Advancing oncology care with AI-powered virtual assistants and chatbots: A qualitative exploration of future potential and challenges

Abstract

The integration of artificial intelligence (AI) through virtual assistants and chatbots is transforming oncology care by providing continuous, personalized, and accessible support. This study aims to evaluate the role of AI-powered tools in improving patient engagement, symptom management, emotional support, and treatment adherence in oncology. A qualitative methodology was employed, which included an extensive review of peer-reviewed literature from 2015 to 2023 and the development of a conceptual framework for oncology-specific chatbot systems. This framework incorporates natural language processing, machine learning, and personalized response algorithms. Key findings from this study indicate that these AI tools enhance access to healthcare information, empower patients, and reduce the burden on healthcare systems, particularly in remote or underserved regions. However, challenges remain concerning data privacy, accuracy, and the need for human intervention in complex cases. The study underscores the importance of maintaining a balance between innovative AI applications and human-centered care, advocating the integration of AI-based technologies as complementary tools in oncology.

Keywords

Artificial intelligence / Virtual assistants / Chatbots / Oncology care / Patient support / Personalized healthcare / AI in cancer care

Funding

None.

Conflict of interest

All the authors declare they have no competing interests.

References

[1] Kang J, Lafata K, Kim E, et al. Artificial intelligence across oncology specialties: Current applications and emerging tools. BMJ Oncol. 2023; 3:e000134. doi: 10.1136/bmjonc-2023-000134

[2] National Cancer Institute. The Tech Revolutionizing Cancer Research and Care; 2022. Available from: https://www. cancer.gov [Last accessed on 2025 Jan 11].

[3] National Cancer Institute. How CRISPR is Changing Cancer Research and Treatment; 2022. Available from: https://www.cancer.gov [Last accessed on 2025 Jan 11].

[4] World Economic Forum. Technology Can Revolutionize Cancer Care. 6 Experts Explain How; 2023. Available from: https://www.weforum.org [Last accessed on 2025 Jan 11].

[5] Velindre University NHS Trust. Velindre Cancer Centre Launches Artificial Intelligence Chatbots RITA; 2023. Available from: https://velindre.nhs.wales [Last accessed on 2025 Jan 11].

[6] Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017; 542(7639):115-118. doi: 10.1038/nature21056

[7] Gulshan V, Peng L, Coram M, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016; 316(22):2402-2410. doi: 10.1001/jama.2016.17216

[8] Liao CH, Li CH, Lee CC. Development of an automated breast cancer detection system based on a deep learning approach. Int J Med Sci. 2019; 16(5):746-755. doi: 10.7150/ijms.31999

[9] Chen W, Li X, Liu J, Xie X. Machine learning models for predicting radiotherapy outcomes in cancer patients. Cancer Commun (Lond). 2020; 40(3):131-142. doi: 10.1002/cac2.12092

[10] Wang S, Li X. Lung cancer diagnosis using deep learning. J Med Imaging. 2018; 5(3):033003. doi: 10.1117/1.JMI.5.3.033003

[11] Liu Y, Chen PC, Krause J. Deep learning for colorectal cancer screening: A review of recent applications. IEEE Access. 2020; 8:205106-205119. doi: 10.1109/ACCESS.2020.3037058

[12] Rajpurkar P, Hannun AY, Haghpanahi M, et al. CheXNet: Radiologist-Level Pneumonia Detection on Chest X-rays with Deep Learning. [arXiv Preprint]; 2017.

[13] Bejnordi BE, Veta M, Van Diest PJ, et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA. 2017; 318(22):2199-2210. doi: 10.1001/jama.2017.14585

[14] Tran TM, Nguyen HT, Nguyen HS. Prostate cancer detection using deep learning in magnetic resonance imaging. J Med Imaging. 2020; 7(2):024502. doi: 10.1117/1.JMI.7.2.024502

[15] Zhang Z, Zhang Z, Chen Y. Brain tumor segmentation using deep learning algorithms. IEEE Trans Med Imaging. 2019; 38(7):1694-1705. doi: 10.1109/TMI.2019.2902909

[16] Cirillo D, Pippa L. Tumor classification and grading with deep learning: A review. J Cancer Res Clin Oncol. 2020; 146(1):67-76. doi: 10.1007/s00432-019-03134-1

[17] Lee J, Kim Y, Park S. AI-based chemotherapy response prediction in cancer patients using machine learning algorithms. Comput Biol Med. 2019; 116:103545. doi: 10.1016/j.compbiomed.2019.103545

[18] Mazurowski MA, Buda M. Predicting breast cancer prognosis with machine learning. J Digit Imaging. 2017; 30(4):531-538. doi: 10.1007/s10278-017-9882-4

[19] Wang Z, Li H. AI-based ovarian cancer detection using convolutional neural networks. J Cancer Res Clin Oncol. 2020; 146(10):2459-2468. doi: 10.1007/s00432-020-03335-4

[20] Shen D, Wu G, Suk HI. Deep learning in medical image analysis. Annu Rev Biomed Eng. 2019; 21:77-108. doi: 10.1146/annurev-bioeng-060418-052858

[21] Hwang D, Park S, Kim J. Skin cancer detection with deep learning in histopathology slides. J Pathol Inform. 2019; 10:10. doi: 10.4103/jpi.jpi_65_19

[22] Liu X, Zhang J, Li J. AI-based brain tumor diagnosis using deep learning. Front Neurol. 2021; 12:672150. doi: 10.3389/fneur.2021.672150

[23] Choi Y, Lee J, Kwon T. Cancer genomics analysis with artificial intelligence for personalized treatment. J Biomed Inform. 2021; 116:103699. doi: 10.1016/j.jbi.2021.103699

[24] Tan J, Zhang C, Huang Z. Early detection of pancreatic cancer using deep learning models. Artif Intell Med. 2020; 103:101818. doi: 10.1016/j.artmed.2019.101818

[25] Zhang T, Zhang Y, Xu J. AI in cancer drug discovery: A review of machine learning applications. Comput Biol Med. 2021; 136:104704. doi: 10.1016/j.compbiomed.2021.104704