Medical Academic Writing Humanizer

Removes identifiable markers of AI-generated prose from medical academic papers and makes them read naturally, as if written by an experienced human researcher. Based on Wikipedia's AI writing detection patterns and adapted specifically for medical and biomedical literature conventions, the skill rewrites text to eliminate formulaic phrasing, mechanical transitions, and stylistic uniformity that characterize machine-generated content while preserving scientific accuracy and appropriate academic register.

Quick Install

npx skills add Open-Medica/open-medical-skills --skill medical-paper-humanizer

What It Does

• AI writing pattern detection: Identifies over 40 common markers of AI-generated text including hedging overuse ("it is important to note that"), filler transitions ("furthermore," "moreover," "additionally" in sequence), sycophantic framing ("this groundbreaking study"), uniform sentence length, and formulaic paragraph structure
• Medical register preservation: Maintains the precision required in scientific writing — exact statistical language, proper use of clinical terminology, accurate citation integration, and appropriate hedging where scientific uncertainty genuinely exists — while eliminating artificial hedging that adds no meaning
• Section-specific adaptation: Applies different humanization strategies to each manuscript section: more varied voice in the Introduction and Discussion, strict precision in Methods and Results, measured interpretation in the Abstract and Conclusions
• Voice and style variation: Introduces natural sentence length variation, occasional passive-to-active voice shifts where appropriate, discipline-specific idioms, and the kind of concise directness that characterizes experienced medical writers
• Before/after comparison: Outputs the revised text alongside a marked-up diff showing every change made, with brief rationale notes explaining why each modification was applied, enabling the author to accept, reject, or further modify individual changes

Clinical Use Cases