USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIANCT07395713
The Predictability of the Necessity for Cardiology Consultation in Patients Scheduled for Non-Cardiac Surgery Using Artificial Intelligence Models in Preoperative Anesthesia Assessment
An observational study of USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA and PREOPERATIVE CARDIOLOGY CONSULTATION REQUIREMENT, run by Bursa City Hospital at 1 site in Turkey (Türkiye).
Trial summary
Structured Summary Title Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models Background Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA, PREOPERATIVE CARDIOLOGY CONSULTATION REQUIREMENT
- What is being tested?
- Patient scenarios were presented to different AI models (ChatGPT 4.5, ChatGPT 5, Copilot, Deepseek, Grok, Claude, Gemini Flash, Gemini Pro) with and without prompts.
- How many people
- 183 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Jan 2025to 15 Mar 2026
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: This study will be conducted with the participation of patients who will be referred to cardiology following an examination at the anesthesia clinic prior to undergoing non-cardiac surgery.
Who may be able to join
- Adults aged 18 years or older
- ASA physical status classification I-IV
- Scheduled for non-cardiac surgery
- Patients evaluated preoperatively by anesthesia residents with less than two years of clinical experience
- Availability of complete clinical data including medical history, ECG findings, and chest radiography
- Ability to provide informed consent
Who cannot join
- Patients younger than 18 years of age
- Patients undergoing cardiac surgery
- Patients with incomplete clinical data
- Patients who declined participation
- Emergency surgery cases
- Patients unable to undergo standard preoperative evaluation
Trial locations and contacts
1 site in 1 country.
About this study
Official title: The Effectiveness of Using Artificial Intelligence (Chat GPT) in Cardiac Assessment During Anesthesia Examination of Preoperative Cases
Structured Summary Title
Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models
Read more
Background
Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays.
Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations.
Study Design
Prospective, observational, comparative study.
Ethical Approval
The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki.
Sample Size
Sample size was calculated using G*Power software based on anticipated effect size and statistical power requirements.
Participants
Inclusion Criteria:
Adults aged 18 years or older
ASA physical status I-IV
Scheduled for non-cardiac surgery
Evaluated by anesthesia residents with less than two years of clinical experience
Exclusion Criteria:
Pediatric patients
Patients declining participation
Incomplete clinical data
Data Collection
The following patient data will be recorded:
Demographics (age, sex, BMI)
Medical history (comorbidities, medication use, allergies, substance use)
Functional capacity (METs score)
ECG findings
Chest radiography findings
Planned surgical procedure characteristics
AI Model Evaluation
Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats:
Prompted format:
"You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation."
Non-prompted format:
"Evaluate whether this patient requires cardiology consultation."
AI recommendations will not influence clinical decision-making.
Outcome Measures
Primary and secondary analyses will include:
Agreement between AI recommendations and expert anesthesiologist evaluations
Readability of AI-generated responses
Quality assessment of responses
Classification performance comparisons across models
Statistical Analysis
Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p < 0.05 will be applied.
Study Objective
The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.
Read the detailed description
Structured Summary Title
Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models
Background
Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays.
Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations.
Study Design
Prospective, observational, comparative study.
Ethical Approval
The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki.
Sample Size
Sample size was calculated using G*Power software based on anticipated effect size and statistical power requirements.
Participants
Inclusion Criteria:
Adults aged 18 years or older
ASA physical status I-IV
Scheduled for non-cardiac surgery
Evaluated by anesthesia residents with less than two years of clinical experience
Exclusion Criteria:
Pediatric patients
Patients declining participation
Incomplete clinical data
Data Collection
The following patient data will be recorded:
Demographics (age, sex, BMI)
Medical history (comorbidities, medication use, allergies, substance use)
Functional capacity (METs score)
ECG findings
Chest radiography findings
Planned surgical procedure characteristics
AI Model Evaluation
Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats:
Prompted format:
"You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation."
Non-prompted format:
"Evaluate whether this patient requires cardiology consultation."
AI recommendations will not influence clinical decision-making.
Outcome Measures
Primary and secondary analyses will include:
Agreement between AI recommendations and expert anesthesiologist evaluations
Readability of AI-generated responses
Quality assessment of responses
Classification performance comparisons across models
Statistical Analysis
Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p < 0.05 will be applied.
Study Objective
The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.
- Conditions
- USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA, PREOPERATIVE CARDIOLOGY CONSULTATION REQUIREMENT
- Being tested
- Patient scenarios were presented to different AI models (ChatGPT 4.5, ChatGPT 5, Copilot, Deepseek, Grok, Claude, Gemini Flash, Gemini Pro) with and without prompts.
- Keywords
- THE ROLE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA, ARTIFICIAL INTELLIGENCE IN CONSULTATION NEEDS
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Patient scenarios were presented to different AI models (ChatGPT 4.5, ChatGPT 5, Copilot, Deepseek, Grok, Claude, Gemini Flash, Gemini Pro) with and without prompts.Other
Responses:
Compared with expert opinion according to the ESC 2024 guidelines
Evaluated using the Ateşman readability score and the Global Quality Scale (GQS)
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement Between AI Model Recommendations and Expert Anesthesiologist Decision Regarding Cardiology Consultation Requirement
The level of agreement between artificial intelligence model recommendations and expert anesthesiologist evaluations for cardiology consultation necessity will be assessed using Cohen's Kappa coefficient based on ESC 2024 guidelines.
Measured: At baseline preoperative evaluation (Day 1)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted9 Feb 2026
- Study start1 Jan 2025
- Primary completion30 Jun 2025
- Study completion15 Mar 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIAStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Enrolment
- 183 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Bursa City Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, eralp çevikkalp, Bursa City Hospital
- Investigators
- eralp çevikkalp, Principal investigator, bursa şehir hastanesi
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07395713
- Sponsor's protocol ID
- Bursa Şehir Hastanesi
- Status last verified
- Jan 2026
- First submitted
- 20 Dec 2025
- Last updated
- 9 Feb 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: Due to our commitment to patient rights, data privacy, and the consent form
Publications
1.M. Graeßner et al., "Enabling personalized perioperative risk prediction by using a machinelearning model based on preoperative data," Scientific Reports, vol. 13, no. 1, May 2023, doi: 10.1038/s41598-023-33981-8. 2.B. Choi et al., "Prediction Model for 30-Day Mortality after Non-Cardiac Surgery Using MachineLearning Techniques Based on Preoperative Evaluation of Electronic Medical Records," Journal of Clinical Medicine, vol. 11, no. 21, p. 6487, Nov. 2022, doi: 10.3390/jcm11216487. 3.M. Vine et al., "Innovative approaches to preoperative care including feasibility, efficacy, and ethical implications: a narrative review," AME Surgical Journal, vol. 4. AME Publishing Company, p. 1, Feb. 01, 2024. doi: 10.21037/asj-23-41. 4.P. Chung, C. T. Fong, A. M. Walters, N. Aghaeepour, M. Yetişgen, and V. N. O'Reilly-Shah, "Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication," JAMA Surgery, vol. 159, no. 8, American Medical Association, p. 928, Jun. 05, 2024. doi: 10.1001/jamasurg.2024.1621 5.T. Yurttas, R. Hidvegi, and M. Filipovic, "Biomarker-Based Preoperative Risk Stratification for Patients Undergoing Non-Cardiac Surgery," Journal of Clinical Medicine, vol. 9, no. 2, p. 351, Jan. 2020, doi: 10.3390/jcm9020351 6.J. Stones and D. Yates, "Clinical risk assessment tools in anaesthesia," BJA Education, vol. 19, no. 2. Elsevier BV, p. 47, Dec. 15, 2018. doi: 10.1016/j.bjae.2018.09.009. 7. Engineering as an Important Emerging Skill for Medical Professionals: Tutorial. J Med Internet Res. 2023 Oct 4;25:e50638. doi: 10.2196/50638. PMID: 37792434; PMCID: PMC10585440. 8. ATEŞMAN, Ender. (1997). Türkçe'de okunabilirliğin Ölçülmesi. A.Ü. Tömer Dil Dergisi, sayı:58,s.171174. 9. Coskun B, Ocakoglu G, Yetemen M, Kaygisiz O. Can ChatGPT, an Artificial Intelligence Language Model, Provide Accurate and High-quality Patient Information on Prostate Cancer? Urology. 2023 Oct;180:35-58. doi: 10.1016/j.urology.2023.05.040. Epub 2023 Jul 4. PMID: 37406
Study at a glance
From the registry record, last updated by the sponsor on 9 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- The Effectiveness of Using Artificial Intelligence (Chat GPT) in Cardiac Assessment During Anesthesia Examination of Preoperative Cases
- ClinicalTrials.gov ID
- NCT07395713
- Acronym / sponsor ID
- Bursa Şehir Hastanesi
- Conditions
- USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA, PREOPERATIVE CARDIOLOGY CONSULTATION REQUIREMENT
- Keywords
- THE ROLE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA, ARTIFICIAL INTELLIGENCE IN CONSULTATION NEEDS
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 183 participants (estimated)
- Lead sponsor
- Bursa City HospitalOther government
- Study start
- 1 Jan 2025 (actual)
- Primary completion
- 30 Jun 2025 (actual)
- Study completion
- 15 Mar 2026 (estimated)
- First posted
- 9 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Structured Summary Title
Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models
Background
Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays.
Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations.
Study Design
Prospective, observational, comparative study.
Ethical Approval
The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki.
Sample Size
Sample size was calculated using G*Power software based on anticipated effect size and statistical power requirements.
Participants
Inclusion Criteria:
Adults aged 18 years or older
ASA physical status I-IV
Scheduled for non-cardiac surgery
Evaluated by anesthesia residents with less than two years of clinical experience
Exclusion Criteria:
Pediatric patients
Patients declining participation
Incomplete clinical data
Data Collection
The following patient data will be recorded:
Demographics (age, sex, BMI)
Medical history (comorbidities, medication use, allergies, substance use)
Functional capacity (METs score)
ECG findings
Chest radiography findings
Planned surgical procedure characteristics
AI Model Evaluation
Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats:
Prompted format:
"You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation."
Non-prompted format:
"Evaluate whether this patient requires cardiology consultation."
AI recommendations will not influence clinical decision-making.
Outcome Measures
Primary and secondary analyses will include:
Agreement between AI recommendations and expert anesthesiologist evaluations
Readability of AI-generated responses
Quality assessment of responses
Classification performance comparisons across models
Statistical Analysis
Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p < 0.05 will be applied.
Study Objective
The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.
Detailed description
Structured Summary Title
Predictability of Cardiology Consultation Requirement in Patients Undergoing Non-Cardiac Surgery Using Artificial Intelligence Models
Background
Preoperative cardiac risk assessment is essential for minimizing perioperative morbidity and mortality in patients undergoing non-cardiac surgery. Cardiology consultations are often requested to assess surgical eligibility and reduce complication risks. However, unnecessary consultations may contribute to inefficient healthcare resource utilization and procedural delays.
Recent advances in artificial intelligence, particularly large language models, have demonstrated potential in clinical decision support systems. The European Society of Cardiology (ESC) 2024 guidelines provide a structured framework for evaluating perioperative cardiac risk. This study aims to investigate whether AI-based models can assist in predicting the need for cardiology consultation and to examine the effect of prompted versus non-prompted input formats on AI recommendations.
Study Design
Prospective, observational, comparative study.
Ethical Approval
The study has been approved by the Bursa City Hospital Ethics Committee and will be conducted in accordance with the Declaration of Helsinki.
Sample Size
Sample size was calculated using G*Power software based on anticipated effect size and statistical power requirements.
Participants
Inclusion Criteria:
Adults aged 18 years or older
ASA physical status I-IV
Scheduled for non-cardiac surgery
Evaluated by anesthesia residents with less than two years of clinical experience
Exclusion Criteria:
Pediatric patients
Patients declining participation
Incomplete clinical data
Data Collection
The following patient data will be recorded:
Demographics (age, sex, BMI)
Medical history (comorbidities, medication use, allergies, substance use)
Functional capacity (METs score)
ECG findings
Chest radiography findings
Planned surgical procedure characteristics
AI Model Evaluation
Multiple AI language models will be tested using standardized patient scenarios. Each scenario will be presented in two formats:
Prompted format:
"You are a 10-year experienced anesthesiologist. According to ESC 2024 guidelines, evaluate whether this patient requires cardiology consultation."
Non-prompted format:
"Evaluate whether this patient requires cardiology consultation."
AI recommendations will not influence clinical decision-making.
Outcome Measures
Primary and secondary analyses will include:
Agreement between AI recommendations and expert anesthesiologist evaluations
Readability of AI-generated responses
Quality assessment of responses
Classification performance comparisons across models
Statistical Analysis
Statistical analyses will be performed using appropriate comparative and agreement tests. Readability and quality scores will be analyzed using non-parametric methods where applicable. ROC analysis will be used to assess classification ability. A significance level of p < 0.05 will be applied.
Study Objective
The objective of this study is to explore the feasibility of AI-assisted decision support systems in predicting cardiology consultation requirements and to evaluate whether prompt engineering influences AI performance.
Study design
- Observational model
- Cohort
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- This study will be conducted with the participation of patients who will be referred to cardiology following an examination at the anesthesia clinic prior to undergoing non-cardiac surgery.
Key inclusion criteria (6 listed)
- Adults aged 18 years or older
- ASA physical status classification I-IV
- Scheduled for non-cardiac surgery
- Patients evaluated preoperatively by anesthesia residents with less than two years of clinical experience
- Availability of complete clinical data including medical history, ECG findings, and chest radiography
Key exclusion criteria (6 listed)
- Patients younger than 18 years of age
- Patients undergoing cardiac surgery
- Patients with incomplete clinical data
- Patients who declined participation
- Emergency surgery cases
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Patient scenarios were presented to different AI models (ChatGPT 4.5, ChatGPT 5, Copilot, Deepseek, Grok, Claude, Gemini Flash, Gemini Pro) with and without prompts.Responses: Compared with expert opinion according to the ESC 2024 guidelines Evaluated using the Ateşman readability score and the Global Quality Scale (GQS) | Other | – |
Primary outcome
Agreement Between AI Model Recommendations and Expert Anesthesiologist Decision Regarding Cardiology Consultation Requirement
Time frame: At baseline preoperative evaluation (Day 1)
The level of agreement between artificial intelligence model recommendations and expert anesthesiologist evaluations for cardiology consultation necessity will be assessed using Cohen's Kappa coefficient based on ESC 2024 guidelines.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 9 Feb 2026.
- Background1.M. Graeßner et al., "Enabling personalized perioperative risk prediction by using a machinelearning model based on preoperative data," Scientific Reports, vol. 13, no. 1, May 2023, doi: 10.1038/s41598-023-33981-8. 2.B. Choi et al., "Prediction Model for 30-Day Mortality after Non-Cardiac Surgery Using MachineLearning Techniques Based on Preoperative Evaluation of Electronic Medical Records," Journal of Clinical Medicine, vol. 11, no. 21, p. 6487, Nov. 2022, doi: 10.3390/jcm11216487. 3.M. Vine et al., "Innovative approaches to preoperative care including feasibility, efficacy, and ethical implications: a narrative review," AME Surgical Journal, vol. 4. AME Publishing Company, p. 1, Feb. 01, 2024. doi: 10.21037/asj-23-41. 4.P. Chung, C. T. Fong, A. M. Walters, N. Aghaeepour, M. Yetişgen, and V. N. O'Reilly-Shah, "Large Language Model Capabilities in Perioperative Risk Prediction and Prognostication," JAMA Surgery, vol. 159, no. 8, American Medical Association, p. 928, Jun. 05, 2024. doi: 10.1001/jamasurg.2024.1621 5.T. Yurttas, R. Hidvegi, and M. Filipovic, "Biomarker-Based Preoperative Risk Stratification for Patients Undergoing Non-Cardiac Surgery," Journal of Clinical Medicine, vol. 9, no. 2, p. 351, Jan. 2020, doi: 10.3390/jcm9020351 6.J. Stones and D. Yates, "Clinical risk assessment tools in anaesthesia," BJA Education, vol. 19, no. 2. Elsevier BV, p. 47, Dec. 15, 2018. doi: 10.1016/j.bjae.2018.09.009. 7. Engineering as an Important Emerging Skill for Medical Professionals: Tutorial. J Med Internet Res. 2023 Oct 4;25:e50638. doi: 10.2196/50638. PMID: 37792434; PMCID: PMC10585440. 8. ATEŞMAN, Ender. (1997). Türkçe'de okunabilirliğin Ölçülmesi. A.Ü. Tömer Dil Dergisi, sayı:58,s.171174. 9. Coskun B, Ocakoglu G, Yetemen M, Kaygisiz O. Can ChatGPT, an Artificial Intelligence Language Model, Provide Accurate and High-quality Patient Information on Prostate Cancer? Urology. 2023 Oct;180:35-58. doi: 10.1016/j.urology.2023.05.040. Epub 2023 Jul 4. PMID: 37406
- Individual participant dataSharing planned: No. Due to our commitment to patient rights, data privacy, and the consent form
Related trials for USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
eralp çevikkalp principal investigator
bursa şehir hastanesi
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of USE OF ARTIFICIAL INTELLIGENCE IN ANESTHESIA and PREOPERATIVE CARDIOLOGY CONSULTATION REQUIREMENT, run by Bursa City Hospital at 1 site in Turkey (Türkiye).
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 6 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 9 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Turkey (Türkiye). Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between AI Model Recommendations and Expert Anesthesiologist Decision Regarding Cardiology Consultation Requirement (time frame: At baseline preoperative evaluation (Day 1)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07395713, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 9 Feb 2026.