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AI design and development for high risk industries

Why Trusted AI Has to Matter in the Healthcare Industry 

healthcare AI

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.

Common AI Use Cases in Healthcare

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:  

  • Administrative Assistance. Patients can complete online appointment scheduling and pay bills with AI assistance, while internal technology can automate tasks like refilling prescriptions and ordering lab work. These automated tasks allow providers more time to handle urgent patient issues.
  • Chatbots and Virtual Staff. With chatbots and other virtual assistance tools, patient information can be collected and reported to the appropriate human personnel efficiently and often instantly. These tools have the potential to build positive rapport with patients and alleviate the provider’s burden of answering a high velocity of unorganized queries.   
  • Robotic Procedures. Surgery is one of many medical procedures that can be augmented by AI, reducing risk and minimizing harm caused by involuntary human error.
  • Diagnostics Support. Clinical images, including X-rays, CT scans, and MRIs, can be quickly read and accurately interpreted by artificial intelligence models trained to identify abnormalities
  • Clinical Decisions Support. Predictive artificial intelligence models can suggest appropriate care management strategies that account for the likelihood of a patient’s readmission and future risk factors. 

When patient privacy is involved, AI-powered solutions that are ethical, approachable, and compliant with regulations are critical to success.

3 Ways the Trusted AI Framework Guides Compliant AI Design

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.

1. Trusted AI Design Leads to Unbiased, Transparent Outcomes

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.  

2. Ethical AI Protects Privacy of Patients

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.   

3. Designing Trustworthy AI Ensures Fair Treatment Among All Patients

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.

Gain More Expert Insight on Trusted AI 

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Contributors: Nicole Ponstingle is the COO and AI Translator at Pandata. Niki Agrawal is an Associate Data Science Consultant at Pandata.

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