Perceptions of Kidney Transplant Recipients Regarding the Role of Artificial Intelligence in Medicine

NCT07600541 | Recruiting

• 1 other identifier: AI_patient
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 2

Brief Summary

The AITX study is an international, multicenter survey exploring how kidney transplant recipients perceive artificial intelligence (AI) in medicine and, specifically, a system that predicts graft loss risk. Through an open-ended online questionnaire distributed across transplant centers and patient associations in France and the United States, the study captures patients' expectations, concerns, and the perceived impact of AI-driven prediction on their daily lives. Responses are analyzed using large language models (LLMs) with systematic human verification. The study aims to ensure that the deployment of AI in transplantation is ethical, transparent, and patient-centered.

Conditions

Kidney Transplant

Keywords

artificial intelligence; survey

Outcome Measures

Primary Outcomes (1)

• Perceptions of patients about AI

  Perceptions of kidney transplant recipients regarding the use of artificial intelligence, assessed with a questionnaire

  Baseline (corresponding to questionnaire administration)

Secondary Outcomes (1)

• Perceptions of patients about the use of a graft failure prediction system

  Baseline (corresponding to questionnaire administration)

Study Arms (11)

Vita move

Association of transplant recipients

France rein pays de la loire

Association of patients with kidney disease

France rein île de france

Association of patients with kidney disease

France rein nord pas de calais

Association of patients with kidney disease

Saint-Louis hospital

Hospital

Nice Pasteur

Hospital

Marseille hospital

hospital

Lille

hospital

The voice of the patient

Association of patients with kidney disease

Utah university

hospital

Mayo Clinic jacksonville

hospital

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

The study targets adult kidney transplant recipients (≥18 years), fluent in French or English, and able to provide electronic consent. Patients with severe cognitive impairment preventing comprehension or technical inability to access the online questionnaire are excluded. Participants are recruited through transplant centers and patient associations in France and the United States. The questionnaire will be distributed to an estimated 10,000-20,000 patients, with an expected response rate of 10-15%

You may qualify if:

• Age ≥ 18 years
• Fluency in French or English
• Electronic consent given

You may not qualify if:

• Severe cognitive impairment preventing comprehension
• Technical inability to access the questionnaire

Sponsors & Collaborators

• Paris Translational Research Center for Organ Transplantation: lead

Study Sites (2)

Le flambeau de la vie

Paris, Île-de-France Region, 75015, France

RECRUITING

Nice Pasteur

Paris, Île-de-France Region, 75015, France

RECRUITING

Related Publications (10)

• Young AT, Amara D, Bhattacharya A, Wei ML. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit Health. 2021 Sep;3(9):e599-e611. doi: 10.1016/S2589-7500(21)00132-1.

  PMID: 34446266 BACKGROUND

• https://osf.io/preprints/psyarxiv/pnx9e_v1

  RESULT

• Fritsch SJ, Blankenheim A, Wahl A, Hetfeld P, Maassen O, Deffge S, Kunze J, Rossaint R, Riedel M, Marx G, Bickenbach J. Attitudes and perception of artificial intelligence in healthcare: A cross-sectional survey among patients. Digit Health. 2022 Aug 8;8:20552076221116772. doi: 10.1177/20552076221116772. eCollection 2022 Jan-Dec.

  PMID: 35983102 RESULT

• Erul E, Aktekin Y, Danisman FB, Gumustas SA, Aktekin BS, Yekeduz E, Urun Y. Perceptions, Attitudes, and Concerns on Artificial Intelligence Applications in Patients with Cancer. Cancer Control. 2025 Jan-Dec;32:10732748251343245. doi: 10.1177/10732748251343245. Epub 2025 May 23.

  PMID: 40407404 RESULT

• Divard G, Raynaud M, Tatapudi VS, Abdalla B, Bailly E, Assayag M, Binois Y, Cohen R, Zhang H, Ulloa C, Linhares K, Tedesco HS, Legendre C, Jouven X, Montgomery RA, Lefaucheur C, Aubert O, Loupy A. Comparison of artificial intelligence and human-based prediction and stratification of the risk of long-term kidney allograft failure. Commun Med (Lond). 2022 Nov 23;2(1):150. doi: 10.1038/s43856-022-00201-9.

  PMID: 36418380 RESULT

• Truchot A, Raynaud M, Helantera I, Aubert O, Kamar N, Divard G, Astor B, Legendre C, Hertig A, Buchler M, Crespo M, Akalin E, Pujol GS, Ribeiro de Castro MC, Matas AJ, Ulloa C, Jordan SC, Huang E, Juric I, Basic-Jukic N, Coemans M, Naesens M, Friedewald JJ, Silva HT Jr, Lefaucheur C, Segev DL, Collins GS, Loupy A. Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure. J Am Soc Nephrol. 2025 Apr 1;36(4):688-701. doi: 10.1681/ASN.0000000517. Epub 2024 Oct 16.

  PMID: 40168162 RESULT

• Lombardi Y, Raynaud M, Schatzl M, Mayer KA, Diebold M, Patel UD, Schrezenmeier E, Akifova A, Budde K, Loupy A, Bohmig GA. Estimating the efficacy of felzartamab to treat antibody-mediated rejection using the iBox prognostication system. Am J Transplant. 2025 May;25(5):1130-1132. doi: 10.1016/j.ajt.2024.12.004. Epub 2024 Dec 12. No abstract available.

  PMID: 39674514 RESULT

• Loupy A, Preka E, Chen X, Wang H, He J, Zhang K. Reshaping transplantation with AI, emerging technologies and xenotransplantation. Nat Med. 2025 Jul;31(7):2161-2173. doi: 10.1038/s41591-025-03801-9. Epub 2025 Jul 14.

  PMID: 40659768 RESULT

• Raynaud M, Aubert O, Divard G, Reese PP, Kamar N, Yoo D, Chin CS, Bailly E, Buchler M, Ladriere M, Le Quintrec M, Delahousse M, Juric I, Basic-Jukic N, Crespo M, Silva HT Jr, Linhares K, Ribeiro de Castro MC, Soler Pujol G, Empana JP, Ulloa C, Akalin E, Bohmig G, Huang E, Stegall MD, Bentall AJ, Montgomery RA, Jordan SC, Oberbauer R, Segev DL, Friedewald JJ, Jouven X, Legendre C, Lefaucheur C, Loupy A. Dynamic prediction of renal survival among deeply phenotyped kidney transplant recipients using artificial intelligence: an observational, international, multicohort study. Lancet Digit Health. 2021 Dec;3(12):e795-e805. doi: 10.1016/S2589-7500(21)00209-0. Epub 2021 Oct 28.

  PMID: 34756569 RESULT

• Hart A, Gustafson SK, Wey A, Salkowski N, Snyder JJ, Kasiske BL, Israni AK. The association between loss of Medicare, immunosuppressive medication use, and kidney transplant outcomes. Am J Transplant. 2019 Jul;19(7):1964-1971. doi: 10.1111/ajt.15293. Epub 2019 Mar 5.

  PMID: 30838768 RESULT

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Scientific leader

Study Record Dates

• First Submitted: April 28, 2026
• First Posted: May 20, 2026
• Study Start: April 15, 2026
• Primary Completion (Estimated): September 30, 2026
• Study Completion (Estimated): December 30, 2026
• Last Updated: May 20, 2026

Record last verified: 2026-05

Data Sharing

• IPD Sharing: Will not share

Locations

FR (2)