Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation

Abstract

Early diagnosis and accurate prognosis play a pivotal role in the clinical management of cancer and in preventing cancer-related mortalities. The burgeoning population of Asia in general and South Asian countries like India in particular pose significant challenges to the healthcare system. Regrettably, the demand for healthcare services in India far exceeds the available resources, resulting in overcrowded hospitals, prolonged wait times, and inadequate facilities. The scarcity of trained manpower in rural settings, lack of awareness and low penetrance of screening programs further compounded the problem. Artificial Intelligence (AI), driven by advancements in machine learning, deep learning, and natural language processing, can profoundly transform the underlying shortcomings in the healthcare industry, more for populous nations like India. With about 1.4 million cancer cases reported annually and 0.9 million deaths, India has a significant cancer burden that surpassed several nations. Further, India's diverse and large ethnic population is a data goldmine for healthcare research. Under these circumstances, AI-assisted technology, coupled with digital health solutions, could support effective oncology care and reduce the economic burden of GDP loss in terms of years of potential productive life lost (YPPLL) due to India's stupendous cancer burden. This review explores different aspects of cancer management, such as prevention, diagnosis, precision treatment, prognosis, and drug discovery, where AI has demonstrated promising clinical results. By harnessing the capabilities of AI in oncology research, healthcare professionals can enhance their ability to diagnose cancers at earlier stages, leading to more effective treatments and improved patient outcomes. With continued research and development, AI and digital health can play a transformative role in mitigating the challenges posed by the growing population and advancing the fight against cancer in India. Moreover, AI-driven technologies can assist in tailoring personalized treatment plans, optimizing therapeutic strategies, and supporting oncologists in making well-informed decisions. However, it is essential to ensure responsible implementation and address potential ethical and privacy concerns associated with using AI in healthcare.

Keywords: artificial intelligence; cancer management; developing nation; digital health; personalized medicine.

Figure 1

Charts showing the estimates of new cases and deaths due to different types of cancer during the year 2022 in (a) Worldwide (Top 15 cancer sites); (b) India (both sexes) (Top 10 cancer sites); (c) deaths in India males, females (Top 10 cancer sites) (Data Source: GLOBOCON 2022; International Agency for Research on Cancer).

Figure 3

Workflow methodology for article collection. This workflow provides a detailed overview of the process used for the article collection in our review on the role and impact of AI in oncology. The workflow highlights the key stages, from identification and screening to eligibility assessment and final inclusion of relevant studies.

Figure 2

Artificial intelligence and its sub-disciplines. Although weak AI (ANI), and strong AI (AGI) are broadly defined based on their rigorous nature, there are other subfields of AI based on their applications. This includes- machine learning, which can be used to make predictions, classify data, and make decisions; deep learning, capable of learning complex patterns in data; Natural Language Processing for addressing the meaningful content of text-by-text miming and for translating languages) and computer vision (for images and video information) and Robotics for performing complex tasks.

Figure 5

Key domains within oncology and research where artificial intelligence (AI) can significantly impact patient care and scientific advancement. While each of these domains may appear disjoint, they are in fact seamlessly integrated with AI playing a significant role in this process. AI-based tools can screen data from an enormous volume of subjects to find patterns and connections between them. This information can not only assist in personalized cancer care but can also recommend the most appropriate therapy as well as digital communications for networking a large number of centres.

Figure 4

AI workflow for image-based classification of breast mammograms (240). This schematic illustrates the step-by-step workflow of an AI system using a Convolutional Neural Network (CNN) algorithm. Key stages include data collection, preprocessing, training/testing split, model selection, feature extraction, classification, evaluation, hyperparameter optimization, clinical validation, and continuous monitoring for improved accuracy.

Figure 6

Figure showing broad applications of AI in the areas of novel drug discovery from target identification, drug designing, pharmacokinetics to clinical trials.

Figure 7

This figure illustrates how technology and training can enable collaboration between urban super speciality centres and rural healthcare professionals. Patient data (clinical reports, imaging, vitals signatures) collected by wearable and other point-of-care (POC) devices can be securely transmitted through cloud-based digital health platforms (mHealth Apps) for AI-driven analysis by urban specialists. A specialized committee can ensure continuous monitoring and timely diagnosis, empowering rural healthcare practitioners with actionable insights for prompt interventions.

Figure 8

Pictorial representation of challenges encountered by populous countries and how advancements driven by artificial intelligence and digital health can enhance the management of cancer care.