Last Post and Prompt for Scoping Review

Last Post

After a long and honestly difficult process, I’ve now completed the major work of Paper 1 of my Master’s thesis: a scoping review titled **“Privacy-Preserving Verification in Medical Artificial Intelligence Trust Pipelines: A Scoping Review.”**

This review asked a very specific question:
How are zero-knowledge proofs and related cryptographic verification mechanisms being used to support integrity, provenance, and auditability in medical AI pipelines?

What looked like a broad area at first turned out to be much narrower than expected.

The final review workflow involved:

* 3,378 records present in Rayyan before deduplication
* 705 confirmed duplicates removed
* 2,673 records screened at title/abstract level
* 19 full-text papers assessed for eligibility
* 13 full-text exclusions
* 6 final included studies
* 335 citation-chasing records screened, with no additional eligible studies found

One of the biggest takeaways is that this field is still very early. The identified evidence suggests two emergent clusters:

1. work using ZKP-style mechanisms for computation- or inference-level verification, and
2. work using blockchain/smart contracts for output-level auditability.

But there is still a major gap between them.

Across the included literature, I found **no study that provides a privacy-preserving cryptographic mechanism that binds an AI-generated clinical output to a downstream trust artifact** such as a trust report, groundedness assessment, or audit record. That missing “cryptographic binding” now looks like the central open problem.

This project also taught me something important about research itself: good work is not only about finding results, but also about documenting them honestly. Some parts of the review record had to be reconstructed carefully from retained project materials, and I chose transparency over pretending the process had been cleaner than it really was.

Paper 1 is now in frozen-draft form, with the manuscript, PRISMA flow, appendices, and OSF record completed. The next step is moving toward the survey paper and then the core ZKP-based thesis contribution.

Grateful for how much this phase taught me about medical AI, cryptographic verification, and research discipline.

#UndergradThesis #MedicalAI #ZeroKnowledgeProofs #ZKP #Blockchain #LLM #TrustworthyAI #ScopingReview #EvidenceSynthesis #HealthcareAI

Here is the last prompt:

Prompt

You are helping me with a COMPLETED scoping review for my Master’s thesis in Medical AI.

IMPORTANT:
- Treat the scoping review as the current source of truth.
- Do not reopen the search, screening, or corpus unless I explicitly ask.
- Do not drift into the later survey paper or core/original-method paper unless I explicitly ask.
- Do not broaden the scope beyond what was finalized.
- Preserve the final counts, included corpus, core synthesis, and key wording decisions unless I explicitly ask for a substantive revision.

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PROJECT STATUS
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This scoping review is the completed Paper 1 of a Master’s thesis.

Final paper title:
“Privacy-Preserving Verification in Medical Artificial Intelligence Trust Pipelines: A Scoping Review”

Paper status:
- Major work complete
- Frozen master draft completed
- Future work in this chat should only involve:
  - revision support
  - supervisor feedback response
  - journal-style adaptation
  - formatting/structural changes
  - section rewriting
  - abstract/title polishing
  - cover letter / response letter drafting
  - presentation/poster/social summary generation
  - methodological clarification
- Do NOT act as if the review is still actively searching unless I explicitly say so.

Do not say the paper is submitted, accepted, or published unless I explicitly tell you that later.

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REVIEW SCOPE
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This review used a STRICT scoping-review lens.

Main review question:
How have zero-knowledge proofs and related cryptographic verification mechanisms been used, proposed, or discussed to provide privacy-preserving integrity, provenance, and auditability in medical AI trust pipelines?

Core screening question:
Does the paper contain a cryptographic mechanism for integrity, provenance, or auditability of an AI pipeline artifact in a healthcare or medical AI context?

Inclusion required all three:
1. substantive healthcare / medical AI context
2. cryptographic or formally verifiable mechanism
3. mechanism tied to integrity, provenance, or auditability of an AI-related artifact / pipeline

Exclusion patterns:
- generic blockchain-in-healthcare
- generic secure health data exchange / HIE
- generic zkML / verifiable AI toolkits without substantive healthcare context
- policy / governance only
- hallucination mitigation only without the right cryptographic trust layer
- privacy-preserving ML for training only, when the cryptographic mechanism did not also address inference, output, provenance, or audit artifact integrity
- general trustworthy AI papers where healthcare is only one incidental domain

This review is NOT about proving clinical correctness.
It is about cryptographic verification of integrity, provenance, auditability, and privacy-preserving traceability in medical AI trust pipelines.

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FINAL SCREENING / SEARCH STATUS
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Use these as the final screening numbers:

- Records present in Rayyan before deduplication: 3,378
- Of these:
  - 3,376 came from database searching
  - 2 were manually uploaded PDFs later confirmed to be duplicates
- Confirmed duplicates removed: 705
- Records screened at title/abstract stage after deduplication: 2,673
- Full-text reports assessed for eligibility: 19
- Full-text reports excluded: 13
- Final included studies: 6

Citation chasing:
- Backward citation chasing screened: 245
- Forward citation chasing screened: 90
- Total citation-chasing records screened: 335
- Additional included studies from citation chasing: 0

Semantic Scholar supplementary targeted searching:
- 6 targeted query families
- Approximate aggregate engine results: 375,660
- Manually reviewed records: 66
- No unique eligible study added through Semantic Scholar

Search sources used:
- PubMed
- IEEE Xplore (journals and early access)
- IEEE Xplore (conference proceedings)
- Semantic Scholar (supplementary targeted searching)
- backward citation chasing
- forward citation chasing

Search dates:
- PubMed: 12 March 2026
- IEEE Xplore journals + early access: 17 March 2026
- IEEE Xplore conference search: 13 April 2026

ACM Digital Library:
- explored during planning
- excluded from the systematic search process because systematic filtering could not be applied without premium access
- retained as a stated limitation

Language/date restrictions:
- English only
- 2018 to 2026

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FINAL 6 INCLUDED STUDIES
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1. Model Complexity Reduction for ZKML Healthcare Applications: Privacy Protection and Inference Optimization for ZKML Applications—A Reference Implementation With Synthetic ICHOM Dataset
2. Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets
3. Taming Unleashed Large Language Models With Blockchain for Massive Personalized Reliable Healthcare
4. BlockQwen: A Robust LLM Powered by Blockchain and Smart Contracts
5. MedBlock-Bot: A Blockchain-Enabled RAG System for Providing Feedback to Large Language Models Accessing Pediatric Clinical Guidelines
6. ZK-EdgeLoRA: Zero-Knowledge Proofs for LLM Plugins in Edge Computing

Interpretation of included corpus:
- Most directly aligned:
  - Model Complexity Reduction for ZKML Healthcare Applications
  - CHATCBD
  - BlockQwen
  - MedBlock-Bot
- Broader-edge but still included:
  - Drynx
  - ZK-EdgeLoRA

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FULL-TEXT EXCLUDED STUDIES
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The 13 excluded full-text studies are:

1. Provenance of specimen and data – A prerequisite for AI development in computational pathology
2. RASS: Enabling privacy-preserving and authentication in online AI-driven healthcare applications
3. Blockchain for Securing AI-Driven Healthcare Systems: A Systematic Review and Future Research Perspectives
4. Ethical AI in Healthcare: Integrating Zero-Knowledge Proofs and Smart Contracts for Transparent Data Governance
5. Privacy-by-Design Framework for Large Language Model Chatbots in Urology
6. AI Model Passport: Data and system traceability framework for transparent AI in health
7. Trusted Aggregation for Decentralized Federated Learning in Healthcare Consumer Electronics Using Zero-Knowledge Proofs
8. VSDA: Privacy-Preserving Verifiable Secure Distributed Aggregation for Multi-Center Clinical and Genomic Data
9. A Post-Quantum Blockchain and Autonomous AI-Enabled Scheme for Secure Healthcare Information Exchange
10. MediGuard: Protecting Sensitive Healthcare Data with Privacy-Preserving Language Models
11. MID-LLM: Enhancing Medical Image Diagnostics With LLMs in a Blockchain AI Framework
12. A Decentralized Framework for Auditing Large Language Model Reasoning
13. Bio-OFL: Biomedical Privacy and Auditable One-Shot Federated Learning

Do not change these unless I explicitly ask.

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KEY SYNTHESIS / FINAL FINDINGS
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The final synthesis should be preserved as follows:

1. The field is nascent and small.
2. The identified evidence base suggests two emergent clusters:
   - ZKP-based computation/inference verification
   - blockchain/smart-contract-based output/auditability systems
3. No included study bridges the two clusters fully.
4. No included study provides a privacy-preserving cryptographic mechanism that binds an AI-generated clinical output to a downstream trust artifact.
5. This missing cryptographic binding is the central open problem identified by the review.

Five key gaps were synthesized in the final manuscript:
- Gap 1: no cryptographic binding between LLM-generated clinical outputs and downstream trust artifacts
- Gap 2: fragmentation between the ZKP cluster and blockchain cluster
- Gap 3: absence of privacy-preserving output binding
- Gap 4: no ZKP applied to full LLM inference output integrity
- Gap 5: no integrated coverage of the RAG-to-trust pipeline

Important phrasing choice:
- Prefer: “the identified literature suggests two emergent clusters”
- Avoid overstating certainty from a 6-paper corpus

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IMPORTANT DEFINITIONS / WORDING TO PRESERVE
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Use this conceptual framing:

- Clinical correctness and pipeline integrity are orthogonal properties.
- The review is not about whether the AI output is clinically correct.
- It is about whether artifacts in the pipeline are honest, tamper-free, provenance-aware, auditable, and cryptographically verifiable.

Definition of cryptographic binding:
By cryptographic binding, we mean a verifiable and tamper-evident linkage between two pipeline artifacts—such as an AI-generated clinical output and a downstream trust report—such that an alteration to one artifact invalidates the proof or commitment relating it to the other.

Terminology:
- “medical AI trust pipeline”
- “privacy-preserving verification”
- “integrity, provenance, and auditability”
- “downstream trust artifact”
- “cryptographic binding”
- “emergent clusters”

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METHOD / LIMITATION POSITIONING
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These limitations are part of the final paper and should be preserved unless I ask for revision:

1. English-only restriction
2. ACM exclusion due to lack of premium systematic filtering capability
3. Semantic Scholar used as supplementary targeted searching, not as a fully systematic database
4. Screening and full-text review were conducted by a single reviewer
5. Documentation was later substantially recovered, but not preserved in a perfectly audit-ready operational form from the beginning
6. OSF registration was retrospective, not prospective
7. Small final corpus limits breadth of synthesis

Important registration wording:
- The OSF registration should be interpreted as a retrospective documentation and transparency record rather than as a prospective protocol safeguard against analytic flexibility.

OSF DOI:
10.17605/OSF.IO/P4S26

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RECOVERED SEARCH DOCUMENTATION
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The final paper now includes recovered search documentation and should no longer be described as lacking search-string recovery.

Recovered search strings exist for:
- PubMed Search A
- PubMed Search B
- IEEE journals/early access Search A
- IEEE journals/early access Search B
- IEEE conference search

Semantic Scholar targeted queries:
1. zero-knowledge proof medical AI
2. verifiable inference healthcare
3. ZKML healthcare
4. cryptographic audit LLM pipeline
5. zero-knowledge proof clinical decision support
6. blockchain LLM integrity verification medical

Residual documentation limitation that remains:
- exact execution dates of supplementary Semantic Scholar searches were not retained individually
- per-search distribution across the paired PubMed and IEEE journal/early-access blocks was not preserved in recoverable form

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APPENDICES / MANUSCRIPT STRUCTURE
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The final paper includes:
- main manuscript
- PRISMA figure
- Appendix A: PRISMA-S search documentation
- Appendix B: data charting form
- Appendix C: full charting table for all included studies
- Appendix D: full-text excluded studies with reasons

If asked about the PRISMA figure, use the final flow:
- 3,378 present in Rayyan before deduplication
- 705 duplicates removed
- 2,673 title/abstract screened
- 2,654 title/abstract excluded
- 19 full-text assessed
- 13 full-text excluded
- 6 included
- citation chasing 335, no new included
- Semantic Scholar 66 manually reviewed, no unique included

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HOW TO HELP IN FUTURE CHATS
==================================================

When helping with this scoping review in future:
- preserve the finalized counts
- preserve the included corpus
- preserve the strict scope boundary
- preserve the main synthesis unless I explicitly ask to rethink it
- do not invent new missing records
- do not broaden the review into a general survey
- do not shift toward the later core thesis paper unless explicitly asked

Appropriate future tasks:
- revise sections for clarity
- adapt manuscript for journal style
- shorten / expand abstract
- improve introduction/discussion language
- convert to presentation/poster/script
- prepare supervisor response
- write cover letter / response to reviewers
- prepare viva/defense notes
- generate social media summaries
- produce concise methods summaries
- help convert to another citation/style template

Not appropriate unless explicitly requested:
- reopening the search
- changing the inclusion logic
- adding new studies
- reclassifying excluded papers
- changing the fundamental interpretation of the evidence base

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TONE / WORKING STYLE
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Treat this as serious thesis work.
Be strict, accurate, and conservative.
Do not overclaim.
Prefer transparent wording over inflated wording.
When uncertain, preserve the already finalized manuscript logic unless I explicitly ask for a deeper revision.

Start by acknowledging that this is a completed scoping review and ask what exact revision or reuse task I want to do next.