management. This article also discusses the
challenges and opportunities, such as data quality,
interpretability, privacy issues, collaboration across
disciplines, and recent advances in LLMs. At present,
AI cannot replace professional medical staff, but the
assistance of AI can bring great convenience to
medical staff and users. AI can provide significant
benefits for personalized healthcare management, but
it also requires careful evaluation and
implementation. In the era of rapid technological
development, it is also time to think about the safety
and ethics problems of artificial intelligence
technology. In any case, there is still much potential
for the further development and application of
artificial intelligence in personalized healthcare
management, and future results are expected to better
improve medical outcomes and patient experience.
REFERENCES
Abbasian, M., Azimi, I., Rahmani, A. M., & Jain, R. 2023.
Conversational health agents: A personalized llm-
powered agent framework. arXiv preprint
arXiv:2310.02374.
Abdel-Fattah, M. A., Othman, N. A., & Goher, N. 2022.
Predicting Chronic Kidney Disease Using Hybrid
Machine Learning Based on Apache Spark.
Computational Intelligence and Neuroscience, 2022,
1–12.
Aguilera, A., Figueroa, C. A., Hernandez-Ramos, R.,
Sarkar, U., Cemballi, A., Gomez-Pathak, L.,
Miramontes, J., Yom-Tov, E., Chakraborty, B., Yan,
X., Xu, J., Modiri, A., Aggarwal, J., Jay Williams, J., &
Lyles, C. R. 2020. mHealth app using machine learning
to increase physical activity in diabetes and depression:
clinical trial protocol for the DIAMANTE Study. BMJ
Open, 10(8), e034723.
Ahmed, H., Younis, E. M. G., Hendawi, A., & Ali, A. A.
2019. Heart disease identification from patients ’
social posts, machine learning solution on Spark.
Future Generation Computer Systems, 111.
Alotaibi, S., Mehmood, R., Katib, I., Rana, O., & Albeshri,
A. 2020. Sehaa: A Big Data Analytics Tool for
Healthcare Symptoms and Diseases Detection Using
Twitter, Apache Spark, and Machine Learning. Applied
Sciences, 10(4), 1398.
An, Y., Huang, N., Chen, X., Wu, F., & Wang, J. 2019.
High-risk prediction of cardiovascular diseases via
attention-based deep neural networks. IEEE/ACM
transactions on computational biology and
bioinformatics, 18(3), 1093-1105.
Benary, M., Wang, X. D., Schmidt, M., Soll, D.,
Hilfenhaus, G., Nassir, M., ... & Rieke, D. T. 2023.
Leveraging large language models for decision support
in personalized oncology. JAMA Network Open, 6(11),
e2343689-e2343689.
Cheerla, N., & Gevaert, O. 2017. MicroRNA based Pan-
Cancer Diagnosis and Treatment Recommendation.
BMC Bioinformatics, 18(1).
Chiang, P.-H., & Dey, S. 2018. Personalized Effect of
Health Behavior on Blood Pressure: Machine Learning
Based Prediction and Recommendation. 2018 IEEE
20th International Conference on e-Health Networking,
Applications and Services (Healthcom) (pp. 1-6). IEEE.
Chiang, P.-H., Wong, M., & Dey, S. 2021. Using
Wearables and Machine Learning to Enable
Personalized Lifestyle Recommendations to Improve
Blood Pressure. IEEE Journal of Translational
Engineering in Health and Medicine, 9, 1–13.
Dai, D., Chen, H., Dong, X., Chen, J., Mei, M., Lu, Y., ...
& Zhou, W. 2021. Bronchopulmonary dysplasia
predicted by developing a machine learning model of
genetic and clinical information. Frontiers in
Genetics, 12, 689071.
Goetz, L. H., & Schork, N. J. 2018. Personalized medicine:
motivation, challenges, and progress. Fertility and
Sterility, 109(6), 952–963.
Hassan, F., E., M., & Sahal, R. 2020. Real-Time Healthcare
Monitoring System using Online Machine Learning
and Spark Streaming. International Journal of
Advanced Computer Science and Applications, 11(9).
Kim, S.-H., & Chung, K. 2015. Emergency situation
monitoring service using context motion tracking of
chronic disease patients. Cluster Computing, 18(2),
747–759.
Krishnamoorthi, R., Joshi, S., Almarzouki, H. Z., Shukla,
P. K., Rizwan, A., Kalpana, C., & Tiwari, B. 2022. A
Novel Diabetes Healthcare Disease Prediction
Framework Using Machine Learning Techniques.
Journal of Healthcare Engineering, 2022, 1–10.
Pinto, S., Cabral, J., & Gomes, T. 2017. We-care: An IoT-
based health care system for elderly people. 2017 IEEE
International Conference on Industrial Technology
(ICIT).
López Martínez, A., Gil Pérez, M., & Ruiz-Martínez, A.
2023. A comprehensive review of the state-of-the-art
on security and privacy issues in healthcare. ACM
Computing Surveys, 55(12), 1-38.
Lu, H., Uddin, S., Hajati, F., Moni, M. A., & Khushi, M.
2021. A patient network-based machine learning model
for disease prediction: The case of type 2 diabetes
mellitus. Applied Intelligence, 52(3), 2411-2422.
Nguyen, T., Nguyen, G. T., Nguyen, T., & Le, D. H. 2021.
Graph convolutional networks for drug response
prediction. IEEE/ACM transactions on computational
biology and bioinformatics, 19(1), 146-154.
Qiu, Y., Wang, J., Jin, Z., Chen, H., Zhang, M., & Guo, L.
2022. Pose-guided matching based on deep learning for
assessing quality of action on rehabilitation
training. Biomedical Signal Processing and
Control, 72, 103323.
Sekulić, I., & Strube, M. 2019. Adapting Deep Learning
Methods for Mental Health Prediction on Social Media.
Proceedings of the 5th Workshop on Noisy User-
Generated Text (W-NUT 2019), 322–327.