How AI is Revolutionizing Oncology Care at Your Practice

AI-driven solutions can streamline oncology clinical workflows and improve patient outcomes.

Photo: National Cancer Institute

Rapid advancements in oncology research and development (R&D) are improving treatment efficacy, extending the overall lifespan of cancer patients, and helping them experience a higher quality of life.

However, oncologists still have to navigate complexities such as variations in drug responses or emerging drug resistance across the patient populations they treat. But it’s challenging to do so with current traditional tools.

That’s where artificial intelligence (AI) comes in.

By integrating AI into clinical workflows, oncologists can make more informed and timely care decisions.

Read on to learn how AI-driven solutions can revolutionize oncology care at your practice.

It’s typically easier to start patients on targeted cancer therapies when the cancers are in their earlier stages. However, current gaps in detecting cancers early in their progression present significant challenges to delivering novel, timely treatments to patients.

For instance, high-incidence cancers like lung cancer are more difficult to treat when diagnosed later in their progression. The five-year survival rate for patients first diagnosed with early-stage lung cancer is 63%, which is dramatically higher than the 8% rate for those diagnosed with the same disease at its later stages.

Likewise, early detection of breast cancer significantly increases the likelihood of patient survival—demonstrated by the 99% survival rate for localized breast cancers at the time they are detected.

Today, AI-enabled imaging solutions can identify localized lesions and predict the risk of these lesions becoming malignant over time, especially in lung and breast cancers. Beyond analyzing imaging data, these tools can evaluate tumor biomarker models and form predictions that help oncologists discover malignancies at early stages.

By identifying potential indications of cancerous growth early on, oncologists can effectively target cancers before they metastasize into more complex, harder-to-treat tumors.

In typical cancer screening workflows, it’s challenging for teams of radiologists—already grappling with staffing shortages—to read large volumes of patient images and detect cancers with high sensitivity and low reader variability.

Reading images manually and delineating qualitative features such as tumor density, cellular composition, and anatomic relationships between adjacent tissues takes time, even for seasoned radiologists.

With AI-based radiomics tools, clinicians can characterize various cancers based on tumor markers. These tools powerfully identify complex patterns in patient imaging data that may not be immediately obvious when observing with a human eye.

By training AI models on thousands of data points, they can learn to differentiate between malignant and benign tumors. Such capabilities are helpful when tracking morphological changes in individual nodules localized in high-risk, difficult-to-biopsy tissues in lung cancers.

For oncologists to make informed decisions about starting, modifying, or maintaining the course of a patient’s treatment, they typically evaluate a comprehensive set of data, which might include:

The stage to which the cancer has progressed

The patient’s unique medical history

Genomic data (if the tumor has been sequenced)

Without understanding the combinatorial role of these factors in the evolving biology of a specific cancer, it’s challenging for oncologists to make fully informed decisions about patient treatments.

For instance, the changes in molecular biomarkers observed in non-small cell lung cancer patients may determine which first-line therapy is best to start them on. But there’s simply too much data to analyze and make decisions quickly, especially when dealing with complex multimodal datasets.

Visualizing these disparate data and extracting the necessary insights requires fast, automated AI tools that can identify difficult-to-detect signals buried in these immense datasets.

These AI tools can also help clinicians understand the relationships between structured or unstructured data, predict certain biological behaviors, and inform clinical decision-making and management.

Clinicians directly involved in clinical trials can optimize their study design and stratification and find eligible patients for specific studies with the help of AI-driven clinical trial matching and screening tools.

Using these tools, oncologists looking to enroll patients in promising studies can also determine ahead of time which patients are likely eligible for these studies—saving time and resources if the patients don’t qualify.

With features like natural language processing (NLP), AI matching tools can compare the eligibility and exclusion criteria listed for specific trials with patient data to identify the trials for which patients can qualify.

Such features are helpful when planning to enroll patients in breast cancer clinical studies, which typically have complex inclusion and exclusion criteria based on patient age, comorbidities, and performance status.

There’s growing evidence that AI is transforming oncology care and helping clinicians meet patient needs more effectively.

Adopting an AI solution at your oncology practice not only enhances early detection of high-incidence cancers or their characterization but can also help streamline data visualization and guide clinical triaging and study stratification.

So, how do you adopt a robust AI solution into your existing clinical workflow?

You can speak with an expert who understands the ins and outs of AI-driven automation within oncology clinical workflows. With their guidance, you can determine and implement the best path for adopting these AI tools into your specific practice.