Artificial intelligence (AI) has vast potential within the medical field. It’s particularly worthwhile in procedures like endoscopy that, despite being common, require complex evaluation and expert insight. The health care industry hasn’t ignored this chance, either, as early uses of AI in endoscopy have already driven promising results.
Endoscopy is the strategy of examining patients’ bodies using a skinny, flexible tube equipped with a camera and light-weight. While the procedure itself is fairly straightforward, making sense of the photographs will be difficult. AI has already offered solutions along several fronts.
1. Improved Abnormality Detection
Probably the most significant AI breakthrough in endoscopy is how machine learning improves detection. Endoscopes often seek for small abnormalities like precancerous polyps or lesions. Early studies suggest machine learning can detect these warning signs more accurately than humans.
As early as 2017, AI algorithms could detect polyps with 86% accuracy, while expert doctors only achieved 74% accuracy. Since then, machine learning models have reached accuracies as high as 96.4%. Such systems can often spot abnormalities that humans may miss, too.
In practice, AI models won’t replace specialists. Nevertheless, physicians can use them to realize greater confidence of their diagnoses and not using a time-consuming process. In consequence, health care systems can provide patients the assistance they need earlier of their conditions’ timeline, resulting in improved outcomes.
2. More Reliable Classification
Accuracy isn’t the one advantage of AI in endoscopy. Machine vision models are also adept at classification — or differentiating between various kinds of detected abnormalities.
Classification is essential because various kinds of polyps or lesions require different approaches to treat effectively. Consequently, AI models could ensure people get the care they really want by detecting subtle differences between abnormal growths.
One neural network was able to differentiate between colorectal polyps with as much as 87% accuracy, putting it on par with expert pathologists. Using this model, doctors could diagnose a patient without additional review, resulting in quicker, more accurate treatment. In cases where the AI and initial diagnoses differ, the additional opinion could help staff consider additional possibilities to enhance diagnostic confidence.
3. Streamlined Procedures
It’s also price noting that endoscopy AI is fast on top of being accurate and specific. While certainty is crucial thing in a medical diagnosis, speed matters, too. A quicker process means treatment can begin sooner and doctors can see more patients in less time.
Some neural networks have proved effective at detecting polyps in real time, removing the necessity for post-endoscopy evaluation for greater confidence. Other algorithms may not deliver immediate results but can take minutes as a substitute of the hours or days a lab procedure would take.
When doctors can improve their detection and classification without taking additional time, it results in dramatically improved patient outcomes. Earlier treatment aside, the time savings let a constrained workforce serve a bigger variety of patients, making turnover and labor shortages less impactful.
4. Lower Cross-Contamination Risks
The uses of AI in endoscopy transcend the procedure itself. Stopping cross-contamination between tests can also be necessary, as roughly one in 1,000 colonoscopy patients get infected from the method. AI may also help by ensuring cleaner, safer storage and sanitization.
Smart drying cabinets employ HEPA filtration, positive pressurization, and similar steps to dry and disinfect endoscopes between procedures. Algorithms push them further by monitoring interior conditions in real time. They’ll then adjust settings as crucial to keep up sterile storage as cabinets open and shut.
Alternatively, AI can predict equipment failures and alert staff of the difficulty before it compromises endoscope cleanliness. Processes like this are already common in smart homes and industrial HVAC equipment, but within the medical field, they may prevent infections and improve overall health.
5. Expanded Specialist Training
AI can also be a useful training tool. Endoscopy is a posh, specialized process, but equipping prospective specialists with the crucial skills and knowledge is commonly too slow to maintain pace with rising demand. Considering how the U.S. alone will be short 86,000 physicians by 2036, something needs to vary.
Because AI is so accurate, it’s a helpful strategy to show trainees what various polyps, lesions or other abnormalities appear to be. Doctors in areas without as many expert specialists or other training equipment profit essentially the most from this use case. Through the use of AI as a guide, they will quickly improve their detection and classification skills.
As AI streamlines specialist training, reliable endoscopy and related care will turn into accessible to more people. Such a shift could work against long-standing barriers to care between different demographics.
Potential Downsides to AI in Endoscopy
As helpful as AI will be in endoscopy, it comes with a number of drawbacks. Skewed training data can cause AI to amplify human biases, and lots of historical medical records lack equal representation. Consequently, these tools might not be reliable for each patient demographic.
Collecting enough data to coach these models can also introduce privacy concerns. The health care industry faces strict regulations on patient data security, so it may very well be difficult to balance model reliability with cybersecurity and compliance.
Over-reliance on AI introduces one other concern — such diagnostic tools are highly accurate but imperfect. Doctors may turn into complacent over time and take their input at face value, resulting in rushed screenings and potential misdiagnoses. Such use cases would counteract the advantages of using the technology.
Using AI in Endoscopy Safely
Thankfully, there’s a protected way forward. Once medical organizations recognize these downsides, they will construct safer AI policies to mitigate the negative effects while capitalizing on the advantages.
Greater care during training is paramount. A various team must oversee development and often audit the algorithm to seek out and proper biased tendencies. During this phase, teams may use synthetic data to guard patient privacy while providing a bigger training database. Models trained on synthetic data will be more accurate than others, so it will be the best way forward, even outside of privacy and bias concerns.
Finally, health care systems must train doctors to make use of AI fastidiously. They have to emphasize how human experts should at all times have the ultimate say and teach professionals about AI’s shortcomings to stop them from over-relying on the technology.
AI Is Driving the Endoscopy Field Forward
While challenges remain, it’s hard to overlook the potential of AI in endoscopy. Some hospital networks are already recurrently using AI-assisted screenings, and as technology improves, its adoption will likely expand. Broader usage, in turn, will result in growth in relevant datasets and the event of additional best practices.
As such trends proceed, AI could reshape the sphere of endoscopy. These procedures will turn into more accurate, precise, accessible, efficient and protected. Each doctors and patients will profit from that shift.
