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Related Experiment Video

Updated: Mar 10, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Ethics in publishing: From plagiarism to artificial intelligence.

Fatima Alnaimat1, Abdel Rahman Feras AlSamhori2, Birzhan Seiil3

  • 1Department of Internal Medicine, Division of Rheumatology, School of Medicine, University of Jordan, Amman 11942, Jordan.

Autoimmunity Reviews
|March 9, 2026
PubMed
Summary

Digitization in medical writing presents plagiarism and artificial intelligence (AI) ethics challenges. Safeguarding scholarly integrity requires plagiarism detection, AI disclosure, training, and oversight.

Keywords:
Academic publishingArtificial intelligenceEthicsPlagiarism

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Area of Science:

  • Medical writing and publishing ethics
  • Digital transformation in research

Background:

  • Research digitization introduces ethical concerns in medical writing, including plagiarism, authorship, and artificial intelligence (AI) use.
  • Contemporary medical publishing faces challenges in maintaining scholarly integrity due to evolving technologies.

Purpose of the Study:

  • To examine current strategies for preventing plagiarism in medical writing.
  • To assess methods for ensuring the ethical application of artificial intelligence (AI) in medical publishing.

Main Methods:

  • Structured narrative review methodology.
  • Comprehensive literature search across PubMed/MEDLINE, EMBASE, Scopus, and Web of Science databases (January 2005-March 2026).
  • Focus on conceptual synthesis over quantitative meta-analysis.

Main Results:

  • Artificial intelligence (AI) offers potential benefits for efficiency and quality in medical writing.
  • Misuse of AI, including unacknowledged use, over-reliance, or biased outputs, threatens scholarly integrity.
  • Existing plagiarism detection tools are crucial but insufficient alone.

Conclusions:

  • A proactive framework is essential to maintain trust in medical literature.
  • This framework should integrate plagiarism detection, mandatory AI disclosure, and ethical training.
  • Human accountability and ethical responsibility remain paramount, with technology serving as a supportive tool.