How Artificial Intelligence is Improving Cancer Treatment

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Cancer is a multifaceted disease that impacts numerous lives globally. Conventional methods of treating cancer, such as surgical procedures, chemotherapy and radiation therapy, can be somewhat efficacious but also invasive, costly and induce various side effects. Artificial intelligence has many solutions to mankind including cancer treatment.

Is AI really improving cancer treatment?

Artificial intelligence (AI), as an evolving domain, demonstrates promising potential to transform oncologic care. AI involves the application of complex algorithmic systems to assimilate and deduce cancerous/carcinogenic patterns from immense datasets. Researchers have long leveraged such computational abilities of AI to develop novel, personalized and optimized cancer interventions.

AI shows capabilities in multiple areas that may augment standard cancer management. For example, machine learning algorithms can identify biomarkers and molecular signatures from vast biomedical literature and patient records. Such insights help understand cancer on a deeper molecular level to develop targeted therapies.

AI likewise aids in early detection by analyzing medical images for subtle abnormalities. Additionally, AI-powered systems personalize treatment plans by integrating a patient's clinical, genomic and lifestyle factors.

If validated through rigorous clinical testing, AI-based applications offer a supplemental avenue to refine cancer screening, prognostication and therapy. Some studies indicate AI achieves accuracy on par or exceeding human experts for certain cancer types.

Also Read: Michael Spencer’s talk about AI supremacy in cancer therapy

Case studies: Use of artificial intelligence for cancer therapy

Institute of Cancer Research (ICR) researchers used machine learning to identify five new subtypes of breast cancer based on gene sequences. This allows for more precise diagnoses and potentially better treatment decisions.

The organization is almost on the verge of launching an AI application called Phenmap. PhenMap integrates biological data with patient outcomes to categorize patients into subgroups based on cancer stage and progression. This information can aid clinicians in treatment planning.

Dr. Stephen-John Sammut, a Clinician Scientist at ICR, focuses on using AI to predict a patient's response to treatment based on the tumor ecosystem, including both cancer cells and their surrounding environment. This could allow doctors to identify patients who may not benefit from specific treatments. He is also leveraging AI to monitor cancer during treatment and detect emerging drug resistance early on.

Imagene AI, situated in Tel Aviv, Israel, focuses on AI-based biomarker profiling for various cancer types, providing real-time, reliable results. The company has already received an enormous amount of money from its venture capitalists to provide cutting-edge deep-learning algorithms that will dramatically shorten biomarker detection time from weeks to two minutes. This rapid examination of digitized biopsy images allows for faster, more tailored treatment decisions, perhaps improving the health of patients.

In October 2022, Dr. Dana Pe'er, Chair of the Computational and Systems Biology Program at Memorial Sloan Kettering Cancer Center (MSK), gave insights into the increasing usage of computational methods in oncology research.

Dr. Pe'er recognized that modern biology is transitioning into an information science due to the vast troves of genomic and clinical data now available. She highlighted that artificial intelligence (AI) and machine learning are playing a critical role in identifying meaningful patterns within these large and complex datasets.

However, Dr. Pe'er stressed the importance of collaboration between computational experts and biological researchers.

She cited examples of projects at MSK leveraging AI approaches. One involves using deep learning models to predict which lung cancer patients are most likely to benefit from immunotherapy based on analysis of their tumor gene expression profiles. In another study, AI is helping to determine personalized treatment strategies for breast cancer patients according to the activity levels of genes within their tumor samples.

The deep-learning researchers at AI Authority also make a significant contribution to disseminating the most recent findings regarding the application of AI in the healthcare industry, particularly concerning cancer.

5 ways artificial intelligence is improving cancer therapy

The following list highlights the five key domains where AI plays the most important role. There could be others, however, the following are the most often discussed.

  1. Early detection

AI can be used to diagnose cancer early on, when it is more likely to be cured. For example, AI algorithms can be used to identify malignant cells in tissue biopsies. AI can also help to create new screening tests that are more sensitive and specific than existing ones.

  1. Personalized treatment planning

AI can design personalized treatment strategies for each cancer patient. This is determined by the patient's specific genetic profile, tumor features and other variables. AI can assist in identifying the best therapy alternatives for each patient and predicting how they will react to treatment.

  1. Drug discovery

AI has the potential to discover new cancer medicines and improve the efficacy of existing ones. AI algorithms can be used to screen millions of chemicals for anticancer efficacy. AI can potentially be used to create new, more targeted medications with fewer negative effects.

  1. Treatment monitoring

Artificial intelligence can be used to monitor cancer patients' responses to treatment. This data can be utilized to modify treatment programs as necessary and to identify patients who are at risk of recurrence. Artificial intelligence can also be utilized to create new imaging techniques that provide more specific information about the tumor and its response to treatment.

  1. Palliative care

Artificial intelligence can help cancer patients enhance their quality of life. AI algorithms can help create individualized treatment plans that address the patient's physical, emotional and social requirements. AI can also be utilized to create new solutions to help patients manage cancer-related pain, nausea and other symptoms.

Take a look at critical advancements in AI in 2024 and beyond!

Here’s a sum-up!

There is no denying AI's promise to treat cancer, and as the technology develops and existing issues are resolved, even more progress can be made in this area. Health-tech startups working on cancer patients have enormous potential to scale things with intense AI and LLM engineering.

Robotics is another case where AI is helping surgeons perform more accurate and minimally invasive cancer procedures.

AI has the ability to monitor a patient's response to treatment and make real-time recommendations. It can also analyze large datasets to speed up the development of new and more effective cancer medicines.

Do you think AI will be able to significantly improve cancer treatment in the near future? If you work in the fields of AI and oncology, kindly let us know your thoughts on it.

Find more references below

Artificial Intelligence and Cancer Care: The Future of Oncology?

Transforming Early Breast Cancer Detection in India

AI tool to predict each cell of the tumor