How well-trained, mature AI solutions can earn trust

What healthcare organizations want most from their AI solutions

By | 2 minute read | February 21, 2020

As artificial intelligence (AI) solutions saturate the imaging market, healthcare providers are asking: which ones can we trust? To earn the trust of users, AI solutions should be both well-trained and mature.

First, it is the vendor’s responsibility to offer a well-trained solution. Data used to hone the algorithms should be vast and offer several dimensions of diversity, including:

  • People – Represent different geographies, ethnicities, and races.
  • Conditions – Include multiple types of lesions, tumors, and diseases, especially rare ones.
  • Sources – Handle a variety of vendors and modalities that generate information.

Depth and detail in large data sets enable well-trained AI solutions to avoid potential bias. They also enable the AI to better recognize when it encounters a rarity, which may cause its responses to be unreliable. Companies that offer well-trained solutions should also clearly communicate how and when they should be used; not every AI solution is trained for every situation.

Second, there is the issue of maturity, which I define as the comfort and adoption level of the clinician using the AI solution. To be considered mature, AI should be fully integrated into the user’s workflow. If healthcare providers must log into or toggle between separate systems, it adds a burden to their workload and contributes to physician burnout. Users may get frustrated and stop using them altogether.

Ideally, AI should help healthcare providers have better access to the information they need to do their work with confidence and efficiency. AI should be there when they need it and stay out of their way when they don’t.

Relying on trusted partners

Knowing how to evaluate an individual AI application is the first step, but how do organizations find these quality solutions among the hundreds out there? The key is a reliable partner that provides access to applications that have been rigorously vetted for quality and curated especially for the needs of healthcare organizations.

For example, knowing that the vendor has the highest standards for data security and privacy is fundamentally important. Health systems are looking for assurances that data will not escape or mingle with other data or be re-used for unauthorized purposes.

Health systems also want to know that their AI solution comes from a company that will stand behind its offerings, including those offered by partners. They offer appropriate privacy and security standards, testing validation processes and seamless methods of payment and usage. These companies offer comprehensive services supporting a variety of AI applications within existing workflows and infrastructures.

Trust doesn’t happen overnight. It takes years to establish a strong history in healthcare and build trust with advanced data analytics. But reliable partnerships, as well as assurances that the AI solution is well-trained and mature, help build trust.