by Russell Emmental
Everyone dreads going to the doctor. After submitting to a round of screenings and scans, the last thing anyone wants to hear is bad news. But sometimes, good news is misinterpreted and delivered to patients, wrecking their confidence and optimism.
A major goal of modern medical technological development has been to reduce the incidence rate of false positives. Now that artificial intelligence is being incorporated into the medical field, doctors and nurses are getting even better at interpreting results accurately.
Here’s how generative AI is making test result interpretation even more precise.
Deep Learning
The difference between traditional AI and generative AI is that the former type relies on data to make predictions, while the latter generates its own data as it operates. In other words, generative AI learns just like us, and for that reason, it has begun to transform the face of clinical practice.
This kind of deep learning is being employed in a variety of contexts, from streamlining complex information processes to improving the accuracy ofX-ray results like those provided by X-ray systems from Patient Image.
Automatic Detection
One 2021 study found that the implementation of AI systems helped reduce false positives in the interpretation of mammogram screenings. Conducted by a team of radiologists and data scientists at NYU Langone Health Center, mammogram screenings were selected as the subject of the study because they traditionally have a high false positive rate.
Culling from a dataset of over 280,000 exams, the researchers found that the implementation of AI decreased the false positive rate by 37.3%. That has wide-ranging impacts, from costing practitioners less in follow-up exams and improving patient satisfaction.
Improved Accuracy
Researchers have been aggressively testing AI-facilitated screening services to determine their efficacy and helpfulness. One study indicated that when AI provided false or misleading results, doctors were more likely to believe them.Â
That’s one potential flaw when considering the implementation of AI to reduce false positives. But as clinicians and other healthcare professionals learn to work with AI, like any other tool, kinks will likely be resolved. AI isn’t being viewed as the answer to all of our medical technology problems, but simply another tool to help alleviate them.
Discovering Mechanical Flaws
Another benefit of incorporating AI into medical technology is discovering the flaws in machines that generate false positives in the first place.
The American Association for Cancer Research noted that incorporating AI into imaging processes can help detect mechanical and algorithmic flaws. Deep learning was successfully used to identify features in mammographic imaging machines that either aren’t working or aren’t working as well as they should. Thanks to AI, not only are results becoming more accurate, but the machines we use to generate those results are improving too.
Improving Patient Care and Saving Lives
The fewer false positives that are handed out, the better healthcare becomes for everybody. Receiving a false positive not only causes undue stress but also shatters trust in our healthcare professionals, who work around the clock to help us live healthier lives.
Embracing AI means accepting a healthier and more accurate future.
