Technology and Its Effect on Cancer Treatment


For a patient diagnosed with cancer, technology can be an important tool in the fight against the disease. However, it can also cause some negative effects that affect people’s lives.

For example, a growing number of cancer patients are experiencing unpleasant side effects from their treatments. To help alleviate these issues, it’s essential to develop next-generation cancer therapies with fewer side effects.

Personalized Treatment

Personalized treatment is a type of cancer medicine that uses information about your tumor, including its genetic changes and protein expression, to help you choose the right treatment for you. This approach is also called precision medicine.

In a recent study, researchers found that using personalized treatment led to better responses and longer progression-free survival in patients with solid tumors and blood cancers than in people enrolled in non-personalized trials.

One example of personalized treatment is using a patient’s own immune cells to kill mutated cancer cells. These cells have receptors on their surface that help them recognize and kill cancer cells.

This is a new approach in cancer research. It aims to tailor treatments to groups of patients who share similar faults in their tumours, helping to match them with the best drugs.


Telehealth refers to the use of technology to provide health care to patients at a distance. It allows people to receive appointments online, through telephone calls or using video chat programs such as Apple FaceTime, Facebook Messenger, Google Hangouts, and Zoom.

Despite its promise of convenience, cost savings and access to specialists far away, telehealth cannot always replace in-person visits. Some appointments need physical examinations, lab tests and other complex procedures.

For these reasons, cancer patients sometimes prefer to see a doctor in person for the first time. Then they can build a relationship with their doctor.

In addition to providing remote medical assistance, telehealth can help address a patient’s behavioral risk behaviors — smoking, overeating and lack of physical activity. Those health risks contribute to cancer recurrence and can lead to heart disease and diabetes.

Predictive Models

Predictive models use statistics and data to determine the likelihood of a future event. They may be used by organizations to predict a consumer’s credit history, upcoming sales opportunities or account-related security alerts.

In addition, these models can also be used to help identify potential health risks and recommend a preventive strategy. For example, if a patient has a high risk of diabetes, a predictive model could suggest they take steps to control their sugar intake.

However, making predictions based on a dataset isn’t infallible. It’s important to ensure the data is complete and rich with all the appropriate variables on which to base a prediction.

Using machine learning (ML), a predictive model can be trained to predict the outcome of treatment. This is especially useful in radiation oncology, where outcomes like survival rate, tumor response and radiation toxicity are key factors to consider.

Artificial Intelligence

Artificial intelligence (AI) is a type of computer program that can use data to make decisions. Scientists train AI algorithms to learn how to analyze data and make predictions, then they test them on real-world situations.

AI can be used to improve cancer detection, according to a recent study from the National Cancer Institute (NCI). A team of researchers developed an AI model that could help radiologists read a new kind of prostate MRI scan called multiparametric MRI more accurately.

This type of MRI is not as common as regular MRIs, so some radiologists can’t use it as well as others, which leads to disagreements over what looks like prostate cancer on the scan. The NCI team’s AI model can help less-experienced radiologists quickly learn how to read multiparametric MRI, which could reduce the number of false positive results and prevent unnecessary stress, procedures, and follow-up tests for patients.

Another potential use for AI is to help oncologists decide which treatments are best for patients with different types of cancer. These types of algorithms are able to predict survival and recurrence after treatment, helping doctors choose the most effective strategy for each patient.

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