Artificial intelligence in improving efficiency in breast cancer care

“Artificial intelligence is the science of making machines do things that would require intelligence if done by humans” -John McCarthy

With rapid strides being made in AI, cancer care cannot stay immune from it. Machine learning and its application in breast cancer care is here to stay and will take up a more central role. AI can significantly enhance breast cancer surgery outcomes in the following ways. 

Preoperative planning: AI systems can analyse imaging data to help surgeons map out the best surgical pathway and give a more detailed patient information

Predictive Modeling via patient data, AI can predict complications or the likelihood of cancer recurrence.

Preventing unnecessary breast biopsy: AI allows you to determine whether an area flagged during a mammogram or other breast imaging contains cancer or not. AI systems may be able to reduce the number of unnecessary biopsies.  For instance, an AI tool called iBRISK (Intelligent-Augmented Breast Cancer Risk Calculator) could accurately predict whether abnormal tissue flagged by doctors was more likely to be benign or cancerous, according to a study in Radiology: Artificial Intelligence.

Image analysis: AI aids in real-time analysis of mammograms, the Lancet Oncology describes how researchers used AI to help screen mammograms of more than 80,000 women in Sweden. Half of these women had their mammogram read by AI before it was looked at by a radiologist, while the other half had theirs read by two radiologists. The study revealed that the AI group had 20 per cent more cancers detected than the radiologist-only group. 

Screening tools: NIRAMAI breast cancer screening test, Thermalytix, is a computer-aided diagnostic engine that is powered by artificial intelligence. The solution uses a high-resolution thermal sensing device and a cloud-hosted analytics solution for analysing the thermal images for reliable, early and accurate breast cancer screening.  

Recurrence scoring: AI predictions of recurrence scoring of breast cancers can help avoid potential chemotherapy complications by identifying patients in whom chemotherapy has no role thereby reducing a significant load on the existing healthcare. A study published in June 2023 found that AI was more accurate in predicting breast cancer risk than the Breast Cancer Surveillance Consortium (BCSC) risk model. The BCSC Risk Calculator estimates a woman’s 5-year risk of developing invasive breast cancer based on such factors as a woman’s age, her family history of the disease, race/ethnicity, breast density, and any history of benign breast biopsies. Using screening images collected from 13,600 women who had normal mammograms, five AI systems generated risk scores for developing cancer over that five-year period. AI was more accurate than the BCSC model in predicting breast cancer

AI-supported augmented reality technology overlays critical information onto the surgical field, providing surgeons with real-time, data-driven insights, enhancing their ability to navigate complex anatomy and at the same time train future breast surgeons and bringing about standardisation surgical outcomes thereby improving overall cancer-free survival

Personalised surgery model: By understanding the unique genetic and molecular profile of a patient’s cancer, AI can support decisions on whether surgery is needed and recommend the best surgical approach.

Postoperative care optimisation: AI systems can monitor patient recovery through wearable tech and data analytics, predicting complications and enabling timely interventions post-surgery.

Research and Drug Development: In oncology research, AI processes vast amounts of biomedical data to identify new drug targets and conduct virtual screening of chemical compounds, speeding up research and development.

AI in cancer care before becoming the norm, needs to address a few concerns like

1. Data privacy concerns: Extensive data collection and sharing can pose significant privacy concerns if not managed properly.
2. Bias in algorithms: AI systems may perpetuate existing biases in healthcare data, leading to disparities in care.
3. Overreliance: Dependence on AI might lead to skill atrophy among healthcare professionals or errors if technology fails.
4. Cost: Implementing and maintaining advanced AI systems can be expensive, potentially increasing healthcare costs.
5. Ethical issues: Decision-making in patient care driven by AI can raise ethical questions, particularly if outcomes are unfavourable.

Whether AI is predominantly a boon or a bane depends largely on the frameworks and safeguards put in place, ensuring that the benefits are maximised while minimising potential drawbacks. Careful integration, along with transparency, accountability, and ongoing research, can help tip the balance towards AI being more of a boon in breast cancer management.