Clinical trial · Interventional
Acceptability of Artificial Intelligence in the Diagnosis of Prostate Cancer
Understanding the Acceptability of Artificial Intelligence as a Support for Healthcare Providers in the Diagnosis of Prostate Cancer - the Patient at the Heart of His Care
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This study investigates the acceptability of artificial intelligence (AI) as a diagnostic support tool among patients with localized prostate cancer and healthcare providers, as well as their willingness to share health data for AI development. Background AI tools in healthcare show promising potential, especially in improving diagnosis accuracy and personalizing treatment. However, successful implementation depends not only on technical performance but also on the acceptability of AI among its users-both patients and professionals. Prior research has shown varied acceptability depending on context, disease severity, task performed by AI, and user population. Objectives Assess patients' acceptability of AI as a diagnostic support in prostate cancer. Explore patients' willingness to share health data for developing clinical AI. Assess healthcare providers' acceptability of AI in this diagnostic context. Methodology Design: A cross-sectional, mixed-method, multinational study (Belgium, Italy, Spain). Quantitative Phase: Online questionnaire, using adapted theoretical frameworks (Value Perception Model, NASSS-AI, TFA). Qualitative Phase: Will follow based on quantitative findings. Participants: Adults diagnosed with localized prostate cancer. Recruitment via hospitals, social media, and patient associations. Data Collected: Personal and health information, attitudes toward AI, willingness to share data. Ethics Approved by ethics committees in each participating country. Informed consent obtained digitally before participation. Data anonymized and GDPR-compliant.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Questionnaire and Physical Exam | Other | — | UNRESOLVED |
Design
Arms and outcomes
Arms (1)
- type
- EXPERIMENTAL
- label
- Questionnaire
- description
- This is an online questionnaire that the patients copmlete on their own or with the help of the responsable person
- interventionNames
- Other: Questionnaire and Physical Exam
Primary outcomes (4)
- measure
- Perceived Benefits of using AI in the diagnosis of prostate cancer
- timeFrame
- Baseline
- description
- They are latent measures and will be assessed in several questions: * Benefits: I believe that tools based on artificial intelligence can 'Improve the diagnosis of prostate cancer', 'Advance the prostate cancer diagnostic process', 'Provide an accurate diagnosis of prostate cancer', Reduce the costs of prostate cancer diagnosis'. Scale: "Strongly disagree", "Disagree", "Somewhat disagree", "Neither agree nor disagree", "Somewhat agree", "Agree", "Strongly agree"; 'Strongly agree' gives the highest value of benefit.
- measure
- Perceived Risks of using AI in the prostate cancer diagnosis
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: * over 18 years; * with diagnose of localized prostate cancer; * consent to the study. Exclusion Criteria: * not speaking French; * diagnosed with metastatic cancer from the outset; * terminally ill; * people suffering from mental retardation, dementia or altered state of consciousness.
References
Publications (3)
- RESULTFrost EK, Bosward R, Aquino YSJ, Braunack-Mayer A, Carter SM. Facilitating public involvement in research about healthcare AI: A scoping review of empirical methods. Int J Med Inform. 2024 Jun;186:105417. doi: 10.1016/j.ijmedinf.2024.105417. Epub 2024 Mar 22. PMID 38564959
- RESULTEsmaeilzadeh P. Use of AI-based tools for healthcare purposes: a survey study from consumers' perspectives. BMC Med Inform Decis Mak. 2020 Jul 22;20(1):170. doi: 10.1186/s12911-020-01191-1. PMID 32698869
- RESULTFazakarley CA, Breen M, Thompson B, Leeson P, Williamson V. Beliefs, experiences and concerns of using artificial intelligence in healthcare: A qualitative synthesis. Digit Health. 2024 Feb 11;10:20552076241230075. doi: 10.1177/20552076241230075. eCollection 2024 Jan-Dec. PMID 38347935