (35%) were the most popular forms of software used to build healthcare AI applications, respondents said. This shows a 12% drop in the use of cloud services (30%) compared to last year’s survey (42%).

One potential reason for this shift from public cloud providers to reliance on open source technologies could be a sign that users are taking data security and privacy more seriously. In light of recent violations and strict laws and regulations unique to the healthcare industry, this is a step in the right direction.

In fact, the majority of respondents (53%) chose to rely on their own data to validate models, rather than third-party or software vendor metrics. Mature organizations (68%) relied even more on using internal assessment and tuning models themselves, an essential step to avoid model degradation over time.

While model training and optimization remain a priority for users, this year technical leaders and respondents from mature organizations cited the availability of healthcare-specific models and algorithms as the most important requirement. more important when evaluating locally installed software libraries or SaaS solutions. This is further proof of the improvement of AI in healthcare.

Gradient Flow’s survey ran online for 50 days, from January 13 to March 4, 2022, generating a total of 321 respondents from 41 countries. A quarter of respondents were in technical leadership positions, with a fifth working in organizations that had AI models in production for more than two years (referred to as “mature organizations” throughout the survey).

Read the full Gradient Flow report.

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