AI can transform the healthcare sector

AI can transform the healthcare sector


Design technology for healthcare with humans in mind

Will Reese, Chief Innovation Officer at Inizio Evoke, was featured in Healthcare Digital. Will discussed how healthcare is more human when we design AI and technology around people. When we do this, we can help to transform the industry and reduce systemic and individual biases.

Here are 5 ways to reduce bias in AI projects

Immerse in Experience.

“AI solutions cannot be created in an artificial setting solely viewed as a statistical challenge Ground your solution teams within the real-world healthcare experience and insights of your target populations at a localised level. This deeper understanding can identify data gaps, focus efforts on the greatest unmet needs, and instil empathy from the beginning of design efforts.” 

Examine the algorithm history.

“When embarking on an AI effort, even long-accepted algorithms and medical technologies should be freshly examined for bias and traced back to their origins. Early and transparent risk identification of potential biases can help to mitigate future provider adoption and trust issues. Document and socialize these algorithms and data-set histories to spark further research and innovation.”

Create diverse & inclusive teams.

“Build your solution team to include representation from diverse community representatives with diverse skill sets,” says Reese. “Blending the humanity skillsets with technology and data skillsets helps to design human-centred experiences delivered through technology. These skillsets include ethicists, social scientists, user experience designers, and communications experts. Promoting inclusive STEM-based education opportunities can help to grow talent diversity within the data and technology fields.”

Diversify your data sets.

“Many current healthcare data sets have little or incomplete information regarding under-served patient populations, causing significant issues if used as the base to train AI,” says Reese. “Evaluate and document the origins, demographics, and diversity of your data sets. An innovative organisation focused on improving ethics in AI and reducing data bias is the Data Nutrition Project.”

Partner with the communities you serve.

“Community trust is required to close data diversity gaps and evolve the available healthcare data sets to reflect today’s patients,” says Reese. “Diversify and innovate your partnerships and build relationships with community and advocacy organisations. Immersion in their community insights, co-sponsor new data gathering initiatives and innovate algorithms through data science challenges as valuable ways to partner.”

“Ethical and inclusive applications of AI can unlock healthcare potential,” concludes Reese. “This requires new ways of working and partnerships across healthcare, technology, life science, and community organisations that lead with a consistent focus on health equity.”

Read the article in Healthcare Digital.