Use of AI in Gastroenterology Can Move Beyond “Cool Tools” to Improve Practice Efficiency

As artificial intelligence (AI) technology in the gastrointestinal field continues to advance, speakers at Digestive Disease Week 2022 discussed how these tools can be put into practice to improve efficiency, reduce physician burnout, and reap cost savings.

The session, “Improving Your GI Practice With Digital Technologies,” began with an audience member asking the moderators and panelists to name the “coolest” recent development in AI. Their responses emphasized that new technologies are emerging every day, but the truly attention-worthy innovations are those that will help clinicians and patients. For instance, moderator Cadman Leggett, MD, of the Mayo Clinic, noted that new technology can create a “deepfake” image of an esophagus that could fool a gastroenterologist into thinking it’s real—something that is a cool feat but has absolutely no clinical utility.

In contrast, a presentation from Cesare Hassan, MD, PhD, of Humanitas University in Milan, Italy, delivered an overview of the use of AI in colonoscopy for automated polyp detection and characterization. He discussed research showing that AI-assisted colonoscopy can decrease the rate of missed neoplasms by half, and argued that even suboptimal machines can be useful because humans perform much worse.

“When you are distracted, you miss everything,” Hassan said, referring to a human pitfall that is avoided by machines. And unlike humans, machines can’t lie or cheat, so randomized trials are not necessary to assess their performance. He noted that AI tools to detect polyps have been incorporated into clinical practice almost immediately, but a paradigm shift is needed for computer-aided characterization and diagnosis to take hold.

Despite the clear performance advantages of AI for colonoscopy, its cost has prevented widespread implementation in Europe, where Hassan said it is difficult to convince politicians to pay for an expensive tool that will save money over a very long period of time by reducing colorectal cancer incidence. He called for more studies conducted in community endoscopy practices, which can help demonstrate the real-world value of AI tools.

In the United States, the tension lies in getting insurers to pay for a technology that won’t yield cost benefits until decades later, when beneficiaries are likely to have moved on to a different payer, added the next speaker, Tyler Berzin, MD, of Harvard Medical School and Beth Israel Deaconess Medical Center.

His presentation focused on how AI can help make the lives of gastroenterologists easier by breaking the cycle of disengagement and burnout that is often accelerated by spending too much time in front of a screen performing data entry. The exponential growth of patient data and medical knowledge, which he called the “data deluge,” can feel crippling for physicians, and they need something to support them.

Enter natural language processing (NLP) and speech recognition, Berzin continued. These tools can derive structure and meaning from language, allowing software to extract data and organize it for analysis. He noted that an opportunity to integrate these tools into the gastroenterology practice is by generating analytics for quality measures in a far more efficient manner than can be done by humans.

To illustrate this point, Berzin cited research published in Gastrointestinal Endoscopy in which the researchers developed an NLP algorithm that takes less than 30 minutes to extract data on all colonoscopy procedures performed at their institution since the introduction of electronic health records, whereas manual review by a human takes about 160 hours to extract data for fewer than 600 patients.¹

Berzin also touched on the potential of workflow solutions to transform the clinician experience. These are triage and notification tools that can alert radiologists of which images to prioritize, rather than diagnostic AI tools. These workflow tools may not be as flashy, but they can improve efficiency and have a lower regulatory barrier to approval.

“The goal for AI in medicine is not a promise yet, but it is an opportunity for us to improve clinical insight by leveraging data, decrease the fast and shallow work that we do, and replace that with an opportunity for us to actually think as physicians,” Berzin concluded. “I would wager that that would be a very effective way to combat burnout if we can focus again on what got us into medicine in the first place.”

The final speaker was Prateek Sharma, MD, of University of Kansas Medical Center, who presented a glimpse of the future of AI in gastroenterology and our current status along the timeline. For example, research such as that described by Hassan has advanced from conventional endoscopy to computer-aided detection and diagnosis, but the next steps to be achieved could be automated endotherapy, self-driven scopes, and finally endoscopy without the endoscopist.

Despite the potential for such technology to transform diagnosis, drug discovery, and personalized medicine, Sharma said, some key barriers include data access, data security, and regulatory issues.

He outlined the characteristics of the responsible AI of the future: reproducible, secure, human-centered, unbiased, justifiable, explainable, and monitored.

“Don’t think that this is asking too much, because it’s the same concept we use for clinical trials for pharmaceuticals, for example,” he reminded the audience.

REFERENCE

1. Laique SN, Hayat U, Sarvepalli S, et al. Application of optical character recognition with natural language processing for large-scale quality metric data extraction in colonoscopy reports. Gastrointest Endosc. 2021;93(3):750-757. doi:10.1016/j.gie.2020.08.038