Transparency In The reporting of Artificial INtelligence – the TITAN guideline

Riaz A. Agha¹ , Ginimol Mathew² , Rasha Rashid³ , Ahmed Kerwan⁴ , Ahmed Al-Jabir⁵ , Catrin Sohrabi² , Thomas Franchi⁶ , Maria Nicola⁷ , Maliha Agha¹ ; TITAN Group

1. Premier Science, London, UK
2. Royal Free London NHS Foundation Trust, London, UK
3. Imperial College School of Medicine, London, UK
4. Harvard T.H. Chan School of Public Health, Boston, USA
5. University College London Hospital, London, UK
6. Wellington Regional Hospital, Te Whatu Ora Capital Coast and Hutt Valley, Wellington, New Zealand
7. Imperial College London, London, UK

Correspondence to: Riaz Agha, Premier Science riaz@premierscience.com 

DOI: https://doi.org/10.70389/PJS.100082

TITAN Group Contributors

1. Achilleas Thoma, McMaster University, Canada
2. Alessandro Coppola, Sapienza University of Rome, Italy
3. Andrew J Beamish, Swansea Bay University Health Board, Swansea University, UK
4. Ashraf Noureldin, Almana Hospital, Khobar, Saudi Arabia
5. Ashwini Rao, Manipal Academy of Higher Education Manipal, India
6. Baskaran Vasudevan, MIOT Hospital, Chennai, India
7. Ben Challacombe, Guy’s and St Thomas’ Hospitals, UK
8. C S Pramesh, Tata Memorial Hospital, Homi Bhabha National Institute and National Cancer Grid, India
9. Duilio Pagano, IRCCS-ISMETT – UPMC Italy, Italy
10. Frederick Heaton Millham, Harvard Medical School, USA
11. Gaurav Roy, Cactus Communications Pvt Ltd, India
12. Huseyin Kadioglu, Saglik Bilimleri Universitesi, Turkiye
13. Iain James Nixon, NHS Lothian, UK
14. Indraneil Mukherjee, Staten Island University Hospital Northwell Health, USA
15. James Anthony McCaul, Queen Elizabeth University Hospital Glasgow and Institute for Cancer Therapeutics University of Bradford, UK
16. James Ngu, Changi General Hospital, Singapore
17. Joerg Albrecht, Cook County Health, USA
18. Juan Gomez Rivas, Hospital Clinico San Carlos, Madrid, Spain
19. K Veena L Karanth, District Hospital Udupi, India
20. Kandiah Raveendran, Fatimah Hospital, Malaysia
21. M Hammad Ather, Aga Khan University, Pakistan
22. Mangesh A. Thorat, Centre for Cancer Screening, Prevention and Early Diagnosis, Wolfson Institute of Population Health, Queen Mary University of London, London, UK; Breast Services, Homerton University Hospital, London, UK
23. Mohammad Bashashati, Dell Medical School, UT Austin, USA
24. Mushtaq Chalkoo, Government Medical College, Srinagar, Kashmir, India
25. Oliver J. Muensterer, Dr. von Hauner Children’s Hospital, LMU Medical Center, Munich, Germany
26. Patrick Bradley, Nottingham University Hospital, UK
27. Prabudh Goel, All India Institute of Medical Sciences, New Delhi, India
28. Prathamesh Pai, P D Hinduja Hospital, Khar, India
29. Priya Shinde, Homerton University Hospital, UK
30. Priya Ranganathan, Tata Memorial Centre, India
31. Raafat Yahia Afifi Mohamed, Cairo University, Egypt
32. Richard David Rosin, University of the West Indies Barbados, Barbados
33. Roberto Cammarata, Fondazione Policlinico Campus Biomedico, Italy
34. Roberto Coppola, Campus Bio Medico University, Italy
35. Rolf Wynn, UiT The Arctic University of Norway, Norway
36. Salim Surani, Texas A&M University, USA
37. Salvatore Giordano, University of Turku, Finland
38. Samuele Massarut, Centro di Riferimento Oncologico Aviano IRCCS, Italy
39. Shahzad G. Raja, Harefield Hospital, UK
40. Somprakas Basu, All India Institute of Medical Sciences Rishikesh, India
41. Syed Ather Enam, Aga Khan University, Pakistan
42. Teo Nan Zun, Changi General Hospital, Singapore
43. Todd Manning, Bendigo Health and Monash University, Australia
44. Veeru Kasivisvanathan, University College London, UK
45. Vincenzo La Vaccara, Fondazione Policlinico Campus Bio-Medico di Roma, Italy
46. Zubing Mei, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, China

Additional information

• Ethical approval: N/a
• Consent: N/a
• Funding: None
• Conflicts of interest: The authors have no financial, consultative, institutional, or other relationships that might lead to bias or a conflict of interest.
• Author contribution: R.A.A.: conceptualisation and study design, supervision of the Delphi process, data interpretation, manuscript drafting and critical revision, approval of the final manuscript. A.K., A.A.-J., C.S., T.F., G.M., M.N., R.R., M.A. R.A.A: Participation in study design, generation of Delphi survey materials, data collection and analysis, contribution to drafting of new checklist items, manuscript writing and revision, approval of the final manuscript.
• Guarantor: Riaz A Agha
• Provenance and peer-review:
  Unsolicited and externally peer-reviewed
• Data availability statement: The Delphi survey data that informed this guideline (individual expert ratings and comments) are confidential and not publicly available, in accordance with the consensus process protocol. All relevant aggregated results are reported in this article.

Keywords: AI transparency guidelines, AI use in research, delphi consensus exercise, scare process strocss updates, scholarly publishing.

Peer-review
Received: 22 May 2025
Revised: 23 May 2025
Accepted: 23 May 2025
Published: 23 May 2025

Introduction

Artificial intelligence (AI) is increasingly being used in research and the development of the scholarly literature.^1-3 With this comes the need for transparency in the reporting of its use. It is now incumbent on editors, journals, publishers and the wider scholarly publishing community to ensure authors declare this in a transparent and comprehensive way.  The recent update to the SCARE, PROCESS and STROCSS guidelines has moved us significantly in this direction and of course as AI and its use evolves, so will the guidelines.^4-6 These guidelines were updated through a Delphi consensus exercise and the papers went through peer-review, AI review, editorial review and subsequent refinement. 

Here we provide a short guideline which allows for the declaration of AI use in other article types, such as review articles, other experimental study types, editorials, letters and so on, to ensure transparency in their reporting too.  The guideline items cover; declaration, purpose and scope, AI tools and configuration, data inputs and safeguards, human oversight and verification, bias, ethics and regulatory compliance and reproducibility and transparency.

Methods

The guideline development group responsible for the recent SCARE, PROCESS and STROCSS guideline updates reconvened to develop this general-purpose use of AI guideline.  Here we utilize the same items that have already been approved through the SCARE, PROCESS and STROCSS guideline development process (table 1). Given how these items have already gone through a Delphi consensus exercise amongst 49 participants with over 90% response and strong agreement, we felt it unnecessary to repeat this exercise. 

Table 1: The TITAN Guideline items
TITAN Guideline Checklist 2025
Topic	Item	Description	Page number
Artificial Intelligence (AI) (some journals may prefer this in the methods and/or acknowledgments section and it should also be declared in the cover letter)	1	Declaration of whether any AI was used in the research and manuscript development   State no, if that’s the case. If yes, proceed to item 1a
	1a	Purpose and Scope of AI Use – Precisely state why AI was employed (e.g. development of research questions, language drafting, statistical analysis/summarisation, image annotation, etc).  – Was generative AI utilised and if so, how? – Clarify the stage(s) of the reporting workflow affected (planning, writing, revisions, figure creation). – Confirmation that the author(s) take responsibility for the integrity of the content affected/generated
	1b	AI Tool(s) and Configuration – Name each system (vendor, model, major version/date).  – State the date it was used – Specify relevant parameters (e.g. prompt length, plug-ins, fine-tuning, temperature).  – Declare whether the tool operated locally on-premises, or via a cloud API and any integrations with other systems.
	1c	Data Inputs and Safeguards – Describe categories of data provided to the AI (patient text, de-identified images, literature abstracts).  – Confirm that all inputs were de-identified and compliant with GDPR/HIPAA.  – Note any institutional approvals or data-sharing agreements obtained.
	1d	Human Oversight and Verification – Identify the supervising author(s) who reviewed every AI output.  – Detail the process for fact-checking, clinical accuracy checks – State whether any AI-generated text/figures were edited or discarded. – Acknowledge the limitations of AI and its use
	1e	Bias, Ethics and Regulatory Compliance – Outline steps taken to detect and mitigate algorithmic bias (e.g. cross-checking against under-represented populations).  – Affirm adherence to relevant ethical frameworks. – Disclose any conflicts of interest or financial ties to AI vendors.
	1f	Reproducibility and Transparency – Provide the exact prompts or code snippets (as supplementary material if lengthy).  – Supply version-controlled logs or model cards where possible. – if applicable, state repository, hyperlink or digital object identifier (DOI) where AI-generated artefacts can be accessed, enabling attempts at independent replication of the query/input.

Conclusion

The authors commend these items to the scholarly community to aid with transparency in the reporting of AI use (TITAN). We will monitor the development of AI use in research and the scholarly literature to ensure these guidelines remain up to date. 

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References

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   https://doi.org/10.25100/cm.v54i3.5868
4. Ahmed Kerwan, Ahmed Al-Jabir, Ginimol Mathew, Catrin Sohrabi, Rasha Rashid, Thomas Franchi, Maria Nicola, Maliha Agha, Riaz A. Agha. Revised Surgical CAse REport (SCARE) guideline: An update for the age of Artificial Intelligence. Premier Journal of Science 2025:10;100079
5. Agha RA, Mathew G, Rashid R, Kerwan A, Al-Jabir A, Sohrabi C, Franchi T, Nicola M, Agha M. Revised Preferred Reporting of Case Series in Surgery (PROCESS) Guideline: An update for the age of Artificial Intelligence. Premier Journal of Science 2025:10;100080
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Cite this article as:
Agha RA, Mathew G, Rashid R, Kerwan A, Al-Jabir A, Sohrabi C, Franchi T, Nicola M, Agha M, TITAN Group. Transparency In The reporting of Artificial INtelligence
– the TITAN guideline. Premier Journal of Science 2025:10;100082

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