Artifical IntelligenceNCT07314125
ChatGPT-5 vs. CDSS for Drug-Drug Interactions in ICU
An observational study of Artifical Intelligence, run by Bursa Yuksek Ihtisas Training and Research Hospital at 1 site in Turkey (Türkiye).
Trial summary
Evaluating ChatGPT-5 for Detecting Potential Drug-Drug Interactions in Intensive Care: A Comparative Analysis with a Clinical Decision Support System Background: Polypharmacy is a frequent challenge in intensive care units (ICUs), where critically ill patients are exposed to multiple concurrent medications. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Artifical Intelligence
- How many people
- 101 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Sep 2025to 1 Oct 2025
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- None posted in the registry
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 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study population consists of adult patients (≥18 years) admitted to the intensive care unit (ICU) for at least 48 hours at a tertiary care hospital. Eligible patients received four or more concurrent medications during their ICU stay, and only those with complete clinical and medication records were included. Patients with incomplete drug or interaction data, those receiving experimental or unproven drugs, as well as pediatric or pregnant patients, were excluded
Who may be able to join
- Patients aged 18 years or older
- Admission to the intensive care unit (ICU) for at least 48 hours
- Receipt of five or more medications concurrently during ICU stay
- Availability of complete clinical data and medication lists
Who cannot join
- Cases with incomplete medication or interaction data
- Patients receiving experimental or unproven drugs
- Pediatric patients or those with pregnancy
Trial locations and contacts
1 site in 1 country.
Turkey (Türkiye)1 site
Bursa Yuksek Ihtisas Research and Education Hospital
Bursa, Bursa, 16235
About this study
Official title: Evaluating ChatGPT-5 for Detecting Potential Drug-Drug Interactions in Intensive Care: A Comparative Analysis With a Clinical Decision Support System
Evaluating ChatGPT-5 for Detecting Potential Drug-Drug Interactions in Intensive Care: A Comparative Analysis with a Clinical Decision Support System
Background:
Read more
Polypharmacy is a frequent challenge in intensive care units (ICUs), where critically ill patients are exposed to multiple concurrent medications. This situation significantly increases the risk of potential drug-drug interactions (pDDIs), which may contribute to adverse drug events, prolonged ICU stays, and higher morbidity and mortality rates. Ensuring timely and accurate detection of pDDIs is therefore a cornerstone of patient safety in critical care settings. Traditional rule-based clinical decision support systems (CDSSs), such as the UpToDate Drug Interaction Checker, provide standardized alerts but may have limitations in contextual interpretation and adaptability. Recently, large language models (LLMs), such as ChatGPT-4.0, have emerged as advanced tools with natural language processing capabilities, potentially offering a novel approach to medication safety.
Objective:
This study aims to compare the performance of ChatGPT-4.0 with the UpToDate Drug Interaction Checker in identifying, classifying, and interpreting potential drug-drug interactions within real ICU patient medication orders.
Methods:
A retrospective dataset of ICU patient orders will be systematically analyzed using both ChatGPT-4.0 and the UpToDate Drug Interaction Checker. Each potential interaction will be assessed for sensitivity, specificity, accuracy, and clinical relevance. Discrepancies between the two systems will be documented and evaluated by independent critical care experts. Statistical analysis will be performed to compare detection rates and the qualitative depth of interaction explanations provided by each tool.
Expected Outcomes:
The study is expected to determine whether ChatGPT-4.0, as an AI-based system, can enhance the detection of clinically meaningful drug-drug interactions compared to traditional CDSS. The results may inform future integration of generative AI into ICU clinical workflows and contribute to safer pharmacotherapy practices in critical care.
Conclusion:
By directly comparing a state-of-the-art LLM with a widely used rule-based system, this study seeks to highlight the strengths, weaknesses, and potential clinical implications of generative AI in the domain of drug safety.
- Conditions
- Artifical Intelligence
What is being measured
The primary outcome is the main question the study is designed to answer.
Sensitivity of pDDI Detection
Measured: September 1 2025 to october 1 2025
Accuracy of Drug-Drug Interaction Detection of chatgpt
Measured: from seprember 1 2025 to october 1 2025
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Jan 2026
- Study start1 Sep 2025
- Primary completion2 Sep 2025
- Study completion1 Oct 2025
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 Artifical IntelligenceStudies 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
- Other
- Time perspective
- Other
- Enrolment
- 101 participants (actual)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Bursa Yuksek Ihtisas Training and Research Hospital
- Sponsor type
- Other government
- Responsible party
- Principal investigator, Ilkay Ceylan, Bursa Yuksek Ihtisas Training and Research Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07314125
- Sponsor's protocol ID
- 2024-TBEK 2025/07-04
- Status last verified
- Dec 2025
- First submitted
- 10 Dec 2025
- Last updated
- 2 Jan 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: This is a point-prevalence study using ICU patient records. Due to ethical and privacy considerations, individual participant data will not be shared outside the study team. De-identified individual participant data underlying the study findings may be shared upon reasonable request.
Publications
Riera P, Sole N, Suarez JC, Lopez PA, Fonts N, Rodriguez-Farre N, Fernandez de Gamarra-Martinez E, Moran I. Drug-drug interactions in an intensive care unit and comparison of updates in two databases. Farm Hosp. 2022 Aug 25;46(5):290-295.
PubMed 36183229
Study at a glance
From the registry record, last updated by the sponsor on 2 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Evaluating ChatGPT-5 for Detecting Potential Drug-Drug Interactions in Intensive Care: A Comparative Analysis With a Clinical Decision Support System
- ClinicalTrials.gov ID
- NCT07314125
- Acronym / sponsor ID
- 2024-TBEK 2025/07-04
- Conditions
- Artifical Intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 101 participants (actual)
- Lead sponsor
- Bursa Yuksek Ihtisas Training and Research HospitalOther government
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 2 Sep 2025 (actual)
- Study completion
- 1 Oct 2025 (actual)
- First posted
- 2 Jan 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.
Evaluating ChatGPT-5 for Detecting Potential Drug-Drug Interactions in Intensive Care: A Comparative Analysis with a Clinical Decision Support System
Background:
Polypharmacy is a frequent challenge in intensive care units (ICUs), where critically ill patients are exposed to multiple concurrent medications. This situation significantly increases the risk of potential drug-drug interactions (pDDIs), which may contribute to adverse drug events, prolonged ICU stays, and higher morbidity and mortality rates. Ensuring timely and accurate detection of pDDIs is therefore a cornerstone of patient safety in critical care settings. Traditional rule-based clinical decision support systems (CDSSs), such as the UpToDate Drug Interaction Checker, provide standardized alerts but may have limitations in contextual interpretation and adaptability. Recently, large language models (LLMs), such as ChatGPT-4.0, have emerged as advanced tools with natural language processing capabilities, potentially offering a novel approach to medication safety.
Objective:
This study aims to compare the performance of ChatGPT-4.0 with the UpToDate Drug Interaction Checker in identifying, classifying, and interpreting potential drug-drug interactions within real ICU patient medication orders.
Methods:
A retrospective dataset of ICU patient orders will be systematically analyzed using both ChatGPT-4.0 and the UpToDate Drug Interaction Checker. Each potential interaction will be assessed for sensitivity, specificity, accuracy, and clinical relevance. Discrepancies between the two systems will be documented and evaluated by independent critical care experts. Statistical analysis will be performed to compare detection rates and the qualitative depth of interaction explanations provided by each tool.
Expected Outcomes:
The study is expected to determine whether ChatGPT-4.0, as an AI-based system, can enhance the detection of clinically meaningful drug-drug interactions compared to traditional CDSS. The results may inform future integration of generative AI into ICU clinical workflows and contribute to safer pharmacotherapy practices in critical care.
Conclusion:
By directly comparing a state-of-the-art LLM with a widely used rule-based system, this study seeks to highlight the strengths, weaknesses, and potential clinical implications of generative AI in the domain of drug safety.
Study design
- Observational model
- Other
- Time perspective
- Other
- Target follow-up
- 1 Day
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult patients (≥18 years) admitted to the intensive care unit (ICU) for at least 48 hours at a tertiary care hospital. Eligible patients received four or more concurrent medications during their ICU stay, and only those with complete clinical and medication records were included. Patients with incomplete drug or interaction data, those receiving experimental or unproven drugs, as well as pediatric or pregnant patients, were excluded
Key inclusion criteria (4 listed)
- Patients aged 18 years or older
- Admission to the intensive care unit (ICU) for at least 48 hours
- Receipt of five or more medications concurrently during ICU stay
- Availability of complete clinical data and medication lists
Key exclusion criteria (3 listed)
- Cases with incomplete medication or interaction data
- Patients receiving experimental or unproven drugs
- Pediatric patients or those with pregnancy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Sensitivity of pDDI Detection
Time frame: September 1 2025 to october 1 2025
Accuracy of Drug-Drug Interaction Detection of chatgpt
Time frame: from seprember 1 2025 to october 1 2025
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Jan 2026.
- Background: PubMed 36183229Riera P, Sole N, Suarez JC, Lopez PA, Fonts N, Rodriguez-Farre N, Fernandez de Gamarra-Martinez E, Moran I. Drug-drug interactions in an intensive care unit and comparison of updates in two databases. Farm Hosp. 2022 Aug 25;46(5):290-295.
- Individual participant dataSharing planned: No. This is a point-prevalence study using ICU patient records. Due to ethical and privacy considerations, individual participant data will not be shared outside the study team. De-identified individual participant data underlying the study findings may be shared upon reasonable request.
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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.
The record lists no contact. Use the official record to reach the sponsor.
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 Artifical Intelligence, run by Bursa Yuksek Ihtisas Training and Research 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 4 inclusion and 3 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: finished, as of the sponsor's last update on 2 Jan 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Sensitivity of pDDI Detection (time frame: September 1 2025 to october 1 2025). The study lists 2 primary outcomes in all. 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 NCT07314125, 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 2 Jan 2026.