📅 Research Journal Entry – November 2025
Today I’m reflecting on where I stand in my ongoing research journey as an independent AI researcher.
🔹 Project Overview
Title: ChatGPT-5 in Lumbar MRI Interpretation: A Multidisciplinary Case Study
Author: Tawana Mohammadi
Affiliation: Tawana Network – Ethical AI & Data Transparency Research Hub
After publishing the preprint on Zenodo (currently under account review 🕐), I ran an AI-based peer review through Gemini 2.5 and GPT-5, both evaluating it as if for Q1 journals like Nature Scientific Reports and IEEE Transactions on Medical Imaging.
🧠 Feedback Summary
Both reviews praised:
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Transparent methodology (Human-AI Synergy Model)
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Professional academic writing
But they also emphasized:
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Sample size = 1, no statistical validation
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Need for quantitative metrics
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Requirement for an Ethics/IRB statement
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More formal tables & comparative visuals
Both concluded Major Revision, not rejection.
That’s actually encouraging — it means the work has potential once reinforced scientifically.
🔬 Action Plan (Major Revision)
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Expand dataset (10–20 lumbar MRI cases)
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Add quantitative evaluation (sensitivity, specificity, κ-value)
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Add visual comparison (Radiologist vs ChatGPT-5 vs GPT-4 Vision)
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Insert formal Ethics Statement + IRB ID
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Update abstract to reflect “single-case limitation”
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Prepare Rebuttal Letter for resubmission
🎯 Goal & Timeline
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Target journals: Scientific Reports, AI in Medicine, Diagnostics
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Expected window: Q1 2026
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End goal: Demonstrate a reproducible Human-AI diagnostic workflow under ethical AI frameworks.
🔗 Relevant Links
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🌐 Main site: https://tawana.online
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📄 Preprint DOI: (will re-upload after Zenodo reinstatement)
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💬 Contact: info (at) tawana (dot) online
🪶 Personal Note
This stage taught me how AI feedback can mirror real peer review — rigorous, structured, and even philosophical at times.
I see this as part of a bigger vision: promoting transparent, ethical, and human-centered AI research from independent labs like mine.
(© 2025 Tawana Mohammadi – All Rights Reserved)
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