What Healthcare Can Tell Us About the Future of AI

The healthcare industry has long embraced technology, often on the early side of adoption, facilitating new and innovative techniques to improve the delivery of care. As technologies such as artificial intelligence (AI) proliferate, this sentiment has remained the same, and major strides have been made. One only needs to look at the past year — AI applications were used in everything from COVID-19 vaccine development to detecting new strains of the virus and its spread, providing valuable knowledge to researchers, health and government officials, and beyond.

Even outside of the pandemic, ‘everyday’ applications of AI are becoming commonplace in medicine. Natural Language Processing (NLP) can provide significant value in building patient cohorts and identifying clinical risks by understanding the complexities and nuances of clinical jargon. Computer vision, in particular, has proven its value in medical imaging to assist in screening and diagnosis. The list goes on, and as such, there are a lot of other industries earlier in the AI journey that can learn from its applications in healthcare and life sciences.

This is particularly impressive when you consider the formal implications, both contractual and regulatory, the healthcare industry is beholden to. While regulations such as HIPAA can be seen as slowing adoption, they’re only requiring best practices of Responsible AI to be implemented upfront. Healthcare is paving the way in this respect. Other highly regulated industries, such as finance, have also found a balance between security and automation when it comes to AI enablement.

To uncover more about the industry on the pulse of enterprise AI, “The 2021 AI in Healthcare Survey Report” aims to shed light on the challenges, triumphs, tools, and practices fueling AI adoption. By taking cues from the global healthcare community on where we are and where we’re headed, organizations in any industry can apply these learnings to make sense of the technologies and solutions in which to invest time, talent, and capital in. Here are three findings from the research that any business can use to fuel their AI initiatives.

1. Put Your Data to Work With Data Integration, Business Intelligence, and Natural Language Processing

When asked what technologies they plan to have in place by the end of 2021, survey respondents pointed to data integration (45%), NLP (36%), and Business Intelligence (33%). Half of the technical leaders report that they are already using or planning to use technologies for data integration, NLP, BI, and data warehousing. So, what does this tell us? Healthcare is getting serious about putting their massive amounts of data to work—and they’re using AI to connect the dots. Although different in practice, all of these technologies help to consolidate and make sense of siloed data that, when put together, can help decision-makers act on the delivery of care, allocation of funds, and a host of other business-critical activities. The ability for humans to understand and utilize the amount of information digital businesses are accumulating is near impossible. Finding tools to address this has been key for breakthroughs in healthcare and will be for other industries, too.

2. Prioritize Privacy and Domain Expertise When Evaluating Tools and Services

It’s easy to understand why keeping patient information safe is paramount in healthcare, but good security hygiene is industry-agnostic. Whether it’s payment information stored on a retailer’s website, financial documents, or trade secrets, protecting data should be a key priority for all businesses. When asked about what was most important when selecting AI solutions, tools, and services, 44% of technical leaders cited no data sharing with software vendors. Similarly, 45% indicated no sharing or derivative rights of the data or code was important when considering a consulting company to work with. Accuracy and subject matter expertise also ranked as highly important when considering tools and partners to work with. AI is not a one-size-fits-all solution, and organizations would be smart to make sure their vendors and technologies meet appropriate regulatory and industry-specific needs before diving in.

3. Know Your Customer and Your End User

When asked to identify intended users for their AI tools and technologies, 54% identified clinicians (54%) and healthcare providers (45%) among their target users. While it’s not surprising that clinical professionals are using AI more liberally, it’s interesting to see that patients are inching their way into the primary user category. In fact, of mature organizations, nearly 60% indicated patients were also users of their AI technologies. With the ability to streamline processes and free up clinical staff for mission-critical tasks, it’s exciting to see AI use has filtered down to patients. However, when creating and deploying AI solutions it’s important to keep in mind who the end-user is. In any industry, a consumer-facing application is going to look a lot different than the one used by an enterprise user, so be sure UX is top of mind when getting started.

Beginning or improving upon your existing AI endeavors is no easy feat. There’s a lot of noise about what’s next in the field, cutting-edge use cases, and the pressure to keep up with the pace of technology development. But healthcare has been innovating, applying, and refining AI technology at a significant rate, and other industries would be smart to take a page out of their book.

This UrIoTNews article is syndicated fromDzone