How AI Can Help Ease the Stress of a Cancer Diagnosis

Iat a time when cancer rates continue to riseThe emotional impact that a cancer diagnosis and subsequent treatment pathway has on a patient is becoming a critical concern. A 2021 study found that globally, 27% of cancer patients have depression. The same study notes that this rate increases by 0.6% per year.

Most patients and loved ones facing cancer may be faced with a flood of emotions, facing feelings of fear, confusion and uncertainty. Not to mention, both patients and doctors face the daunting task of navigating complex treatment decisions, as well as the intricacies of a complicated healthcare system, in the midst of this emotional turmoil. Today more treatment options are more available than ever, and most patients will receive a combination of treatments. While the increase in options offers more hope, it also offers more paths to take to get to the ideal treatment. Determining the course for each patient based on their unique characteristics and clinical needs can be an uphill battle without the right tools to guide the way.

In the cancer journey, doctors are forced to find a balance to combine the aggressiveness of treatment to aggressiveness of the tumor. Without prediction of how cancer will progress and how quickly, both doctors and patients may find themselves navigating the path forward without clarity or confidence.

But what if technology could help us simplify the problem? With rapid advances in AI, we are witnessing a shift in the way we deliver healthcare. AI-based cancer testing, in particular, has the potential to provide personalized information, empowering both patients and doctors to make more informed treatment decisions and ultimately improve patient outcomes, both mentally and physically.

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One of the factors that most contributes to the mental anguish of cancer patients is the overwhelming uncertainty associated with treatment decisions. Patients are afraid of what they are signing up for, often asking “Will this therapy make a difference for me” or “Is it worth the adverse effects?” These concerns are especially valid because cancer treatments are known to have potential life-altering side effects such as loss of sexual function, loss of bone density, decreased muscle mass and even increased risk of death. When these questions go unanswered, they can greatly impact the patient’s psychosocial adjustment, health behaviors and quality of life.

A patient’s mental health can also prevent them to seek treatment or consistently follow your treatment plan. A 2024 study on Cureus found that patients who do not follow their treatment plan are more likely to have worse outcomes, including increased doctor visits, higher hospitalization rates, longer hospital stays, disease progression, and increased mortality rates.

AI can serve as help. Today there are AI-enabled tests that can quickly analyze real-world data and translate it into digestible, personalized insights, enabling a more personalized approach to cancer therapy. For example, there are tests for localized prostate cancer that use enormous amounts of data from digitized pathology slides to extract hidden patterns and corresponding knowledge about an individual patient’s disease. This is data that doctors are not able to interpret, as the human eye cannot discover the patterns in these large data sets without technological support. The current standard of care is to rely on a pathologist, who will review the pathology slides, determine if cancer is present, and assess cancer risk, e.g., Gleason score for prostate cancer. While these general categories are effective, the reality is that pathology slide scanning can identify between 10,000 and 40,000 image patterns from just one patient’s data. Powered by AI, the test can analyze the scanned slide to determine in much greater detail each patient’s specific risks and provide detailed information about prognosis and treatment benefits. This benefits the doctor not only by saving time and alleviating cognitive load, but also by creating confidence when it comes to determining whether or not to advise a patient to undergo a specific treatment.

Integrating AI into the decision-making process not only puts the patient at the center but also provides greater clarity throughout the cancer treatment journey. How does this happen? Equipping patients with accurate information derived from their unique data and potential outcomes. For example, in prostate cancer, AI tests now have the ability to analyze images from digital biopsies and clinical data from patients to identify those who would benefit from hormone therapy. A 2023 study validating this test showed that only one-third of the patient population who received the therapy demonstrated benefit, meaning that two-thirds of patients were overtreated and could have safely avoided the therapy.

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In addition to the toll that cancer therapy has on the body, there is also the physical stress and apprehension surrounding the potential need for multiple biopsies to obtain tissue samples.

For example, genomic testing may offer the potential for personalized care, however, it often requires the consumption of tissue samples. As new tests emerge, a portion of the tissue sample is needed for reanalysis. Tissue is finite, which presents a significant challenge as each genomic test decreases the tissue available for future testing or procedures. However, the rise of digital pathology allows the opportunity to forgo additional tissue consumption and instead, AI tests can explore the same scanned samples as many times as necessary, producing new information without the need for additional invasive procedures. This approach not only preserves the patient’s tissue, but also streamlines prognostic procedures, potentially eliminating the need for subsequent biopsies.

It is also important to recognize the challenges of implementing AI in clinical care. One of the reasons there is racial bias in AI, for example, is because there is lack of racially diverse clinical data, leaving the algorithms to be trained from a primarily white patient population. This is why researchers must properly train algorithms on racially diverse data that is representative of the patient population. Technology is only as good as the data it receives.

There is no argument that AI will have a significant impact on the healthcare industry. While technology can help ease administrative burdens and streamline workflows, its most powerful potential can be found in its ability to simplify complex problems and signpost solutions. Harnessing this capability will pave the way for patients and doctors to find hope, confidence and comfort through their cancer journey.