9 ways artificial intelligence revolutionizes cancer prediction, detection, and treatment

The first applications of AI in healthcare date back to the 1970s. And as time passes, the technology becomes more advanced, and new subtypes emerge. The most recent AI subtype, generative AI (Gen AI), is gaining popularity in the field. This technology performs exceptionally well when analyzing enormous datasets, and unlike classic AI, Gen AI can produce original content, such as reports, clinical documentation, and realistic synthetic data that can be used for educational purposes or algorithm training.

You can find more information on the differences between AI and generative AI and Gen AI use cases in healthcare and pharma on our blog.

Generally, AI and its subtypes have many benefits in healthcare. Here are the five key advantages of incorporating artificial intelligence into cancer detection and treatment processes:

Now, let’s investigate some practical applications of artificial intelligence in cancer prediction, diagnosis, treatment, and research.

Artificial intelligence can not only detect existing cancer but also identify people at high risk of developing the disease before it sets in, enabling doctors to monitor high-risk patients closely and intervene immediately when needed.

A research team from the Radiological Society of North America discovered that AI algorithms analyzing mammograms to predict cancer risks outperformed the standard Breast Cancer Surveillance Consortium (BCSC) risk model for breast cancer prediction. The researchers tested five AI algorithms, and all of them did better than BCSC when predicting cancer development in the next five years.

AI can help detect different cancer types. Besides the breast cancer endeavor described above, there is a deep learning model for lung cancer prediction. It analyzes low-dose computed tomography scans and determines whether a patient will develop cancer in the coming year with 86%–94% accuracy.

Using AI to predict cancer from medical imaging is becoming popular among cancer researchers. But there are also AI-based tools that can anticipate the disease based solely on patients’s medical records.

There are many applications of AI in medical imaging. In 2023, the FDA approved 122 AI and ML tools for radiology alone. Detecting and classifying cancerous tumors is among the most noticeable use cases.

To show one example, the Providence Health System teamed up with the University of Washington and Microsoft to build Prov-GigaPath—an AI model that can detect cancer in medical images and tissue samples. This algorithm was trained on over a billion pathology image tiles taken from 30,000 patients at Providence, which is a remarkably large training dataset.

Prov-GigaPath is an open-access model that people from all over the world can utilize.

AI-enhanced blood tests can help doctors detect cancer more accurately. An article published in Cancer Cell International claims that blood profiling, where AI algorithms analyze ctDNA and miRNA plasma profiles, is a superior method of detecting and monitoring cancer compared to regular CT scans.

Researchers at the Johns Hopkins Kimmel Cancer Center developed a novel AI-based technology for diagnosing lung cancer through blood tests. This approach was tested on blood samples from 796 subjects in the US, Denmark, and the Netherlands. Researchers combined this blood test with protein biomarkers, patients’ clinical risk factors, and CT scans. As a result, they accurately spotted cancer in 91% of subjects with early disease stages and in 96% of patients with advanced cancer phases.

AI in cancer diagnostics can help individuals get initial feedback on their abnormalities through self-diagnosing and without making an appointment with the doctor. While diagnoses made with such tools are not definitive and don’t substitute for a doctor’s visit, AI-based self-diagnosis solutions could promote early-stage detection of cancer in a broader population.

For instance, SkinVision, a digital health startup based in Amsterdam, has developed a mobile app that assists users in screening their skin abnormalities for cancer. The application can capture a photo of a suspicious spot on the skin with a smartphone camera and submit it for evaluation. An AI algorithm analyzes the color, texture, and shape of the lesion and gives feedback to the user within 30 seconds. The algorithm is believed to offer 95% accuracy in skin cancer detection.

Immunotherapy strengthens the immune system, allowing it to destroy cancer cells.

The UK-based biotech company Etcembly uses its proprietary Gen AI solution, EMLy, to scan the genetic makeup of T-cell receptors (e.g., the body’s immune system cells). EMLy is based on a large language model (LLM) and can analyze millions of genetic code lines. Its goal is to discover which receptors have the largest potential to form bonds with cancerous cells and destroy them without harming healthy cells. Immunotherapy drugs, called T-cell engagers, will use this information to focus on the selected cells and bring them closer to the cancer cells.

With traditional clinical trials, it takes a drug from 10 to 15 years to move along the three trial stages. AI can accelerate clinical trials and take on the lion’s share of drug discovery and development.

Cancer research centers are teaming up with AI development companies to advance their scientific endeavors. For instance, the Institute of Cancer Research together with IDIBELL teamed up with an English biotech company, Vivan Therapeutics, to overcome drug resistance with the help of AI and produce cancer treatments focusing on the cancer-instigating protein KRAS.

An industrial Gen AI startup, Zapata AI, is also working on destroying KRAS. They used Gen AI running on quantum hardware to produce over a million candidate drugs. Then the researchers filtered the results to eliminate molecules with less potential. The 15 remaining molecules were tested through cell-based essays. The successful compounds showed superior properties and were very different from the existing KRAS exhibitors.

Genome sequencing-based cancer AI therapy enables doctors to characterize tumors and develop personalized treatments. Specialized AI algorithms can also identify tumor origin after it has metastasized throughout the body and it’s hard to tell where it originated.

Dana-Farber Cancer Institute, together with MIT, developed a machine learning model called OncoNPC that can analyze a tumor’s genomic sequences to determine its origin. The researchers trained the model on 30,000 patients with 22 different unknown cancer types and then tested it on 7,000 tumors. The model was 80% accurate in determining a tumor’s origin. For some tumors, the model rated its classification as highly accurate. In these cases, the accuracy level surpassed 95%.

With these promising results, the researchers were encouraged to apply the model in real life. OncoNPC analyzed 900 unknown tumors and classified 40% of them with high confidence.

Generative AI is good at processing unstructured data, which humans and traditional search methods struggle to handle. Large language models, such as BioGPT, can transfer unstructured doctor’s notes into organized, easy-to-process information. These models can also search published medical literature, looking for connections between different factors, and organize everything into knowledge graphs.

In an attempt to facilitate cancer research, the Moffitt Cancer Center teamed up with Deloitte, Oracle, and Nvidia to produce an AI-powered platform that can identify and document health conditions that impact cancer care.

Artificial intelligence’s role in cancer treatment expands to offer platforms that assist cancer patients and help them communicate with their physicians.

For instance, Hurone AI used Amazon’s machine learning platform Bedrock to build its own AI solution that connects patients diagnosed with cancer to qualified doctors. The tool prompts patients to answer specific questions so that doctors can get a better idea of their state. The AI also saves all interactions in the corresponding EHR entries.

AI brings about many challenges. Here are the most relevant ones for the healthcare sector.

Unfortunately, bias is common among AI models. They can discriminate against certain ethnic groups and even against hospitals. Algorithms that work well for one care center may perform poorly when transferred elsewhere. A research team from the University of Chicago demonstrated how a machine learning-powered cancer detection application taught itself to consider the medical institution submitting the image as a factor in determining whether the scan shows signs of cancer.

Bias is also painfully common when diagnosing skin cancer in people of color. Many AI models are still trained predominantly on white people, as the medical community fails to gather enough representative data for diverse skin colors.

Some pathologists are afraid that by working with AI on cancer treatment, they are simply training their replacement. They hear quotes, such as, “An AI algorithm can learn from a much larger library than a radiologist can. In some cases, a million images or more.” And they start worrying and thinking that AI can surpass them in everything they do.

The reality is that AI can be great at one task or at a few tasks, but it will not replicate pathologists’ scope of work. So, AI won’t replace doctors, but doctors who use AI might replace the ones who don’t.

Healthcare data is typically stored in heterogeneous and unstructured ways, and it’s challenging to standardize terminology across medical facilities.

Initiatives, such as patient-reported outcome measures (PROMs), allow collecting standardized data from the start when patients experience distress. However, putting pressure on physicians to collect more data can lead to burnout and other increased workload problems.

PotentiaMetrics, a healthcare analytics company, employed ITRex to develop an AI-enabled web portal for collecting, managing, and presenting patient data. The platform gathered and maintained patient information from the moment of diagnosis and throughout their survivor journey. Users would enter their data through a web-based questionnaire and generate reports. Also, the platform helped physicians craft personalized treatment plans based on the detailed information they received.

More training data leads to higher system accuracy, and this is a challenge in the healthcare sector. The existing medical image datasets are significantly smaller than natural image sets. For example, the LUNA dataset of CT images contains only 888 instances, and the Indian Diabetic Retinopathy Image Dataset (IDRiD) includes approximately 600 images.

One way to address this issue is to synthesize medical datasets using generative AI. But it will consume considerable computational power, and a doctor still needs to review the results.

Artificial intelligence algorithms for cancer prediction and treatment are often a black box. Pathologists and even researchers who developed and trained the model can’t explain how it delivers its outcome. For instance, when AI-powered software identifies the optimal cancer treatment for a particular patient, it doesn’t explain how it inferred this information.

Hospitals can use explainable AI for cancer detection and treatment, where algorithms reveal the reasoning behind their decision making. This will make it easier for doctors to act upon the recommendations and explain them to patients. However, turning AI into a white box will take away some of its predictive power. So, it’s a tradeoff that healthcare organizations will have to consider making.

There are also concerns about medical data ownership and obtaining patient consent when using their information. For example, the University of Chicago shared medical records with Google to help them develop an AI-powered predictive EHR environment. As a result, both parties were slammed with a lawsuit for patient data misuse.

Healthcare organizations need to be cautious when using AI to detect and prevent cancer. Artificial intelligence is a powerful tool that can save patients’ lives and physicians’ time. But using AI can also have devastating consequences if it’s not trained and deployed correctly. Contract reliable AI consultants with healthcare experience to help you build and deploy the technology.

Whether working alone or with a team of external AI experts, your company could significantly increase your chances of success by following these steps:

Here at ITRex, we offer an AI proof of concept development service that allows healthcare organizations to experiment with AI before committing to a full-fledged project. You can learn more about this process on our blog. Also, check out our extensive guide on how to implement AI and how much this initiative will cost.