Role of AI in empowering and redefining the oncology care landscape: perspective from a developing nation
Open accessAbstract
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.
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AI technologies provide timely, accurate, and accessible information that can empower patients and caregivers, supporting engagement, comprehension, and autonomy throughout the cancer care continuum.
“These technologies provide timely, accurate, and accessible information that can empower patients and caregivers, supporting engagement, comprehension, and autonomy throughout the cancer care continuum [6-8].”
From Empathic AI for Patient-Centered Cancer Care: A Scoping Review of Patient Navigation, Support, and Clinical Practice (2026) · cites this paper as [8]
The ‘AI in telemedicine/remote monitoring’ section (page unavailable) describes apps that give cancer patients individualized treatment information, advice, reminders, and access to support; the oncology-care table also describes improved access to services. Our copy does not establish that this information is consistently accurate or that these tools improve caregivers’ comprehension, engagement, or autonomy throughout the cancer care continuum. (LLM, conf=0.84)
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Improvement in prediction of prostate cancer prognosis with somatic mutational signatures
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Profiling Lung Cancer Patients Using Electronic Health Records
Menasalvas, E., Tuñas, J. M., Bermejo, G., Gonzalo‐Martín, C., Rodríguez‐González, A., Zanin, M., de Pedro, C. G., Méndez, M., Zaretskaia, O., Jimenez, J. R., et al. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-0975-9 · Cited by 25 · see citing works
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Applications of digital Medicine in oncology: Prospects and challenges
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Exploring the role of ChatGPT in oncology: providing information and support for cancer patients
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A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer
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Mkrtchyan, G. V., Veviorskiy, A., Izumchenko, E., Shneyderman, A., Pun, F. W., Ozerov, I. V., Aliper, A., Zhavoronkov, A. A., & Scheibye‐Knudsen, M. (2022) · Cell Death and Disease · doi:10.1038/s41419-022-05437-w · Cited by 21 · see citing works
Cascini, F., Gentili, A., Causio, F. A., Altamura, G., Melnyk, A., Beccia, F., Pappalardo, C., Lontano, A., & Ricciardi, W. (2023) · Frontiers in Public Health · doi:10.3389/fpubh.2023.1147210 · Cited by 21 · see citing works
A Novel Pipeline for Drug Repurposing for Bladder Cancer Based on Patients’ Omics Signatures
Mokou, M., Lygirou, V., Angelioudaki, I., Paschalidis, N., Stroggilos, R., Frantzi, M., Latosińska, A., Bamias, A., Hoffmann, M. J., Mischak, H., et al. (2020) · Cancers · doi:10.3390/cancers12123519 · Cited by 20 · see citing works
Adequacy and Effectiveness of Watson For Oncology in the Treatment of Thyroid Carcinoma
Yun, H. J., Kim, H. J., Kim, S. Y., Lee, Y. S., Lim, C. Y., Chang, H., & Park, C. S. (2021) · Frontiers in Endocrinology · doi:10.3389/fendo.2021.585364 · Cited by 19 · see citing works
Florensa, D., Mateo, J., Solsona, F., Aige, T. P., Mesas, M., Piñol, R., & Godoy, P. (2022) · Journal of Medical Internet Research · doi:10.2196/29056 · Cited by 17 · see citing works
Noncoding RNAs and Deep Learning Neural Network Discriminate Multi-Cancer Types
Wang, A., Hai, R., Rider, P. J. F., & He, Q. (2022) · Cancers · doi:10.3390/cancers14020352 · Cited by 16 · see citing works
Breast Cancer Risk Prediction Using Electronic Health Records
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Cancer prediction with gene expression profiling and differential evolution
Lakshmi, T. R. V., & Reddy, C. V. K. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02396-9 · Cited by 15 · see citing works
Sahoo, B., Pinnix, Z., Sims, S., & Zelikovsky, A. (2023) · Journal of Computational Biology · doi:10.1089/cmb.2022.0422 · Cited by 14 · see citing works
Han, Y., Zhu, X., Hu, Y., Yu, C., Guo, Y., Hang, D., Pang, Y., Pei, P., Ma, H., Sun, D., et al. (2023) · JMIR Public Health and Surveillance · doi:10.2196/43725 · Cited by 13 · see citing works
Flagged first, then by citations
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- Lee, RLAIF vs RLHF: scaling reinforcement learning from Human feedback with AI feedback. arXiv.org (2023)
- 2021, Global Strategy on Digital Health 2020-2025
Cited by
Sorted by citations
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Self-citationBridging technology and medicine: artificial intelligence in targeted anticancer drug delivery
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Overcoming the Black Box Challenge: Building Trust in Artificial Intelligence Algorithms in Oncology
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Kesiapan Sumber Daya Manusia Rumah Sakit dalam Menghadapi Transformasi Digital Layanan Kesehatan
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Ajibade, V., & Madu, C. S. (2025) · Cureus · doi:10.7759/cureus.85024 · Cited by 3 · see citing works
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“These technologies provide timely, accurate, and accessible information that can empower patients and caregivers, supporting engagement, comprehension, and autonomy throughout the cancer care continuum [6-8].”
Correia, G. S. D. C., Hazim, A. Z., Zheng-Lin, B., Gannon, N., Boyle, A. W. R., Crowe, S., Jones, J. C., & Manochakian, R. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00296 · Cited by 2 · see citing works
Bridging diagnosis and therapeutics: The role of AI in cancer detection and drug development
Barua, S., Balaji, B., & Balaji, S. (2026) · Health Sciences Review · doi:10.1016/j.hsr.2026.100270 · Cited by 2 · see citing works
Navigating AI and machine learning in cancer research: an end-to-end translational framework
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A Precision-driven Artificial Intelligence Approach for Early Detection of Oral Malignancies
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Strengthening healthcare systems through exposomics
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AI-Enhanced Precision Medicine for Leukemia
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Transforming cancer care in emerging economies: India’s AI innovation
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- Authors
- Goel, I., Bhaskar, Y., Kumar, N., Singh, S., Amanullah, M., Dhar, R., & Karmakar, S.
- Affiliations
- All India Institute of Medical Sciences (IN)
- Indian Council of Medical Research (IN)
- King Khalid University (SA)
- Corresponding author
- Dhar, Ruby
- Karmakar, Subhradip
- Venue
- Frontiers in Digital Health
- Publisher
- Frontiers Media SA
- Published
- 2025-03-04
- Volume
- 7
- Pages
- 1550407-1550407
- ISSN
- 2673-253X (ISSN-L)
- PMC
- PMC11913822
- OpenAlex
- W4408135106
- Semantic Scholar
- c8bd86e850416feebe18db1cf47e0d8cc53f68f2
- Cited by
- 31
- References
- 212
- Citations by year
- 2026: 20 · 2025: 11
- FWCI
- 1.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Digital Health (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Keywords
- artificial intelligence in oncology, cancer diagnosis, cancer prognosis, precision oncology, anticancer drug discovery, digital health, healthcare resource shortages, cancer screening, personalized treatment planning, AI ethics and privacy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
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