Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection - A Critical Approach

Sep 24, 2026

Uploaded by Jacob

Cancer remains a major cause of morbidity and mortality worldwide, and individualized, data-driven clinical decision-making is still challenging. This work reviews how artificial intelligence and machine learning are used in oncology to support risk assessment, early diagnosis, prognosis estimation, and treatment selection. It summarizes performance evidence suggesting improved cancer prediction compared with clinicians, outlines current applications, limitations, and future prospects, and discusses broader potential benefits beyond cancer.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Journal of Multidisciplinary Healthcare Dovepress open access to scientific and medical research Open Access Full Text Article REVIEW Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach Bo Zhang 1 , Huiping Shi 1 , Hongtao Wang2 1Jinling Institute of Science and Technology, Nanjing City, Jiangsu Province, People’s Republic of China; 2School of Life Science, Tonghua Normal University, Tonghua City, Jilin Province, People’s Republic of China Correspondence: Bo Zhang, Jinling Institute of Science and Technology, No. 99, Hongjing Avenue, Jiangning District, Nanjing City, Jiangsu Province, 211169, People’s Republic of China, Email [liuxing.2001@163.com](liuxing.2001@163.com) | Abstract: Cancer is a leading cause of morbidity and mortality worldwide. While progress has been made in the diagnosis, prognosis, and treatment of cancer patients, individualized and data-driven care remains a challenge. Artificial intelligence (AI), which is used to predict and automate many cancers, has emerged as a promising option for improving healthcare accuracy and patient outcomes. AI applications in oncology include risk assessment, early diagnosis, patient prognosis estimation, and treatment selection based on deep knowledge. Machine learning (ML), a subset of AI that enables computers to learn from training data, has been highly effective at predicting various types of cancer, including breast, brain, lung, liver, and prostate cancer. In fact, AI and ML have demonstrated greater accuracy in predicting cancer than clinicians. These technologies also have the potential to improve the diagnosis, prognosis, and quality of life of patients with various illnesses, not just cancer. Therefore, it is important to improve current AI and ML technologies and to develop new programs to benefit patients. This article examines the use of AI and ML algorithms in cancer prediction, including their current applications, limitations, and future prospects.
Keywords: machine learning, artificial intelligence, treatment selection, cancer diagnosis, cancer-related mortality | | --- | | Introduction
Cancer is a significant public health issue globally, marked by an elevated incidence and mortality rate.1 According to the GLOBOCAN 2020 database, approximately 19.3 million new cases and 10 million deaths have been reported annually.2 Lung cancer remains the most common cause of cancer-related mortality, with expected 1.8 million fatalities, followed by stomach, liver, colorectal, and breast cancer.2 The prevention and treatment of cancer remain difficult.3 After heart disease, cancer remains the second leading cause of death in the United States. In 2023, there are projected to be 1.9 million new cancer cases (equivalent to around 5370 cases per day) and 609,820 deaths from cancer (equivalent to around 1670 deaths per day) in the US. The International Agency for Research on Cancer (IARC) has released a poster on known causes and prevention by organ site of human cancer. A description of known causes and prevention by organ site is provided in Figure S1.
The “Global Cancer Observatory” reports indicate that on a global scale, 37 individuals are diagnosed with cancer and over 19 individuals succumb to the disease every minute. Figure 1A shows the number of new cases in 2020 and Figure 1B shows the number of deaths in 2020 due to all cancers.
However, the introduction of machine learning and artificial intelligence positively supports cancer prevention and management.5 Artificial intelligence is commonly defined as a set of computer-coded programs or algorithms that use data analysis and pre-programmed instructions to make predictions and decisions about various aspects of a disease. Machine learning is a specialized field within AI that refers to a group of algorithms designed to automatically learn and improve from experience. In other words, machine learning is an AI subset that focuses on devel

FAQs

How does the article describe the role of AI in cancer care?

It states that AI is used to make predictions and support decisions across cancer risk assessment, early diagnosis, prognosis estimation, and treatment selection.

What is the relationship between machine learning and artificial intelligence in the article?

The article defines machine learning as a specialized subset of AI that learns from training data and improves performance over time.

What scope does the article cover regarding machine learning and AI for cancer?

It examines current uses in cancer prediction, discusses limitations, and outlines future prospects for improving existing technologies and benefiting patients.

Sep 24, 2026

Uploaded by Jacob