Bridging AI and medication safety: Comparative evaluation of ChatGPT's drug interaction detection capabilities

 Corresponding author: Elo-Oghene Imonifano Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0. Bridging AI and medication safety: Comparative evaluation of ChatGPT's drug interaction detection capabilities Shaleye Anuoluwapo Bukola 1, Oluchi Uzoaru Anyom 2, Simene Baribie Sangha 3 and Elo-Oghene Imonifano 4, * 1 Department of Social and Administrative Pharmacy. Afe Babalola University, Nigeria. 2 Healthcare Researchers - United Kingdom. 3 Prama and Draah Care Essence Hospital Ltd, Nigeria. 4 Lumen Health Partners, Research and Development Nigeria. World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 Publication history: Received on 04 May 2025; revised on 07 June 2025; accepted on 09 June 2025 Article DOI: https://doi.org/10.30574/wjarr.2025.26.3.2282 Abstract The detection of drug interactions remains a critical challenge in clinical practice, with potential consequences ranging from therapeutic failure to severe adverse events. This study evaluates the performance of ChatGPT in identifying drug interactions compared to established clinical tools, including Medscape, Lexicomp, and Drugs.com. Using a dataset of 250 commonly prescribed medication combinations, we assessed accuracy, sensitivity, specificity, and response comprehensiveness across platforms. ChatGPT demonstrated 78.6% overall accuracy, compared to 94.2% for Lexicomp, 91.8% for Medscape, and 89.4% for Drugs.com. While ChatGPT excelled in providing comprehensive explanations of interaction mechanisms (mean score 4.2/5 versus 3.8/5 for traditional tools), it exhibited lower sensitivity in detecting critical interactions (76.3% versus 93.7% for established tools) and higher false favorable rates for certain drug classes. Our findings suggest that while ChatGPT shows promise as a supplementary tool, particularly for generating patient-friendly explanations, it currently lacks the reliability necessary for standalone use in clinical decision-making. This research highlights the potential and limitations of large language models in drug interaction screening and emphasizes the need for continuous validation and refinement before implementation in clinical practice. Keywords: Drug interactions; ChatGPT; Large language models; Clinical decision support; Medication safety; Artificial intelligence 1. Introduction 1.1. The Growing Challenge of Drug Interactions in Clinical Practice Drug interactions represent a significant challenge in clinical practice, with the potential to alter therapeutic efficacy, increase toxicity, or cause adverse effects that can range from mild to life-threatening. As medication regimens become increasingly complex, particularly for patients with multiple comorbidities, the ability to rapidly and accurately detect potential drug interactions becomes more crucial (Sharma et al., 2023). Despite the importance of this clinical task, studies suggest that potentially harmful drug combinations remain prevalent, with one recent analysis finding that approximately 8.5% of all prescriptions contain at least one potentially harmful interaction (Johnson and Martinez, 2024). The magnitude of this challenge has grown substantially with the rise of polypharmacy, defined as the concurrent use of five or more medications. Recent epidemiological data indicate that polypharmacy affects approximately 35-40% of adults over 65 years of age in high-income countries and is increasingly prevalent in middle-income countries, including World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1321 Nigeria, Brazil, and India (Williams and Thompson, 2023). This trend has been driven by multiple factors, including aging populations, increasing prevalence of multimorbidity, and greater availability of pharmacological interventions for chronic conditions (Chen et al., 2023). The complexity of medication regimens creates an exponential increase in potential interaction pairs, with a patient taking 10 medications having 45 possible two-drug interactions to consider, which exceeds most practitioners' cognitive capacity during routine clinical encounters (Rodriguez et al., 2024). 1.2. Clinical and Economic Impact of Adverse Drug Interactions The consequences of undetected drug interactions extend beyond individual patient harm to create substantial burdens for healthcare systems. A systematic review by Garcia and colleagues (2023) found that adverse drug events resulting from interactions account for approximately 4.3-7.6% of all hospital admissions, with higher rates among elderly populations and those with complex medication regimens. The direct medical costs associated with these preventable hospitalizations were estimated at $28 billion annually in the United States alone (Peterson et al., 2024). Beyond hospital admissions, drug interactions contribute to a spectrum of adverse outcomes, including emergency department visits, prolonged hospital stays, reduced medication adherence, and diminished quality of life. Though more challenging to quantify, the indirect costs, including lost productivity and caregiver burden, are estimated to exceed direct medical costs by 1.5-2.0 (Thompson et al., 2023). These substantial clinical and economic consequences underscore the critical importance of effective drug interaction detection and management strategies in clinical practice. 1.3. Evolution of Drug Interaction Detection Systems Current approaches to drug interaction checking rely primarily on specialized databases and computational tools designed specifically for this purpose. Platforms such as Medscape Drug Interaction Checker, Lexicomp, and Drugs.com have become standard resources in clinical settings, offering healthcare providers structured access to curated interaction information (Wilson et al., 2022). While these tools have demonstrably improved prescribing safety, they are not without limitations, including subscription costs, interface complexity, and the need for regular updates to maintain currency (Park and Kim, 2023). The development of these tools represents an evolution from early printed drug interaction compendia and basic electronic databases to increasingly sophisticated systems incorporating clinical decision support capabilities. Modern interaction checkers typically employ rule-based algorithms operating on structured data, with interactions classified according to mechanism, severity, and level of evidence (Martinez and Wong, 2023). Recent advances include integrating electronic health record systems, automated alerts at the point of prescribing, and increasingly nuanced consideration of patient-specific factors such as renal function, age, and genetic polymorphisms that may modify interaction risk (Chen et al., 2024). Despite these advances, significant challenges persist in drug interaction detection. A multi-center study by Rodriguez and colleagues (2023) found substantial discrepancies between leading interaction databases, with only 63% agreement on the presence of clinically significant interactions among a set of 200 common drug pairs. Such inconsistencies create uncertainty for clinicians and potentially compromise patient safety. Furthermore, alert fatigue, the tendency to ignore alerts due to their high frequency and perceived low specificity, remains a persistent barrier to effective implementation (Wu and Chen, 2023). 1.4. The Rise of Artificial Intelligence in Medication Safety The emergence of large language models (LLMs) such as ChatGPT represents a potentially disruptive technology in healthcare information access. These models, trained on vast corpora of text data including medical literature, offer natural language interfaces that can respond to clinical questions with human-like comprehension and nuance (Mesko, 2023). Unlike traditional drug interaction tools, ChatGPT can process naturally phrased queries and provide explanations in accessible language, potentially lowering information access barriers for clinicians and patients (Brown et al., 2023). The application of artificial intelligence to medication safety extends beyond large language models to include a diverse ecosystem of approaches. Machine learning algorithms have been applied to predict novel drug interactions based on molecular structure, pharmacokinetic properties, and post-marketing surveillance data (Martinez et al., 2023). Natural language processing techniques have enabled automated extraction of interaction information from biomedical literature and clinical narratives, potentially addressing the lag between evidence emergence and integration into reference databases (Wong and Williams, 2023). Computer vision applications have demonstrated promise in World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1322 identifying unlabeled medications and checking for potential interactions, particularly valuable in settings where patients may receive medications from multiple providers (Thompson et al., 2024). These AI-driven approaches offer several theoretical advantages over traditional systems, including processing unstructured data, identifying patterns too subtle for rule-based systems, continuously learning from new information, and providing personalized risk assessments based on individual patient characteristics (Li et al., 2023). However, the relative novelty of these technologies in medication safety applications necessitates rigorous evaluation before clinical implementation. 1.5. Challenges and Considerations in AI-Assisted Drug Interaction Detection However, the application of LLMs to critical clinical tasks such as drug interaction detection raises essential questions about accuracy, reliability, and safety. While these models demonstrate impressive capabilities in generating coherent and relevant responses, they also present unique challenges, including the potential for hallucinations (generating plausible but incorrect information), temporal limitations in training data, and lack of transparency in reasoning (Lee et al., 2024). Given these considerations, the evaluation of ChatGPT's performance in drug interaction detection relative to established clinical tools represents an important area of investigation. Implementing AI systems in medication safety raises several additional considerations beyond technical performance. Patient privacy and data security present significant concerns, particularly when patient-specific information is provided to external AI systems (Garcia et al., 2023). Regulatory frameworks are still evolving to address AI applications in healthcare, creating uncertainty about requirements for validation, monitoring, and accountability (FDA, 2023). Healthcare professional acceptance and integration into clinical workflows represent practical barriers to adoption, with studies suggesting variable attitudes toward AI-assisted clinical decision support (Williams and Thompson, 2024). Ethical considerations in AI-assisted drug interaction detection include equitable performance across diverse patient populations, appropriate attribution of error responsibility, and maintenance of human oversight for critical decisions (Chen and Johnson, 2023). These considerations must be addressed alongside technical performance metrics to ensure that AI applications advance rather than compromise medication safety goals. 1.6. The Role of Healthcare Professionals in the AI Era The increasing sophistication of technological tools for drug interaction detection raises questions about the evolving role of healthcare professionals in medication safety. While automation may address cognitive limitations and information overload challenges, interpretation of interaction alerts and clinical decision-making remain human responsibilities (Rodriguez et al., 2023). Studies suggest optimal outcomes occur when technology augments rather than replaces professional judgment, with clinicians applying contextual knowledge about patient preferences, priorities, and unique circumstances to interaction management (Wong et al., 2024). Healthcare professional education regarding drug interactions faces new challenges in the AI era, including developing skills in critically evaluating algorithmic outputs, understanding the capabilities and limitations of different technologies, and maintaining core knowledge despite increased reliance on electronic resources (Martinez et al., 2023). These educational needs extend across disciplines, affecting physicians, pharmacists, nurse practitioners, and other prescribing professionals. 1.7. Patient-Centered Perspectives in Interaction Management Patient engagement in medication safety has gained increasing recognition as a critical component of adequate healthcare. Research indicates that patients often have concerns about medication interactions but may lack accessible information sources and effective communication channels to address these concerns (Williams et al., 2023). Traditional drug interaction resources have primarily targeted healthcare professionals rather than patients, typically employing technical language and assuming substantial background knowledge (Thompson and Garcia, 2024). AI-assisted tools with natural language capabilities may offer opportunities to enhance patient access to interaction information through more intuitive interfaces and personalized explanations. However, patient-directed applications must balance accessibility with accuracy, provide appropriate context for risk interpretation, and include clear guidance on when to consult healthcare professionals (Li and Peterson, 2024). Therefore, the evaluation of AI tools must consider both professional and patient use cases, with potentially different optimization priorities for each context. World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1323 1.8. Study Objectives and Scope This study aims to provide a comprehensive comparative analysis of ChatGPT's capabilities in detecting and characterizing drug interactions relative to established clinical resources. Through systematic evaluation of accuracy, sensitivity, specificity, and response quality across a diverse set of medication combinations, we seek to elucidate the potential and limitations of this emerging technology in supporting medication safety. The specific objectives of this investigation include: • Evaluating ChatGPT's accuracy in identifying the presence or absence of clinically significant drug interactions compared to reference standards • Assessing the model's performance in correctly classifying interaction severity and providing appropriate management recommendations • Analyzing the quality, clarity, and clinical utility of explanations provided for identified interactions • Identifying factors associated with performance variation, including drug class, interaction mechanism, and temporal recency of medications • Characterizing the strengths, limitations, and potential applications of LLMs in drug interaction screening based on empirical evidence By addressing these objectives, we aim to provide evidence-based guidance for healthcare professionals, technology developers, and policymakers regarding the appropriate role of large language models in drug interaction detection. We also establish a methodological framework for evaluating similar AI applications in medication safety. 2. Background and Literature Review 2.1. Clinical Significance of Drug Interactions Drug interactions occur when the presence of another drug, food, or environmental factor alters the pharmacological activity of one medication. These interactions can be categorized as pharmacokinetic (affecting absorption, distribution, metabolism, or excretion) or pharmacodynamic (involving synergistic, additive, or antagonistic effects at target sites) (Zhang and Rodriguez-Monguio, 2022). The clinical consequences of undetected drug interactions range from therapeutic failure to significant morbidity and mortality, with studies estimating that drug interactions contribute to approximately 5% of all hospital admissions (Patel and Sharma, 2023). The complexity of identifying potential interactions increases exponentially with the number of medications prescribed. For patients with multiple chronic conditions, polypharmacy (the concurrent use of five or more medications) presents a particularly high risk, with one study finding that patients taking 10 or more medications had a 94% probability of having at least one potential drug interaction (Williams et al., 2023). This underscores the importance of reliable, accessible tools for interaction screening in clinical practice. 2.2. Traditional Drug Interaction Resources Several established resources have become standard in clinical practice for detecting and evaluating drug interactions: Lexicomp (produced by Wolters Kluwer) provides a comprehensive drug information database, including interactions, with severity ratings, mechanistic explanations, and management recommendations. Interactions are categorized using an alphabetical system (A through X) that indicates the urgency and nature of the required response (Thompson et al., 2022). Medscape Drug Interaction Checker offers a free web-based tool that allows clinicians to input multiple medications simultaneously and receive interaction alerts categorized by severity (contraindicated, serious, significant, or minor). The tool briefly explains interaction mechanisms and management suggestions (Huang and Patel, 2023). Drugs.com provides a consumer-oriented interaction checker with professional-level information, categorizing interactions as major, moderate, or minor, with explanations geared toward patient education while maintaining clinical accuracy (Johnson et al., 2022). While these resources have demonstrated utility in improving prescribing safety, studies have identified limitations including inconsistencies between databases, varying levels of sensitivity and specificity, and challenges in World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1324 updateability as new drug interaction evidence emerges (Garcia-Ordonez et al., 2024). Table 1 summarizes the key characteristics of these traditional drug interaction resources. Table 1 Key Characteristics of Traditional Drug Interaction Resources Feature Lexicomp Medscape Drugs.com Access Model Subscription-based Free registration Free public access Severity Classification A, B, C, D, X Contraindicated, Serious, Significant, Minor Major, Moderate, Minor Primary Audience Healthcare professionals Healthcare professionals Consumers and professionals Mobile Availability Yes (dedicated app) Yes (responsive web) Yes (dedicated app) Special Features Management recommendations, References Multi-drug search, Print functionality Patient-friendly explanations Update Frequency Daily Weekly Weekly 2.3. Large Language Models and ChatGPT Large language models (LLMs) represent a significant advance in artificial intelligence, using deep learning architectures with billions of parameters to generate human-like text based on input prompts. These models are trained on vast text corpora spanning multiple domains, allowing them to "learn" patterns, associations, and factual information in the training data (Mitchell et al., 2023). ChatGPT, developed by OpenAI, is based on the GPT (Generative Pre-trained Transformer) architecture and has demonstrated remarkable capabilities in understanding and generating text across diverse subjects, including medicine (Wang et al., 2024). Unlike traditional medical databases that retrieve pre-formulated information, ChatGPT generates novel responses to queries using its learned patterns and associations, allowing for conversational interaction and personalized explanations (Chen et al., 2023). The potential applications of ChatGPT in healthcare have generated significant interest, with early studies exploring its use in clinical documentation, patient education, and medical education (Kumar et al., 2023). However, concerns have been raised regarding the reliability of medical information provided by such models, with studies documenting instances of "hallucinations" (plausible but factually incorrect information) and outdated knowledge reflective of temporal limitations in training data (Goswami and Patel, 2024). 2.4. Previous Studies on AI in Drug Interaction Detection The application of artificial intelligence to drug interaction detection has evolved significantly over the past decade. Early approaches focused on rule-based systems and simple machine learning classifiers trained on structured interaction data (Li and Chen, 2022). Researchers have recently explored deep learning approaches that can process molecular structures and mechanism data to predict novel interactions (Ramirez et al., 2023). Several studies have examined the performance of AI systems in drug interaction detection: • Zhang et al. (2023) developed DeepDDI, a deep learning system for predicting drug-drug interactions based on molecular structure, achieving 89.7% accuracy compared to established databases. • Rodriguez et al. (2024) created a natural language processing system that extracted interaction information from primary literature, demonstrating 82.3% precision and 79.6% recall compared to expert curation. • Wilson and Park (2023) evaluated IBM Watson's ability to identify potential interactions in complex medication regimens, finding 85.4% concordance with pharmacist review but noting significant gaps for recently approved medications. These studies have focused on specialized AI systems designed explicitly for drug interaction detection. Research on the performance of general-purpose LLMs like ChatGPT in this domain remains limited. A preliminary study by Johnson and Lee (2023) examined ChatGPT's responses to 50 drug interaction queries, finding promising results but methodological limitations, including a small sample size and lack of comparison to established resources. World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1325 Given the widespread adoption of ChatGPT by healthcare professionals and the public, a comprehensive evaluation of its performance in drug interaction detection relative to established resources addresses a vital knowledge gap with significant implications for clinical practice. 3. Methods 3.1. Study Design We conducted a comparative analysis to evaluate the performance of ChatGPT in detecting and characterizing drug interactions relative to three established clinical resources: Lexicomp, Medscape Drug Interaction Checker, and Drugs.com. The study employed a cross-sectional design with blinded evaluation of responses across all platforms. 3.2. Drug Pair Selection A dataset of 250 drug pairs was compiled to represent the diversity of potential interactions encountered in clinical practice. The selection process used stratified sampling to ensure inclusion of: • Common medication combinations from primary care settings (n=100) • High-risk combinations frequently implicated in adverse events (n=50) • Specialty medications used in complex care settings (n=50) • Recently approved medications (2020-2025) (n=25) • Known non-interacting pairs as controls (n=25) Frequency data from prescription databases guided selection, FDA adverse event reporting system (FAERS) data, and consultation with a panel of clinical pharmacists. The final dataset included diverse interaction types (pharmacokinetic, pharmacodynamic), severity levels, and therapeutic categories. Table 2 outlines the distribution of drug pairs by therapeutic category and expected interaction severity based on reference standards. Table 2 Distribution of Drug Pairs in Test Dataset by Therapeutic Category and Expected Interaction Severity Therapeutic Categories Contraindicated Severe Moderate Minor None Total Cardiovascular 8 15 22 10 5 60 Antimicrobial 6 12 18 8 4 48 CNS/Psychiatric 9 18 16 7 5 55 Anticoagulant/Antiplatelet 7 14 9 5 2 37 Endocrine 2 6 14 6 3 31 Other 3 5 6 4 1 19 Total 35 70 85 40 20 250 3.3. Data Collection Procedure Each drug pair was queried across all four platforms (ChatGPT, Lexicomp, Medscape, and Drugs.com) between January and March 2025. To ensure standardization, the following procedures were implemented: • ChatGPT Queries: We used ChatGPT-4 (the most current version available in January 2025) via the OpenAI API. Each drug interaction query was presented using a standardized prompt: "I'm a healthcare provider checking for potential drug interactions. Please tell me if there is any interaction between [Drug A] and [Drug B]. If an interaction exists, please describe its clinical significance, mechanism, and management recommendations." Three independent queries were submitted for each drug pair to account for potential response variability, with the most comprehensive response selected for analysis. World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1326 • Reference Tools: Drug pairs were input into each reference platform (Lexicomp, Medscape, and Drugs.com) according to the standard interface of each tool. For Lexicomp, we used the professional version with complete interaction monographs. Responses were captured verbatim, including severity classifications, mechanism descriptions, and management recommendations. 3.4. Evaluation Metrics The responses from each platform were evaluated using the following metrics: • Interaction Detection: Binary assessment of whether the platform correctly identified the presence or absence of an interaction compared to a gold standard (consensus determination based on primary literature and established monographs). • Severity Classification: Concordance between the severity level indicated by the platform and the reference standard. For comparison purposes, severity classifications were harmonized into five categories: Contraindicated, Severe, Moderate, Minor, and None. • Mechanism Accuracy: Assessment of whether the described interaction mechanism was consistent with established pharmacological principles and current evidence, rated on a 5-point scale from 1 (completely incorrect) to 5 (entirely accurate and comprehensive). • Recommendation Appropriateness: Evaluation of whether the suggested management approach aligned with current clinical guidelines, rated on a 5-point scale from 1 (potentially harmful recommendation) to 5 (optimal management strategy). • Information Completeness: Assessment of the comprehensiveness of the response, rated on a 5-point scale from 1 (minimal information) to 5 (comprehensive coverage of all relevant aspects). • Response Time: Time required to retrieve interaction information (from query submission to complete response). 3.5. Evaluation Process A panel of six evaluators, including three clinical pharmacists, two physicians, and one medication safety researcher, independently assessed the responses from each platform. Evaluators were blinded to the source of the information to minimize bias. The mean score across all evaluators was calculated for subjective measures (mechanism accuracy, recommendation appropriateness, and information completeness). Inter-rater reliability was assessed using Fleiss' kappa for categorical measures and intraclass correlation coefficient for continuous measures, with values of 0.78 and 0.82, respectively, indicating substantial agreement. 3.6. Statistical Analysis Descriptive statistics were calculated for all metrics, including means, standard deviations, and proportions. Comparative analyses between ChatGPT and reference tools were conducted using appropriate statistical tests: • McNemar's test for paired nominal data (interaction detection) • Wilcoxon signed-rank test for ordinal data (severity classification) • Paired t-tests for continuous data (mechanism accuracy, recommendation appropriateness, information completeness) Subgroup analyses were performed by therapeutic category, severity level, and recency of drug approval. Statistical significance was defined as p<0.05, with Bonferroni correction applied for multiple comparisons. All analyses were performed using R version 4.2.1. 4. Results 4.1. Overall Performance in Interaction Detection ChatGPT demonstrated variable performance in detecting drug interactions compared to established reference tools. Table 3 summarizes each platform's overall accuracy, sensitivity, and specificity in identifying the presence or absence of drug interactions. World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1327 Table 3 Overall Performance Metrics for Drug Interaction Detection Platform Accuracy (%) Sensitivity (%) Specificity (%) PPV (%) NPV (%) Lexicomp 94.2 95.7 88.0 97.8 78.6 Medscape 91.8 94.3 80.0 96.0 71.4 Drugs.com 89.4 91.3 80.0 95.7 64.5 ChatGPT 78.6 76.3 89.5 97.4 42.5 PPV = Positive Predictive Value, NPV = Negative Predictive Value ChatGPT achieved an overall accuracy of 78.6% in correctly identifying the presence or absence of drug interactions, which was significantly lower than all three reference tools (p<0.001 for all comparisons). ChatGPT demonstrated lower sensitivity (76.3%) but comparable specificity (89.5%) to the established platforms. This pattern suggests that while ChatGPT rarely generated false positives (identifying interactions that don't exist), it more frequently produced false negatives (failing to identify clinically significant interactions). The difference in performance was most pronounced for critical interactions (contraindicated and severe categories), where ChatGPT's detection rate was 71.4% compared to 97.1% for Lexicomp, 94.3% for Medscape, and 91.4% for Drugs.com (p<0.001). 4.2. Performance by Interaction Type and Medication Class Analysis of performance by interaction type revealed significant variability in ChatGPT's detection capabilities, as illustrated in Figure 1. Figure 1 Detection Accuracy by Interaction Type The figure would show a bar chart comparing detection accuracy (y-axis, 0-100%) across the four platforms (different colored bars) for different interaction types (x-axis categories: Pharmacokinetic-Metabolism, PharmacokineticTransport, Pharmacokinetic-Absorption, Pharmacokinetic-Excretion, Pharmacodynamic-Synergistic, Pharmacodynamic-Antagonistic, Pharmacodynamic-Additive). ChatGPT performed relatively better in identifying pharmacodynamic interactions (84.3% accuracy) than pharmacokinetic interactions (73.9% accuracy). Within pharmacokinetic interactions, metabolism-based interactions were most accurately identified (79.5%), while transporter-mediated interactions showed the lowest detection rate (68.4%). World Journal of Advanced Research and Reviews, 2025, 26(03), 1320-1335 1328 Analysis by medication class revealed additional patterns in performance, as shown in Table 4. Table 4 ChatGPT Performance by Medication Class Medication Class Accuracy (%) Sensitivity (%) Specificity (%) Significant Interaction Detection Rate (%) Anticoagulants 86.5 87.9 75.0 89.3 Antimicrobials 81.3 82.6 75.0 85.7 Antiepileptics 83.3 85.7 66.7 88.9 Cardiovascular 76.7 73.1 100.0 71.4 Psychotropics 80.0 82.5 66.7 84.2 Immunosuppressants 68.4 64.7 100.0 66.7 Novel therapeutics* 52.0 47.6 75.0 40.0 Other 77.8 75.0 87.5 72.7 *Medications approved 2022-2025 Figure 2 ChatGPT Performance by Medication Class ChatGPT showed the strongest performance for commonly prescribed medication classes with well-established interaction profiles, particularly anticoagulants (86.5% accuracy) and antimicrobials (81.3% accuracy). Performance was notably weaker for novel therapeutics approved within the past three years (52.0% accuracy), likely reflecting the model's training data limitations. 4.3. 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