Breast cancer is one of the most prevalent forms of cancer among women worldwide. The World Health Organization (WHO) reports that almost 2.3 million new cases are recorded yearly, with more than 650,000 deaths annually.

The high mortality rate has spurred extensive research into methods that can help detect breast cancer earlier and prevent death.

Artificial intelligence (AI) is a rapidly evolving technology gaining popularity in the healthcare industry. It involves using computer algorithms to analyze patterns in large datasets.

AI has several applications in medicine, including drug discovery and diagnosis. Researchers are now exploring the potential of AI in early breast cancer detection, diagnosis, and prevention.

Early Detection of Breast Cancer

The key to improving survival from breast cancer is early detection. Mammography, the primary tool for detecting breast abnormalities, involves using X-ray scans to visualize internal breast tissue.

But, mammography has limited sensitivity and specificity, and it can miss early-stage cancerous abnormalities. As a result, radiologists may need to perform further tests, such as an ultrasound or MRI.

AI can enhance mammography by assisting radiologists in analyzing digital mammograms faster, more accurately, and more efficiently, reducing rates of false-positive and false-negative results.

Several AI tools, such as ImageChecker, iCAD, and R2 Technologies, are FDA-approved to help radiologists read mammograms.

AI-Assisted Diagnosis

Interpreting mammography results can be challenging, particularly because different radiologists may interpret the same mammogram differently.

Artificial intelligence could eliminate this variability by providing automated, more precise, and standardized analysis.

A study published in the journal Nature found that a deep learning algorithm outperformed human radiologists in interpreting mammograms.

The algorithm detected 9.4% more cases of breast cancer and reduced false positives by 5.7% than its human counterparts. Other researchers have also reported similar findings, suggesting that AI could reduce missed diagnoses that occur during mammogram analysis by providing a second opinion.

Preventive Screening

Experts suggest that screening women for the early detection of breast cancer should begin at age 40 and should continue annually. However, conducting annual mammograms on all women can be an expensive and time-consuming process.

Therefore, researchers are exploring the role of AI in predicting a woman’s risk of developing breast cancer through biomarkers and imaging.

The machines use a form of machine learning called a convolutional neural network (CNN) to evaluate breast tissue patterns in mammograms and estimate an individual’s future risk of developing breast cancer.

A study found that AI was almost twice as effective as predicting a woman’s risk of developing breast cancer as established clinical risk models.

Tailored Treatment

Breast cancer is not a “one-size-fits-all” disease—the complexity of the condition necessitates tailored therapies.

Individual treatment requires analyzing multiple factors, including breast cancer subtype, disease stage, and the presence of biomarkers. These factors can help determine the type of treatment and the likelihood of recurrence.

AI can aid in locating the right treatment by analyzing a patient’s medical history, imaging studies, and biomarkers to devise a personalized approach to care.

Radiologists are also using AI to determine the optimal dose of radiation a patient should receive. The approach entails feeding machine data on the patient’s medical history, imaging results, and anatomy to produce a customized radiation therapy plan.

Limitations of AI in Breast Cancer Prevention

Despite the potential benefits of AI in breast cancer prevention, there are limitations to the technology.

One of the challenges with AI is the “black box” problem, whereby the algorithms provide specific outcomes, but it is challenging to understand how the model arrived at these outcomes. As such, AI outputs may not always be trusted, which could hinder its widespread adoption.

Another challenge is that the AI algorithms need to be trained on significant volumes of data to function effectively, which can be complicated to access.

There is also a risk that AI algorithms could exclude patients with variable conditions or symptoms that differ from the norm. This means that AI can only be a supplement for healthcare professionals, not a replacement.

Conclusion

Artificial intelligence is revolutionizing the healthcare industry and has the potential to play a crucial role in breast cancer prevention. AI can improve early detection, assist in diagnosis, and aid in tailored treatment.

However, AI’s integration in breast cancer prevention requires careful consideration and monitoring to ensure that the process remains safe, ethical, and effective.