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. 2025 Feb 19;8(2):e70200.
doi: 10.1002/hsr2.70200. eCollection 2025 Feb.

Advancing Medical Research Through Artificial Intelligence: Progressive and Transformative Strategies: A Literature Review

Ahmad R Al-Qudimat  1   2 , Zainab E Fares  1 , Mai Elaarag  1 , Maha Osman  2 , Raed M Al-Zoubi  1   3   4 , Omar M Aboumarzouk  1   5
Affiliations

Advancing Medical Research Through Artificial Intelligence: Progressive and Transformative Strategies: A Literature Review

Ahmad R Al-Qudimat et al. Health Sci Rep. .

Abstract

Background and aims: Artificial intelligence (AI) has become integral to medical research, impacting various aspects such as data analysis, writing assistance, and publishing. This paper explores the multifaceted influence of AI on the process of writing medical research papers, encompassing data analysis, ethical considerations, writing assistance, and publishing efficiency.

Methods: The review was conducted following the PRISMA guidelines; a comprehensive search was performed in Scopus, PubMed, EMBASE, and MEDLINE databases for research publications on artificial intelligence in medical research published up to October 2023.

Results: AI facilitates the writing process by generating drafts, offering grammar and style suggestions, and enhancing manuscript quality through advanced models like ChatGPT. Ethical concerns regarding content ownership and potential biases in AI-generated content underscore the need for collaborative efforts among researchers, publishers, and AI creators to establish ethical standards. Moreover, AI significantly influences data analysis in healthcare, optimizing outcomes and patient care, particularly in fields such as obstetrics and gynecology and pharmaceutical research. The application of AI in publishing, ranging from peer review to manuscript quality control and journal matching, underscores its potential to streamline and enhance the entire research and publication process. Overall, while AI presents substantial benefits, ongoing research, and ethical guidelines are essential for its responsible integration into the evolving landscape of medical research and publishing.

Conclusion: The integration of AI in medical research has revolutionized efficiency and innovation, impacting data analysis, writing assistance, publishing, and others. While AI tools offer significant benefits, ethical considerations such as biases and content ownership must be addressed. Ongoing research and collaborative efforts are crucial to ensure responsible and transparent AI implementation in the dynamic landscape of medical research and publishing.

Keywords: AI technology; artificial intelligence; deep learning; healthcare workers; machine learning; medical research.

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Conflict of interest statement

All authors declare there is no conflict of interest.

Figures

Figure 1
Figure 1
Selection studies flow‐chart.
See this image and copyright information in PMC

References

    1. Tagliaferri S. D., Angelova M., Zhao X., et al., “Artificial Intelligence to Improve Back Pain Outcomes and Lessons Learnt From Clinical Classification Approaches: Three Systematic Reviews,” NPJ Digital Medicine 3 (2020): 93. - PMC - PubMed
    1. Xu Y., Liu X., Cao X., et al., “Artificial Intelligence: A Powerful Paradigm for Scientific Research,” The Innovation 2, no. 4 (2021): 100179. - PMC - PubMed
    1. Kufel J., Bargieł‐Łączek K., Kocot S., et al., “What Is Machine Learning, Artificial Neural Networks and Deep Learning?‐Examples of Practical Applications in Medicine,” Diagnostics 13, no. 15 (2023): 2582. - PMC - PubMed
    1. Halevy A., Norvig P., and Pereira F., “The Unreasonable Effectiveness of Data,” IEEE Intelligent Systems 24, no. 2 (2009): 8–12.
    1. Rajkomar A., Dean J., and Kohane I., “Machine Learning in Medicine,” New England Journal of Medicine 380, no. 14 (2019): 1347–1358. - PubMed

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