Integrating Artificial Intelligence in Disease Modeling : Opportunities and Challenges
Keywords:
Artificial, Intelligence, Disease, Modeling, MachineAbstract
The integration of artificial intelligence (AI) in disease modeling presents significant opportunities and challenges in modern healthcare. This study aims to explore how AI can enhance the accuracy and efficiency of disease prediction, diagnosis, and treatment. Utilizing a systematic review approach, this research analyzes recent advancements in AI algorithms, including machine learning and deep learning, applied to epidemiological data. The findings reveal that AI significantly improves predictive accuracy and personalized medicine but also faces challenges related to data privacy, ethical concerns, and model interpretability. These challenges highlight the need for transparent algorithms and robust regulatory frameworks. This study contributes to the ongoing discourse on leveraging AI for public health, emphasizing the balance between technological innovation and ethical responsibility.
References
BMC Medical Education. (2023). Artificial Intelligence in medical diagnosis and decision-making: A systematic review. BioMed Central. https://bmcmededuc.biomedcentral.com/articles/10.1186/s12909-023-04698-z
BMC Medical Research Methodology. (2023). Systematic review methodologies in health research. BioMed Central. https://bmcmedresmethodol.biomedcentral.com/articles/10.1186/s12874-023-02047-8
British Medical Bulletin. (2022). Opportunities and challenges of AI in medical fields: Ethical considerations. Oxford Academic. https://academic.oup.com/bmb/article/139/1/4/6353269
Clinical and Translational Science. (2023). AI in neurological disease modeling: Precision and therapeutic development. Wiley Online Library. https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.3253
Cochrane Handbook. (2023). Data collection techniques for systematic reviews. Cochrane Training. https://training.cochrane.org/handbook/current/chapter-05
Health and Technology Journal. (2021). Standardized frameworks for AI implementation in healthcare. Springer. https://link.springer.com/article/10.1007/s12553-021-00555-5
Journal of the American Medical Informatics Association (JAMIA) Open. (2024). Standardized frameworks for AI implementation in healthcare: A systematic review. Oxford Academic. https://academic.oup.com/jamiaopen/article/8/1/ooae155/7942556
MDPI Tropical Medicine and Infectious Disease. (2023). Ethical considerations in AI-driven healthcare: Data privacy and algorithmic bias. MDPI. https://www.mdpi.com/2414-6366/9/10/228
National Center for Biotechnology Information (NCBI). (2021). Challenges in the interpretability of AI models in healthcare. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC8165857
National Center for Biotechnology Information (NCBI). (2022). The role of AI in healthcare information composition and analysis. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC8754556
National Center for Biotechnology Information (NCBI). (2023). Challenges in the interpretability of AI-generated models in healthcare. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC11196383
National Center for Biotechnology Information (NCBI). (2023). Ethical considerations in AI deployment in healthcare. PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC9557803
Nature Communications. (2024). Integrated AI models for infectious disease prediction and intervention assessment. Nature Communications. https://www.nature.com/articles/s41467-024-55461-x
Nature Digital Medicine. (2024). Diagnostic accuracy of AI in digital pathology: A systematic review and meta-analysis. Nature. https://www.nature.com/articles/s41746-024-01106-8
Nature Digital Medicine. (2024). Performance of AI algorithms for skin cancer classification: A systematic review and meta-analysis. Nature. https://www.nature.com/articles/s41746-024-01103-x
PubMed Central. (2022). AI applications in oncology: Neuroimaging and disease progression prediction. National Center for Biotechnology Information. https://pmc.ncbi.nlm.nih.gov/articles/PMC8754556
Tropical Medicine and Health. (2019). Guidelines for conducting systematic reviews in public health. BioMed Central. https://tropmedhealth.biomedcentral.com/articles/10.1186/s41182-019-0165-6
University of Hawaii - Health Sciences Library. (2023). Data analysis tools for systematic reviews and meta-analysis. https://hslib.jabsom.hawaii.edu/systematicreview/dataanalysis
