AI & Authorship Policy.
Susruta Publishing House establishes explicit standards regarding the responsible use of artificial intelligence, machine learning tools, and large language models (LLMs) in scholarly manuscript preparation, maintaining uncompromised human accountability.
"Authorship carries moral, intellectual, and legal accountability that cannot be transferred to non-human algorithmic tools."
The rapid evolution of artificial intelligence, generative language models, and text synthesis systems presents new opportunities as well as critical ethical challenges for scholarly publishing. Susruta Publishing House adheres strictly to the consensus established by the Committee on Publication Ethics (COPE), the International Committee of Medical Journal Editors (ICMJE), and the World Association of Medical Editors (WAME).
Artificial intelligence tools (such as ChatGPT, Claude, Gemini, or specialized research synthesizers) do not meet the criteria for authorship. An author must be able to approve the final manuscript, vouch for the integrity and accuracy of the underlying work, identify potential conflicts of interest, and assume legal accountability for plagiarism, libel, and copyright compliance.
Core Ethical Tenets
- ✦ No AI as Co-Author: AI and LLMs cannot be listed as an author, co-author, or contributor on any monograph or book chapter.
- ✦ Full Human Accountability: Human authors are entirely responsible for the factual accuracy, citations, and authenticity of all submitted text.
- ✦ Mandatory Disclosure: Any utilization of AI for drafting, synthesis, data parsing, or visualization must be explicitly documented.
- ✦ Preservation of Provenance: Algorithmic generation must not obfuscate classical Sanskrit interpretations or original empirical observations.
Acceptable vs. Unacceptable Use
To provide transparent boundaries for authors, editors, and reviewers, SAPH categorizes AI utility into permissible assistance and prohibited practices.
Permissible Technical Assistance
Using algorithmic software for grammatical refinement, syntax checking, linguistic clarity, reference formatting, or spelling correction is permitted without mandatory formal methodology disclosure.
Substantive Text & Research Drafting
When generative AI is used to assist in literature scoping, draft structuring, or synthesizing large data arrays, authors must disclose the model name, prompt parameters, and exact sections affected in the manuscript.
Fabricated Citations & Hallucinations
Submitting AI-hallucinated citations, non-existent classical Samhita verses, or synthetic datasets without verification constitutes academic misconduct and will lead to immediate rejection or retraction.
Image & Botanical Artwork Ethics
AI-generated anatomical plates, botanical line diagrams, or surgical charts are prohibited from replacing primary empirical evidence, authentic specimen photography, or verified historical manuscripts.
Plagiarism & Copyright Infringement
Because LLMs are trained on existing copyrighted databases, uncited text synthesized by AI may violate third-party intellectual property. Authors bear sole legal liability for any resulting infringement.
Classical Sanskrit & Translation Scrutiny
Generative AI tools frequently misinterpret nuanced Vedic or classical Sanskrit terms. Authors must personally audit all Devanagari transliterations, Sandhi breakdowns, and contextual commentaries.
Declaration of Generative AI Usage
If generative AI or AI-assisted technologies were used in the development of a manuscript, authors must include a formal declaration in the preliminary apparatus or acknowledgments prior to peer review.
Standard Disclosure Template
Authors should adapt the following language and position it directly within the "Author Declarations" section of their submitted monograph or chapter:
Peer reviewers and editorial board members are strictly forbidden from uploading unpublished manuscripts, proposals, or peer assessment drafts into public generative AI software (e.g., ChatGPT, Copilot). Doing so breaches author confidentiality, violates data protection principles, and risks exposing proprietary intellectual property into external training sets.