One of the main strategies to eliminate cervical cancer is the consistent screening of people with cervixes. However, traditional screening methods are challenging in low and middle-income countries, where approximately 85% of cervical cancers occur. Artificial Intelligence and Machine Learning hold great potential for cervical health screenings, especially in low and middle-income regions where traditional screenings have become a bottleneck to screening performance.

In the areas of the planet where Pap smears and HPV testing are less common, healthcare workers use a less accurate screening method. They use diluted acetic acid on the cervix and a speculum to look for white areas. These white areas are typically an indication of risk for cervical cancer[1]. One of the main challenges of traditional colposcopy is the dependence of experienced operators, the variability between operators, a shortage of experienced colposcopists, an inadequate diagnostic standard, and the lack of strict quality control—this challenge results in discrepant reporting and documentation of colposcopy impressions. While training courses could be a way to mitigate some of these challenges, implementing the training in a short period of time may not be feasible. This is where AI and ML come into play. AIML can address the colposcopic bottleneck and assist colposcopists in imaging judgment. Automating the workflow of a colposcopy examination, and thus creating a novel cervical cancer screening model, could potentially reduce false negatives false positives and improve the accuracy of colposcopy diagnosis.

Considering the healthcare limitations that many low and middle-income regions face, AIML is being adopted and integrated into the existing cervical cancer screening platforms. This addition helps strengthen the screening, diagnostic and patient record management. Ugandan scientists have developed a PapsAI, a digital pathology platform, for automated cervical cancer diagnosis and management within resource-constrained environments[2]. Automated visual evaluation (AVE) is a machine learning algorithm that assesses digital images of the cervix for cancer. This is a fast, easy and accurate screening tool that can integrate with a smartphone or digital camera.

Both of these AIML innovations significantly enhance cervical cancer diagnosis, leading to faster, more reliable, and efficient cervical cell images from pap-smears.

While AIML devices have a long way to go for the diagnostic and treatment of diseases, there is substantial progress being made. As with any new drug or medical device, regulatory approval must be granted before the product can be marketed. EMMA International offers full-circle consulting and can help with regulatory strategies to submissions clinical trials and post-market surveillance. Talk to the experts at EMMA International today by calling 248-987-4497 or emailing


[2] W. William, A. Ware, A.H. Basaza-Ejiri, J. Obungoloch, A review of image analysis and machine learning techniques for automated cervical cancer screening from pap-smear images, Computing Methods Programs in Biomedicine 164 (2018) 15-22: doi: 10.1016/j.cmpb.2018.05.034.

Alexis Ferrier

Alexis Ferrier

Alexis is a Technical Writer at EMMA International. She has research and development experience in both device and drug products. Additionally, Alexis has experience in quality assurance, quality control, regulatory requirements, and international compliance. She holds a Bachelor of Science in Chemistry from Michigan Technological University.

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