Healthcare has seen shifts in standards of care over the years predominantly from a patients’ perspective. The consumerization of healthcare is a shift that we have seen in this country as patients take more control of (and assume more financial responsibility for) their healthcare costs. This means patients are not only looking out for their health, but for their pocketbooks. And while financial reasons may play a role in how our patients make decisions surrounding routine mammography visits and screenings, there are other key factors that lead us to believe that AI will soon be the standard of care in mammography.

From a physician’s standpoint, standard of care models are set forth by trusted scientific societies, through organizations such as the FDA and the Mammography Quality Standards Act (MQSA) and even payer protocols.

Today, we discuss how AI will soon become the standard of care for breast imagers while also considering the needs of our patients. Here are 5 reasons we feel that AI will grow as the standard of care in mammography in the coming years:

AI in mammography reduces burnout

Radiology is many times underrepresented in medical school curricula and as learning opportunities have lessened during COVID-19, we are at a point where more radiologists are needed. Until we can move our practices in adequate staffing levels, we must also be cognizant of burnout and how to incorporate tools and technology to mitigate this. Radiologists work long hours and may need assistance lessening workloads. Implementing AI in mammography can help reduce radiologists’ burnout and, in turn, can make for a more pleasant patient-physician experience. When physicians are feeling happier and less stressed, it reflects on their communication and bedside manners with patients.

AI fits with the ongoing need for personalized healthcare (in some instances)

While patients have a hand in driving their healthcare experience—looking to physicians and practices to provide customized experiences—it’s up to each practice and practitioner to decide the best tools. AI may be one resource to support these initiatives.

It’s important to note that there are two different types of processes associated with AI. One that provides a more personalized approach and one that acts as a faster resource to compute large quantities of data to make finite analysis.

  • Natural Language Processing (NLP)
  • Deep learning (DL)

The first, NLP, is a technique that looks at patterns over time to provide personalization or a change of course due to prior system learnings and activity. You’ve likely experienced instances of NLP through tools like chatbots or smart virtual assistants—technology that changes sentiment, response or outputs based on information coming in at certain times.

The latter, deep learning, is a branch of machine learning that MammoScreen® uses, that can provide intelligence from all cases to find a way to identify certain features in a scan (i.e. malignancies that might otherwise be missed). In simple terms, deep learning acts to replicate brain functionality, imitating information processing but with large volumes of data. It is a process that is used extensively for object recognition, which is why it works well for screening mammograms.

AI is your second set of eyes

We have seen the need for better efficiencies as we deal with the global pandemic and, with reduced staffing, this can mean an investment in technology to ensure we continue the best possible care to patients. While initial mammograms are only the first step in the diagnosis process, implementing AI at the onset can provide you with a second set of eyes to detect suspicious areas that you may not otherwise see in a screening without the use of AI.

AI can lower recall numbers

Approximately 100 out of every 1,000 screened women are recalled for more tests but only 4 to 5 of them will be diagnosed with cancer. This can put undue stress on patients as they wait for further test results. With an ACR recommendation of recalls being lower than 12%, AI in mammography may help in this area.

By utilizing deep learning AI in mammography, radiologists may detect more subtle lesions than the human eye alone (and flag spots of less concern that might have otherwise been called suspicious by radiologists using other methods). This may provide physicians the ability to catch cancers earlier, reassure your patients and reduce their stress or anxiety around mammogram screenings.

Learn how to move your radiology practice forward by adopting AI as a standard of care in mammography. Our subscription-based model assures you always have the latest in technology to provide patients with accurate readings. Sign up for a free MammoScreen® demo to see the difference.