PHSRC Pulse

Artificial Intelligence In The Medical Field

Artificial Intelligence: functioning as decision support in the medical field 

Written by: Emma Walytka, Editorial Assistant, PHSRC

Students taking AHS 1611: The Future Physician- Medicine in the 21st Century, are introduced to the field of medicine and healthcare through physician-selected headlining topics that impact, shape and lead their future in medicine. The topics in this course reach into popular culture, ethics, politics, technology and more.

One of the most pertinent hot topics in the medical field? Artificial Intelligence, otherwise known as (AI) was explored through a medical advancement lens with the Scientific Director of The Program for Clinical Artificial Intelligence, Christopher Tignanelli. 

Capturing the zeitgeist in the nearly 8 trillion-dollar recession-proof healthcare industry, AI is anticipated to grow into a 500-billion-dollar sector by 2030, according to Tignanelli. 

As not only a weary shadower of the course but also a Journalism student who identifies as a “creative,” I can candidly admit that my bias was the equivalent of that of a pre-med student attending a “why poetry matters” lecture.

But, that was before I sat in a sea of pre-health students. When it comes to AI, it’s vital to detach from your specific occupational hopes and dreams, and instead look at it through the lens of: will this benefit people, and if so, will it improve the mental longevity of our helpers, or in this case future doctors, physical therapists and even surgeons. 

A doctor using AI to help assist with patient management.

The use of AI has skyrocketed, with over 25,000 cumulative publications on artificial intelligence published in Pubmed since 2021, Tignanelli said. 

When deploying the technology, the AI 5-step life cycle is an important model for healthcare facilities to recognize, according to Tignanelli. The life cycle in basic terms looks like this: 

  1. Planning and data collection 
  2. Model development 
  3. Model Validation 
  4. Deployment 
  5. Monitoring and Maintenance 

Each stage is critical to ensure that AI tools are not only safe but also effective and scalable, Tignanelli said. 

The Innovative Methods and Data Science Program (IMDS), A new program developed within the University of Minnesota’s Center for Learning Health Systems Science focuses on developing cutting-edge technologies fueling multiple biomedical research laboratories across campus such as: The Autonomous Surgery Lab, Biomedical Natural Language Processing (NLP) Lab and the Healthcare Computer Vision Lab, according to Tignanelli. 

In the NLP biomedical lab, AI technology is researched in how it assists doctors in replying to messages sent in My Chart, Tignanelli said.

Tignanelli said two key barriers to the widespread adoption of AI include: geographical disadvantages among rural health systems that don’t have the informatics resources to deploy AI tools into care and the lack of development of generalizable AI tools due to difficulties surrounding data sharing across institutions.  

When data is obtained, how to keep it safe raises another concern, hacking attempts at U.S. Health Systems have skyrocketed over 40% in the past few years, according to Tignanelli. 

Simply minoring in something like Data Science and having an understanding of AI, can place people at the cutting edge of the emergence of AI, skyrocketing opportunities for leadership, Tignanelli said. 

“No AI is 100%, that is a common misconception people have,” Tignanelli said. “AI is just a tool, functioning as decision support, that’s why the doctor is still in the loop.”