AI adoption doesn’t have to be on a grand scale. With such radical new technology, there’s a tendency to assume it must be used for large-scale purposes. Viktor Zamaruiev described how healthcare providers with limited resources can begin with “small portions of algorithms or applications to make sure that you have the right market fit” for your organization’s needs before undertaking broader projects.
Lack of data standardization remains a challenge. Since data is “protected for privacy reasons, and US HIPAA compliance, it’s often very difficult to move it around…[because organizations] really did focus on specific workflows and provider needs,” making it difficult to draw insights and thus build AI solutions. There is a lot of data but now we need to “get it into the systems where it’s actionable.”
NLP is changing the game. NLP or natural language processing enables computers to read, understand, and interpret human language. Justin Klien explained how “some of the most successful startups right now are actually using that NLP to standardize the datasets in a dynamic fashion and basically solve some of those accuracy and data scalability problems that are inherent in the system.”
Removing boundaries between business and engineering teams. Victor Zamaruiev described the “huge gap between business and engineering or even R&D teams.” To solve this, we should “blend them together…for example, even sit in the same room…and work hand in hand. In these cases, domain experts can be supplemented with software developers and data scientists and a gradually diverse team,” creating better results.