Over the last few years, healthcare providers have increasingly offered telehealth appointments, managed digital transfers of medical records, and encouraged patients to take a more active role in monitoring their own health through remote patient monitoring (RPM) tools.
This widespread adoption of healthcare technology has prompted many providers to design ethically-focused AI-powered solutions that not only meet their needs, but their patients’ as well.
AI-powered solutions have the ability to augment everyday tasks in a multitude of industries—and healthcare is no exception.
Here are a few examples of artificial intelligence applications in healthcare, along with their potential benefits:
When patient privacy is involved, AI-powered solutions that are ethical, approachable, and compliant with regulations are critical to success.
The healthcare industry is laden with data use and privacy regulations that can make designing trustworthy AI challenging. But by using a Trusted AI framework and adopting a design thinking mindset, it’s possible to build a compliant AI solution that you, and your stakeholders, can trust.
Here are three ways the trusted AI framework can ensure an AI solution augments your work without bias.
For an AI algorithm to produce transparent, unbiased results, users must fully understand the data that goes into the algorithm.
One example might include paying close attention to the diagnostic information that’s used to help train an AI to predict future diagnoses. Because a human healthcare provider made the original diagnoses, it’s important to ensure there is no bias in the training data.
Rather than choosing any medical data for AI solutions, we work with clinicians to select metrics that appropriately fit the problem the AI is trying to solve. This ensures that we understand the data being used and that the solution has the relevant data it needs to create informed, trusted results.
When designing an AI solution in healthcare, the privacy of patient data will likely be your top priority—especially when complying with privacy laws like HIPAA, HITECH, GDPR, and CCPA.
The removal of personally identifiable information (PII) is likely not enough to protect patients.
To truly protect privacy and avoid unintended data leaks, it’s critical to audit and monitor an AI solution for bias during development, throughout implementation, and after deployment. To ensure the solution is ethically designed by humans and for humans, this feedback process should include both data scientists and healthcare professionals.
Fairness in AI promotes equity and inclusion while simultaneously eliminating bias. In healthcare specifically, it’s crucial to avoid implicit bias, or unconscious prejudice, when designing trustworthy AI-powered solutions.
One way to design an AI solution that is fair is by incorporating other data sources—like social determinants of health (SDOH)—when developing the model. Without considering SDOH, metrics like historical healthcare spending, for example, cannot accurately correlate to an individual’s level of healthcare need; low income or poor access to healthcare may impact someone’s decision to avoid services, even if they have a need.
Designing and developing ethical AI solutions in healthcare can lead to an enhanced patient experience, improved efficiency of care, and increasingly accurate diagnoses—without fear of unintended bias.
Learn more about implementing AI solutions across a variety of industries by subscribing to our Voices of Trusted AI monthly digest. It’s a once-per-month email that contains helpful Trusted AI resources, reputable information, and actionable tips straight from data science experts themselves.
Contributors: Nicole Ponstingle is the COO and AI Translator at Pandata. Niki Agrawal is an Associate Data Science Consultant at Pandata.
Before designing an AI solution, it’s critical to understand some of the top challenges regulated industry leaders face when considering AI.
In this resource, globally recognized AI Strategist and Pandata CEO, Cal Al-Dhubaib, shares some of the challenges he’s repeatedly seen throughout his years of working with leaders in regulated industries—and how to overcome them.
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