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Updated: Jan 8, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Integrity meets innovation: A first principles approach to the ethics of AI utilization in medical research writing
Joseph Zwillenberg1, Eric M Teichner, Shiv Patil
1Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, PA. abass.alavi@uphs.upenn.edu.
Abstract:
The integration of artificial intelligence (AI), particularly large language models (LLM), into medical research writing is reshaping the landscape of academic authorship, productivity, and scholarly merit. It has been demonstrated that LLM are capable of greatly expediting the process of researching, drafting, and publishing manuscripts, despite current limitations currently necessitating intensive human oversight to ensure veracity and mitigate the phenomenon of "hallucination". With these limitations being addressed by AI developers and perhaps on their way to irrelevance, a different question emerges as the most, and perhaps only, important one. This paper adopts a first-principles ethical approach to examine the core moral question: independent of technological feasibility, to what extent is it ethically permissible to use AI in the drafting of medical research? We argue that the ethical imperative to accelerate scientific discovery, especially in Medicine, outweighs traditional concerns about the mechanics of authorship and merit attribution. Drawing on Aristotelian teleological reasoning, we contend that the primary value of research lies not in the process of its composition but in its capacity to alleviate suffering and advance human knowledge. Further, we understand authorship as inherently human, as only humans possess the moral agency required to accept responsibility for their work, which is something AI, by its nature, lacks. The paper concludes with a set of normative recommendations to guide the responsible and transparent integration of LLM in research.
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