Challenges and Facilitation Approaches for the Participatory Design of AI-Based Clinical Decision Support Systems: Protocol for a Scoping Review.

BACKGROUND: In the last few years, there has been an increasing interest in the development of artificial intelligence (AI)-based clinical decision support systems (CDSS). However, there are barriers to the successful implementation of such systems in practice, including the lack of acceptance of these systems. Participatory approaches aim to involve future users in designing applications such as CDSS to be more acceptable, feasible, and fundamentally more relevant for practice. The development of technologies based on AI, however, challenges the process of user involvement and related methods. OBJECTIVE: The aim of this review is to summarize and present the main approaches, methods, practices, and specific challenges for participatory research and development of AI-based decision support systems involving clinicians. METHODS: This scoping review will follow the Joanna Briggs Institute approach to scoping reviews. The search for eligible studies was conducted in the databases MEDLINE via PubMed; ACM Digital Library; Cumulative Index to Nursing and Allied Health; and PsycInfo. The following search filters, adapted to each database, were used: Period January 01, 2012, to October 31, 2023, English and German studies only, abstract available. The scoping review will include studies that involve the development, piloting, implementation, and evaluation of AI-based CDSS (hybrid and data-driven AI approaches). Clinical staff must be involved in a participatory manner. Data retrieval will be accompanied by a manual gray literature search. Potential publications will then be exported into reference management software, and duplicates will be removed. Afterward, the obtained set of papers will be transferred into a systematic review management tool. All publications will be screened, extracted, and analyzed: title and abstract screening will be carried out by 2 independent reviewers. Disagreements will be resolved by involving a third reviewer. Data will be extracted using a data extraction tool prepared for the study. RESULTS: This scoping review protocol was registered on March 11, 2023, at the Open Science Framework. The full-text screening had already started at that time. Of the 3,118 studies screened by title and abstract, 31 were included in the full-text screening. Data collection and analysis as well as manuscript preparation are planned for the second and third quarter of 2024. The manuscript should be submitted towards the end of 2024. CONCLUSIONS: This review will describe the current state of knowledge on participatory development of AI-based decision support systems. The aim is to identify knowledge gaps and provide research impetus. It also aims to provide relevant information for policy makers and practitioners. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/58185.

An Advanced Machine Learning Model for a Web-Based Artificial Intelligence-Based Clinical Decision Support System Application: Model Development and Validation Study.

BACKGROUND: Breast cancer is a leading global health concern, necessitating advancements in recurrence prediction and management. The development of an artificial intelligence (AI)-based clinical decision support system (AI-CDSS) using ChatGPT addresses this need with the aim of enhancing both prediction accuracy and user accessibility. OBJECTIVE: This study aims to develop and validate an advanced machine learning model for a web-based AI-CDSS application, leveraging the question-and-answer guidance capabilities of ChatGPT to enhance data preprocessing and model development, thereby improving the prediction of breast cancer recurrence. METHODS: This study focused on developing an advanced machine learning model by leveraging data from the Tri-Service General Hospital breast cancer registry of 3577 patients (2004-2016). As a tertiary medical center, it accepts referrals from four branches-3 branches in the northern region and 1 branch on an offshore island in our country-that manage chronic diseases but refer complex surgical cases, including breast cancer, to the main center, enriching our study population's diversity. Model training used patient data from 2004 to 2012, with subsequent validation using data from 2013 to 2016, ensuring comprehensive assessment and robustness of our predictive models. ChatGPT is integral to preprocessing and model development, aiding in hormone receptor categorization, age binning, and one-hot encoding. Techniques such as the synthetic minority oversampling technique address the imbalance of data sets. Various algorithms, including light gradient-boosting machine, gradient boosting, and extreme gradient boosting, were used, and their performance was evaluated using metrics such as the area under the curve, accuracy, sensitivity, and F1-score. RESULTS: The light gradient-boosting machine model demonstrated superior performance, with an area under the curve of 0.80, followed closely by the gradient boosting and extreme gradient boosting models. The web interface of the AI-CDSS tool was effectively tested in clinical decision-making scenarios, proving its use in personalized treatment planning and patient involvement. CONCLUSIONS: The AI-CDSS tool, enhanced by ChatGPT, marks a significant advancement in breast cancer recurrence prediction, offering a more individualized and accessible approach for clinicians and patients. Although promising, further validation in diverse clinical settings is recommended to confirm its efficacy and expand its use.

What Determines the Use of Clinical Decision Support Systems in Nursing? Results of a Multiple Regression Analysis.

INTRODUCTION: In nursing, professionals are expected to base their practice on evidence-based knowledge, however the successful implementation of this knowledge into nursing practice is not always assured. Clinical Decision Support Systems (CDSS) are considered to bridge this evidence-practice gap. METHODS: This study examines the extent to which evidence-based nursing (EBN) practices influence the use of CDSS and identifies what additional factors from acceptance theories such as UTAUT play a role. RESULTS AND DISCUSSION: Our findings from three regression models revealed that nursing professionals and nursing students who employ evidence-based practices are not more likely to use an evidence-based CDSS. The relationship between an EBN composite score (model 1) or is individual dimensions (model 2) and CDSS use was not significant. However, a more comprehensive model (model 3), incorporating items from the UTAUT such as Social Influences, Facilitating Conditions, Performance Expectancy, and Effort Expectancy, supplemented by Satisfaction demonstrated a significant variance explained (R2 = 0.279). Performance Expectancy and Satisfaction were found to be significantly associated with CDSS utilization. CONCLUSION: This underscores the importance of user-friendliness and practical utility of a CDSS. Despite potential limitations in generalizability and a limited sample size, the results provide insights into that CDSS first and foremost underly the same mechanisms of use as other health IT systems.

Evaluating Inflammatory Bowel Disease-Related Quality of Life Using an Interpretable Machine Learning Approach: A Multicenter Study in China.

Purpose: Impaired quality of life (QOL) is common in patients with inflammatory bowel disease (IBD). A tool to more quickly identify IBD patients at high risk of impaired QOL improves opportunities for earlier intervention and improves long-term prognosis. The purpose of this study was to use a machine learning (ML) approach to develop risk stratification models for evaluating IBD-related QOL impairments. Patients and Methods: An online questionnaire was used to collect clinical data on 2478 IBD patients from 42 hospitals distributed across 22 provinces in China from September 2021 to May 2022. Eight ML models used to predict the risk of IBD-related QOL impairments were developed and validated. Model performance was evaluated using a set of indexes and the best ML model was explained using a Local Interpretable Model-Agnostic Explanations (LIME) algorithm. Results: The support vector machine (SVM) classifier algorithm-based model outperformed other ML models with an area under the receiver operating characteristic curve (AUC) and an accuracy of 0.80 and 0.71, respectively. The feature importance calculated by the SVM classifier algorithm revealed that glucocorticoid use, anxiety, abdominal pain, sleep disorders, and more severe disease contributed to a higher risk of impaired QOL, while longer disease course and the use of biological agents and immunosuppressants were associated with a lower risk. Conclusion: An ML approach for assessing IBD-related QOL impairments is feasible and effective. This mechanism is a promising tool for gastroenterologists to identify IBD patients at high risk of impaired QOL.

Improving Naloxone Co-prescribing Through Clinical Decision Support.

BACKGROUND: Despite national guidelines recommending naloxone co-prescription with high-risk medications, rates remain low nationally. This was reflected at our institution with remarkably low naloxone prescribing rates. We sought to determine if a clinical decision support (CDS) tool could increase rates of naloxone co-prescribing with high-risk prescriptions. METHODS:  An alert in the electronic health record was triggered upon signing an order for a high-risk opioid medication without a naloxone co-prescription. We examined all opioid prescriptions written by family and general internal medicine practitioners at the University of Iowa Hospitals and Clinics in outpatient encounters between November 30, 2020, and February 28, 2022. Once triggered by a high-risk prescription, the CDS tool had the option to choose an order set with an automatically selected co-prescription for naloxone along with patient instructions automatically added to the patient's after-visit summary (AVS). We examined the monthly percentage of patients receiving Schedule II opioid prescriptions ≥90 morphine milliequivalents (MME)/day who received concurrent naloxone prescriptions in the 12 months before the CDS went live and the three months following go-live. RESULTS:  Concurrent naloxone prescriptions increased from 1.1% in the 12 months prior to implementation in November 2021 to 9.4% (p<0.001) during the post-intervention period across eight family medicine and internal medicine clinics. DISCUSSION:  This single-center quality improvement project with retrospective analysis demonstrates the potential efficacy of a single CDS tool in increasing the rate of naloxone prescription. The impact of such prescribing on overall mortality requires further research. CONCLUSIONS: The CDS tool was easy to implement and improved rates of appropriate naloxone co-prescribing.

A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives.

INTRODUCTION: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient's symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies. METHODS: Focusing on four key areas-medication dispensing, vital interventions, laboratory testing, and emergency radiology exams, the study employed Natural Language Processing (NLP) and seven advanced machine learning techniques. The research was centered around the innovative use of BioClinicalBERT, a state-of-the-art NLP framework. RESULTS: BioClinicalBERT emerged as the superior model, outperforming others in predictive accuracy. The integration of physiological data with patient narrative symptoms demonstrated greater effectiveness compared to models based solely on textual data. The robustness of our approach was confirmed by an Area Under the Receiver Operating Characteristic curve (AUROC) score of 0.9. CONCLUSION: The findings of our study underscore the feasibility of establishing a decision support system for emergency patients, targeting timely interventions and examinations based on a nuanced analysis of symptoms. By using an advanced natural language processing technique, our approach shows promise for enhancing diagnostic accuracy. However, the current model is not yet fully mature for direct implementation into daily clinical practice. Recognizing the imperative nature of precision in the ER environment, future research endeavors must focus on refining and expanding predictive models to include detailed timely examinations and interventions. Although the progress achieved in this study represents an encouraging step towards a more innovative and technology-driven paradigm in emergency care, full clinical integration warrants further exploration and validation.

Impacts of Clinical Decision Support Systems on the Relationship, Communication, and Shared Decision-Making Between Health Care Professionals and Patients: Multistakeholder Interview Study.

BACKGROUND: Clinical decision support systems (CDSSs) are increasingly being introduced into various domains of health care. Little is known so far about the impact of such systems on the health care professional-patient relationship, and there is a lack of agreement about whether and how patients should be informed about the use of CDSSs. OBJECTIVE: This study aims to explore, in an empirically informed manner, the potential implications for the health care professional-patient relationship and to underline the importance of this relationship when using CDSSs for both patients and future professionals. METHODS: Using a methodological triangulation, 15 medical students and 12 trainee nurses were interviewed in semistructured interviews and 18 patients were involved in focus groups between April 2021 and April 2022. All participants came from Germany. Three examples of CDSSs covering different areas of health care (ie, surgery, nephrology, and intensive home care) were used as stimuli in the study to identify similarities and differences regarding the use of CDSSs in different fields of application. The interview and focus group transcripts were analyzed using a structured qualitative content analysis. RESULTS: From the interviews and focus groups analyzed, three topics were identified that interdependently address the interactions between patients and health care professionals: (1) CDSSs and their impact on the roles of and requirements for health care professionals, (2) CDSSs and their impact on the relationship between health care professionals and patients (including communication requirements for shared decision-making), and (3) stakeholders' expectations for patient education and information about CDSSs and their use. CONCLUSIONS: The results indicate that using CDSSs could restructure established power and decision-making relationships between (future) health care professionals and patients. In addition, respondents expected that the use of CDSSs would involve more communication, so they anticipated an increased time commitment. The results shed new light on the existing discourse by demonstrating that the anticipated impact of CDSSs on the health care professional-patient relationship appears to stem less from the function of a CDSS and more from its integration in the relationship. Therefore, the anticipated effects on the relationship between health care professionals and patients could be specifically addressed in patient information about the use of CDSSs.

Vignette-based comparative analysis of ChatGPT and specialist treatment decisions for rheumatic patients: results of the Rheum2Guide study.

BACKGROUND: The complex nature of rheumatic diseases poses considerable challenges for clinicians when developing individualized treatment plans. Large language models (LLMs) such as ChatGPT could enable treatment decision support. OBJECTIVE: To compare treatment plans generated by ChatGPT-3.5 and GPT-4 to those of a clinical rheumatology board (RB). DESIGN/METHODS: Fictional patient vignettes were created and GPT-3.5, GPT-4, and the RB were queried to provide respective first- and second-line treatment plans with underlying justifications. Four rheumatologists from different centers, blinded to the origin of treatment plans, selected the overall preferred treatment concept and assessed treatment plans' safety, EULAR guideline adherence, medical adequacy, overall quality, justification of the treatment plans and their completeness as well as patient vignette difficulty using a 5-point Likert scale. RESULTS: 20 fictional vignettes covering various rheumatic diseases and varying difficulty levels were assembled and a total of 160 ratings were assessed. In 68.8% (110/160) of cases, raters preferred the RB's treatment plans over those generated by GPT-4 (16.3%; 26/160) and GPT-3.5 (15.0%; 24/160). GPT-4's plans were chosen more frequently for first-line treatments compared to GPT-3.5. No significant safety differences were observed between RB and GPT-4's first-line treatment plans. Rheumatologists' plans received significantly higher ratings in guideline adherence, medical appropriateness, completeness and overall quality. Ratings did not correlate with the vignette difficulty. LLM-generated plans were notably longer and more detailed. CONCLUSION: GPT-4 and GPT-3.5 generated safe, high-quality treatment plans for rheumatic diseases, demonstrating promise in clinical decision support. Future research should investigate detailed standardized prompts and the impact of LLM usage on clinical decisions.

Application of the Kaiser score on contrast-enhanced mammography in the differential diagnosis of breast lesions: comparison with breast magnetic resonance imaging.

Background: The Kaiser score (KS) as a clinical decision rule has been proven capable of enhancing the diagnostic efficiency for suspicious breast lesions and obviating unnecessary benign biopsies. However, the consistency of KS in contrast-enhanced mammography (CEM-KS) and KS on magnetic resonance imaging (MRI-KS) is still unclear. This study aimed to evaluate and compare the diagnostic efficacy and agreement of CEM-KS and MRI-KS for suspicious breast lesions. Methods: This retrospective study included 207 patients from April 2019 to June 2022. The radiologists assigned a diagnostic category to all lesions using the Breast Imaging Reporting and Data System (BI-RADS). Subsequently, they were asked to assign a final diagnostic category for each lesion according to the KS. The diagnostic performance was evaluated by the area under the receiver operating characteristic curve (AUC). The agreement in terms of the kinetic curve and the KS categories for CEM and MRI were evaluated via the Cohen kappa coefficient. Results: The AUC was higher for the CEM-KS category assignment than for the CEM-BI-RADS category assignment (0.856 vs. 0.776; P=0.047). The AUC was higher for MRI-KS than for MRI-BI-RADS (0.841 vs. 0.752; P =0.015). The AUC of CEM-KS was not significantly different from that of MRI-KS (0.856 vs. 0.841; P=0.538). The difference between the AUCs for CEM-BI-RADS and MRI-BI-RADS was not statistically significant (0.776 vs. 0.752; P=0.400). The kappa agreement for the characterization of suspicious breast lesions using CEM-KS and MRI-KS was 0.885. Conclusions: The KS substantially improved the diagnostic performance of suspicious breast lesions, not only in MRI but also in CEM. CEM-KS and MRI-KS showed similar diagnostic performance and almost perfect agreement for the characterization of suspicious breast lesions. Therefore, CEM holds promise as an alternative when breast MRI is not available or contraindicated.

Applying Machine Learning for Prescriptive Support: A Use Case with Unfractionated Heparin in Intensive Care Units.

Continuous unfractionated heparin is widely used in intensive care, yet its complex pharmacokinetic properties complicate the determination of appropriate doses. To address this challenge, we developed machine learning models to predict over- and under-dosing, based on anti-Xa results, using a monocentric retrospective dataset. The random forest model achieved a mean AUROC of 0.80 [0.77-0.83], while the XGB model reached a mean AUROC of 0.80 [0.76-0.83]. Feature importance was employed to enhance the interpretability of the model, a critical factor for clinician acceptance. After prospective validation, machine learning models such as those developed in this study could be implemented within a computerized physician order entry (CPOE) as a clinical decision support system (CDSS).

Active Learning Pipeline to Identify Candidate Terms for a CDSS Ontology.

Ontology is essential for achieving health information and information technology application interoperability in the biomedical fields and beyond. Traditionally, ontology construction is carried out manually by human domain experts (HDE). Here, we explore an active learning approach to automatically identify candidate terms from publications, with manual verification later as a part of a deep learning model training and learning process. We introduce the overall architecture of the active learning pipeline and present some preliminary results. This work is a critical and complementary component in addition to manually building the ontology, especially during the long-term maintenance stage.

User-Centered Development of Explanation User Interfaces for AI-Based CDSS: Lessons Learned from Early Phases.

This paper reports lessons learned during the early phases of the user-centered design process for an explanation user interface for an AI-based clinical decision support system for the intensive care unit. This paper focuses on identifying and verifying physicians' explanation needs in a multi-center, multi-country project. The explanation needs identified through context analysis and user requirements prioritization in an initial center differed from those identified through questionnaire responses from N= 9 physicians after a multi-center project workshop. These results highlight the caution that should be taken when eliciting explanation needs during the user-centered design process.

[Use of a pharmaceutical decision support system in the valuation of hospital stays: Evaluation through 3 examples in collaboration with the department of medical information]. / Utilisation d'un système d'aide à la décision pharmaceutique dans la valorisation des séjours hospitaliers : évaluation à travers 3 exemples en collaboration avec le département de l'information médicale.

Pharmacy decision support systems (PDSS) help clinical pharmacists to prevent and detect adverse drug events. The coding of hospital stays by the department of medical information (DMI) requires expertise, as it determines hospital revenues and the epidemiological data transmitted via the French national hospital database. The aim was to study the interest and feasibility of using a PDSS, in collaboration with the DMI, to help with the coding of hospital stays. Over 5 months, three rules were implemented in the PDSS to detect gout, Parkinson's disease and oro-pharyngeal candidiasis. The PDSS alerts were analyzed by a pharmacy resident and then forwarded to the DMI, who analyzed the stays to see whether or not the coding for the disease corresponding to the alert was present. The absence of coding was evaluated and tracked, along with the resulting change in severity and valuation. Three hundred and ninety-nine alerts from the PDSS were analyzed and sent to the DMI, representing 211 stays and 309 uniform hospital standardized discharge abstract (UHSDA) in the fields of medicine, surgery and obstetrics. Two hundred and eight (67.3%) UHSDA did not have the coding corresponding to the alert. For the majority of these UHSDAs, apart from diagnostic precision, there was no impact on the valuation of stays. For 4 UHSDAs, the addition of the diagnosis code led to an increase in the value of the stay and the severity of the homogeneous patient groups. The total revaluation corresponding to this modification was €5416. The use of PDSS has helped in the precision of diagnosis coding and the valuation of stays. This result must be weighed against the time invested in analyzing alerts and associated coding. An improvement in disease detection and data processing is needed to be feasible in practice, given the more than 227,600 RSS performed per year at our facility.

How Trueness of Clinical Decision Support Systems Based on Machine Learning Is Assessed?

The application of machine learning algorithms in clinical decision support systems (CDSS) holds great promise for advancing patient care, yet practical implementation faces significant evaluation challenges. Through a scoping review, we investigate the common definitions of ground truth to collect clinically relevant reference values, as well as the typical metrics and combinations employed for assessing trueness. Our analysis reveals that ground truth definition is mostly not in accordance with the standard ISO expectation and that used combination of metrics does not usually cover all aspects of CDSS trueness, particularly neglecting the negative class perspective.

Development of Rules and Algorithms for an Intelligent and Integrated Older Care Model.

To break through the current bottleneck in home-based older care globally, we developed an intelligent and integrated older care model (SMART model) to facilitate integrated care for home-dwelling older people. As a knowledge-based clinical decision support system, the SMART model relies on rules and algorithms to ensure its transparent and well-supported decision-making process with clear rationales. Therefore, we conducted a mixed study combining qualitative research, literature review of the latest literature and guidelines, and expert consultation. Following the intervention mapping framework and nursing process, we determined 138 care problems along with their diagnostic criteria and care goals. Building upon this, we curated 450 evidence-informed methods, each accompanied by at least one implementation approach. Two sets of IF-THEN rules and algorithms including diagnostic rules and method trigger rules were employed to trigger appropriate care problems and customized methods and implementation approaches.

Clinical Decision Support Systems for Operating Room Nurses: A Rapid Review.

This rapid review delves into Clinical Decision Support Systems (CDSS) for Operating Room Nurses (ORN). Analyzing three studies over 20 years, it highlights limited impact on ORN decision-making. The findings suggest that CDSS positively influence some aspects of care, ORN perceive them as supplementary rather than pivotal to their decision-making processes. Our review highlights the importance of understanding ORN' decision-making for customizing CDSS effectively.

Prospects for artificial intelligence-enhanced electrocardiogram as a unified screening tool for cardiac and non-cardiac conditions: an explorative study in emergency care.

Aims: Current deep learning algorithms for automatic ECG analysis have shown notable accuracy but are typically narrowly focused on singular diagnostic conditions. This exploratory study aims to investigate the capability of a single deep learning model to predict a diverse range of both cardiac and non-cardiac discharge diagnoses based on a single ECG collected in the emergency department. Methods and results: In this study, we assess the performance of a model trained to predict a broad spectrum of diagnoses. We find that the model can reliably predict 253 ICD codes (81 cardiac and 172 non-cardiac) in the sense of exceeding an AUROC score of 0.8 in a statistically significant manner. Conclusion: The model demonstrates proficiency in handling a wide array of cardiac and non-cardiac diagnostic scenarios, indicating its potential as a comprehensive screening tool for diverse medical encounters.

Development, validation, and usability evaluation of machine learning algorithms for predicting personalized red blood cell demand among thoracic surgery patients.

INTRODUCTION: Preparing appropriate red blood cells (RBCs) before surgery is crucial for improving both the efficacy of perioperative workflow and patient safety. In particular, thoracic surgery (TS) is a procedure that requires massive transfusion with high variability for each patient. Hence, the precise prediction of RBC requirements for individual patients is becoming increasingly important. This study aimed to 1) develop and validate a machine learning algorithm for personalized RBC predictions for TS patients and 2) assess the usability of a clinical decision support system (CDSS) integrating this artificial intelligence model. METHODS: Adult patients who underwent TS between January 2016 and October 2021 were included in this study. Multiple models were developed by employing both traditional statistical- and machine-learning approaches. The primary outcome evaluated the model's performance in predicting RBC requirements through root mean square error and adjusted R2. Surgeons and informaticians determined the precision MSBOS-Thoracic Surgery (pMSBOS-TS) algorithm through a consensus process. The usability of the pMSBOS-TS was assessed using the System Usability Scale (SUS) survey with 60 clinicians. RESULTS: We identified 7,843 cases (6,200 for training and 1,643 for test sets) of TSs. Among the models with variable performance indices, the extreme gradient boosting model was selected as the pMSBOS-TS algorithm. The pMSBOS-TS model showed statistically significant lower root mean square error (mean: 3.203 and 95% confidence interval [CI]: 3.186-3.220) compared to the calculated Maximum Surgical Blood Ordering Schedule (MSBOS) and a higher adjusted R2 (mean: 0.399 and 95% CI: 0.395-0.403) compared to the calculated MSBOS, while requiring approximately 200 fewer packs for RBC preparation compared to the calculated MSBOS. The SUS score of the pMSBOS-TS CDSS was 72.5 points, indicating good acceptability. CONCLUSIONS: We successfully developed the pMSBOS-TS capable of predicting personalized RBC transfusion requirements for perioperative patients undergoing TS.

Optimizing Dietary Habits in Adolescents with Polycystic Ovary Syndrome: Personalized Mediterranean Diet Intervention via Clinical Decision Support System-A Randomized Controlled Trial.

The hypothesis of this randomized controlled trial was that a clinical decision support system (CDSS) would increase adherence to the Mediterranean diet (MD) among adolescent females with polycystic ovary syndrome (PCOS). The objective was to assess the impact of personalized MD plans delivered via a CDSS on nutritional status and psychological well-being. Forty adolescent females (15-17 years) with PCOS were randomly assigned to the MD group (n = 20) or the Control group (n = 20). The MD group received personalized MD plans every 15 days via a CDSS, while the Control group received general nutritional advice. Assessments were conducted at baseline and after 3 months. Results showed significantly increased MD adherence in the MD group compared to the Control group (p < 0.001). The MD group exhibited lower intakes of energy, total fat, saturated fat, and cholesterol, and higher intakes of monounsaturated fat and fiber (p < 0.05). Serum calcium and vitamin D status (p < 0.05), as well as anxiety (p < 0.05) were improved. In conclusion, tailored dietary interventions based on MD principles, delivered via a CDSS, effectively manage PCOS in adolescent females. These findings highlight the potential benefits of using technology to promote dietary adherence and improve health outcomes in this population. ClinicalTrials.gov registry: NCT06380010.