INTRODUCTION:

Clinical decision support systems (CDSS) are a game-changer in contemporary healthcare because they use AI to improve doctors' ability to make informed decisions. CDSS powered by AI has superior skills in assessing patient data, recommending medication therapies, and enhancing diagnostic precision. These systems help doctors by recommending drugs based on evidence that are specific to each patient's profile; this improves treatment results while decreasing the likelihood of human mistake. With an emphasis on improved patient care and tailored medication recommendations, this article delves into the history and present state of AI-powered CDSS.

The capacity to use massive datasets, including as EHR and real-time patient data, is fundamental to AI-based CDSS. Unstructured clinical notes, lab results, and medical literature are analyzed by these systems using machine learning (ML) algorithms and natural language processing (NLP) approaches. Natural language processing (NLP) improves the CDSS by gleaning useful information from textual data, which in turn helps to match medication suggestions with the patient's health background and treatment requirements¹. To further guarantee patient safety, AI technologies permit predictive analytics, which may foretell possible adverse drug reactions (ADR) and suggest dose modifications^2,3.

Critical to CDSS are machine learning models like RNNs and CNNs. One use of convolutional neural networks (CNNs) is the detection of medication interactions in clinical datasets using pattern recognition, building on its reputation for medical image analysis¹. Similarly, RNNs are very good at processing sequential data, such as electrocardiograms (ECGs), which helps with medication recommendations based on changes in health over time^4,5. These artificial intelligence methods improve decision support by spotting trends that would be difficult for human doctors to see.

Achieving broad adoption of AI in CDSS would require addressing a number of obstacles, despite the fact that the advantages are substantial. Given the delicate nature of medical records, data privacy and security is a major issue. Maintaining conformity with data protection standards is critical, as is making sure that all operations adhere to American laws like HIPAA². Furthermore, there is still a lack of interoperability across various healthcare systems, which is a problem since CDSS relies on smooth data exchange to work properly⁶.

There are additional regulatory hurdles for CDSS that use AI. Guidelines to guarantee the security and dependability of AI-driven healthcare systems and devices are now in the works by regulatory agencies such as the Food and Drug Administration (FDA)⁷. Deep learning algorithms in particular are notoriously difficult to understand and work with because of their complexity. To encourage confidence and buy-in, healthcare practitioners must comprehend the reasoning behind AI-generated suggestions^8,9.

There is also the issue of bias in AI models, which might compromise the effectiveness and equity of CDSS. AI systems that are trained on biased datasets might provide unequal healthcare results since they perform differently for various demographic groups¹⁰. For AI to be used ethically in healthcare, it is essential to test models and audit algorithms for biases^11,12. The creation of a CDSS may be aided by embracing varied viewpoints and experience via the use of multidisciplinary teams¹³.

Regardless of these obstacles, AI-based CDSS has enormous potential to transform healthcare. In addition to improving the precision of medication suggestions, these tools bolster personalised medicine by adapting therapies to unique patient profiles¹⁴. By reducing fatigue and improving clinical procedures, CDSS allows healthcare providers to concentrate more on patient care by lowering the cognitive burden on clinicians¹⁵. Progress in this area should be centered on three main areas: making AI models more explainable, validating them via large-scale clinical trials, and investigating federated learning strategies to protect data privacy.

Finally, by providing new approaches to medication recommendation and individualised patient treatment, CDSS powered by AI signal a sea change in the healthcare industry. With an emphasis on the revolutionary potential of AI-powered CDSS to enhance patient outcomes and advance precision medicine, this article will explore the framework, obstacles, and future possibilities of building such systems.

LITTERATURE REVIEW :

One of the most influential technologies in the rapid development of AI and its healthcare applications is clinical decision support systems (CDSS). CDSS powered by AI combines cutting-edge algorithms with medical expertise to help doctors make better judgments, particularly when it comes to prescribing specific medications. This literature review summarises the current state of artificial intelligence (AI) CDSS, discusses its limitations, and looks forward to its possible future applications in clinical settings by citing several research.

CDSS powered by AI use a range of ML models to analyze intricate medical data and provide valuable insights. The capabilities of CDSS have been greatly improved by techniques like deep learning, natural language processing (NLP), and ensemble learning^16,17. To provide a better example, recurrent neural networks (RNNs) and convolutional neural networks (CNNs) have been used to analyse temporal patient data and forecast drug interactions, leading to more precise medication recommendation^18,19. To guarantee that the prescribed medications are in line with individual requirements, these algorithms may sift through massive datasets, such as EHRs and clinical notes.

Among CDSS's primary functions is the capacity to enhance medication prescription safety via the prediction of adverse drug reactions (ADRs)²⁰. Gradient boosting and ensemble algorithms are examples of predictive analytics models that may identify patients at risk for adverse drug reactions (ADRs) and propose alternative therapies²¹. Natural language processing (NLP) techniques improve the system even more by identifying possible contraindications by extracting pertinent information from unstructured data, including clinical notes ²². The increasing importance of AI in the pursuit of personalised medicine is shown by these advancements.

Even with all these improvements, there are still a lot of obstacles that prevent AI-based CDSS from being widely used. Healthcare data is particularly sensitive, making data privacy and security top priorities^23,24. Another obstacle is healthcare system interoperability; CDSS can't work well across institutions without easy data sharing²⁵. Also, it's important to work on improving algorithmic fairness since AI models trained on biassed datasets could provide different medication recommendations for different demographic groups²⁶. The need for thorough validation and auditing procedures to guarantee the moral use of AI in healthcare is highlighted by the fact that these biases might lead to unfair healthcare results.

The deployment of CDSS based on AI is also affected by regulatory frameworks. To guarantee patient safety and system dependability, authorities, including the U.S. Food and Drug Administration (FDA) have started to draft regulations to control AI in healthcare²⁷. On the other hand, issues with interpretability and transparency are often brought about by the complexity of deep learning models. For healthcare providers to have faith in AI-generated medication recommendations, they must comprehend the logic behind them²⁸. The capacity to test AI suggestions and match them with clinical knowledge is crucial for clinical adoption, and explainability plays a key role in this process.

By allowing the decentralized training of AI models without sharing patient data, federated learning has the ability to alleviate privacy problems, according to studies. While keeping patient information private, this method encourages cross-institutional learning. To improve model generalizability across different clinical contexts and to enable safe data sharing, future CDSS advancements should use federated learning frameworks. Research has shown that AI-based CDSS may improve diagnosis accuracy, decrease practitioner effort, and increase patient outcomes, all of which have far-reaching implications for healthcare delivery²⁹.

To sum up, CDSS powered by AI is bringing revolutionary changes to healthcare by providing new approaches to medication prescribing and patient care. Stakeholders must resolve the technological, ethical, and regulatory issues with these systems if they are to reach their full potential. The use of privacy-preserving methods, such as federated learning, and the improvement of algorithmic fairness should be the top priorities of future research. Advancements in AI-based CDSS are expected to be essential in driving personalised medicine forward and enhancing healthcare outcomes as these technologies mature.

Table 1: Summary of Literature Review on AI Applications in Hospital Pharmacy

Ref	AI Technique	Use Case	Challenge	Benefit	Future Focus	AI Model Used	Regulatory Aspect
16	Personalized medicine with AI	Drug suggestions and personalized treatments	Ensuring treatment efficacy and safety	Improves patient outcomes	Enhance precision medicine	Various ML Models	Align with healthcare standards
17	Trends and challenges in AI healthcare	Identifying trends in AI-based healthcare systems	Navigating ethical and technical barriers	Supports strategic healthcare advancements	Address ethical challenges	Ensemble Learning	Overcome regulatory challenges
18	RNNs for temporal patient data	Temporal health data analysis for better drug decisions	Handling sequential medical data	Enables predictive analytics	Expand temporal data usage	RNN	Ensure data compliance
19	CNNs for medical imaging	Pattern recognition for drug interaction identification	Detecting drug interactions accurately	Enhances diagnostic precision	Refine image-based drug analysis	CNN	N/A
20	Machine learning in medicine	Improving diagnostic accuracy	Building trust with reliable models	Increases diagnostic efficiency	Boost trust in AI models	Gradient Boosting	N/A
21	Ethical challenges in AI healthcare	Addressing bias in AI-based decisions	Mitigating bias in AI-based treatments	Promotes fair treatment	Improve fairness in decision-making	N/A	N/A
22	Multidisciplinary perspectives in AI	AI integration across healthcare disciplines	Fostering collaboration across fields	Encourages holistic AI applications	Encourage cross-discipline collaboration	N/A	N/A
23	Interoperability in healthcare systems	Improving data exchange between healthcare systems	Achieving seamless interoperability	Enhances operational efficiency	Resolve data interoperability issues	N/A	Achieve system interoperability
24	Regulatory considerations for AI	Regulating AI healthcare tools for patient safety	Balancing innovation with regulation	Provides safe AI healthcare deployment	Update regulations for AI tools	N/A	Comply with AI regulations
25	Bias in healthcare algorithms	Mitigating bias and promoting fairness	Preventing healthcare disparities	Ensures equitable care	Address demographic biases	N/A	Ensure ethical AI use
26	Burnout reduction with AI	Reducing clinician workload	Addressing healthcare provider burnout	Enhances clinician well-being	Expand burnout prevention efforts	N/A	N/A
27	CDSS regulations and standards	Ensuring safety through regulatory compliance	Complying with healthcare regulations	Promotes patient safety	Strengthen safety measures	N/A	Promote safety compliance
28	Medical informatics frameworks	Frameworks for computer-aided clinical systems	Adopting AI tools within medical contexts	Improves medical informatics	Advance AI frameworks	N/A	N/A
29	Federated learning for data privacy	Privacy-preserving collaboration across institutions	Facilitating secure data sharing	Safeguards patient data privacy	Increase use of federated learning	Federated Learning	Ensure privacy compliance

METHODOLGY:

The development of the AI-based Clinical Decision Support System (CDSS) for drug suggestions follows a multi-phased approach to ensure accuracy, reliability, and security. The first step involves data collection from multiple sources, including Electronic Health Records (EHRs), clinical notes, medication history, diagnostic reports, and public datasets such as MIMIC-III. Collecting comprehensive data ensures that the system can address a wide range of medical conditions and provide personalized drug recommendations. Following collection, data preprocessing is carried out to clean, normalize, and standardize the data, ensuring consistency across sources. Techniques such as Natural Language Processing (NLP), specifically named entity recognition (NER), are applied to extract relevant information from unstructured clinical notes, identifying key medical entities such as diseases, symptoms, and drugs.

Figure 1: System Architecture for AI-Based Clinical Decision Support System (CDSS) for Drug Suggestions

Machine learning models are chosen for the system's core according to how well they perform tasks. In structured clinical datasets, Recurrent Neural Networks (RNNs) are used to identify patterns, while Convolutional Neural Networks (CNNs) are used to analyse sequential data, such as medication histories. Predictive analytics also uses gradient boosting algorithms like XGBoost, which are very useful for ADR predicting. After they're chosen, these models are trained utilising GPU-based computing to speed up the process on top of the preprocessed data. The system's performance is assessed using measures like as accuracy, precision, recall, and F1-score, and K-fold cross-validation is used to prevent overfitting and guarantee generalisability.

Following completion of training, the CDSS is incorporated into a platform that healthcare practitioners may access over the web. Easy data entry and real-time medication recommendations are hallmarks of this platform's user-friendly interface. Implementing a back-end architecture in the cloud allows for quick data processing and easy connection with current EHR systems. Integrating SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations), the system emphasizes explainability and transparency, enabling doctors to comprehend the reasons behind each medication suggestion.

Given the sensitivity of healthcare data, federated learning is adopted to address privacy concerns. This approach ensures that the AI models are trained on decentralized data from multiple institutions without sharing raw patient data, maintaining confidentiality and regulatory compliance. Encryption protocols and secure authentication mechanisms further enhance the system’s security. Once deployed, the system undergoes continuous monitoring to track its performance and ensure consistent accuracy. Feedback from clinicians is collected to refine the models and improve the user experience. Clinical trials are conducted to validate the system in real-world scenarios, and regular updates are implemented to incorporate the latest medical guidelines and drug information.

This comprehensive methodology ensures that the AI-based CDSS provides accurate, personalized drug suggestions while maintaining trust, security, and compliance with healthcare standards. The system is designed to optimize clinical workflows, enhance diagnostic precision, and improve patient outcomes, all while fostering transparency and collaboration across healthcare institutions.

RESULT AND DISSCUSSION:

The AI-based Clinical Decision Support System (CDSS) for drug suggestions demonstrated promising results across multiple metrics, validating the effectiveness of the models used. The Recurrent Neural Network (RNN) achieved an accuracy of 92.5%, with precision and recall of 91.0% and 92.1%, respectively. This model performed well in processing sequential data, such as medication histories, ensuring accurate drug interaction detection. The Convolutional Neural Network (CNN) showed superior performance, with an accuracy of 93.2% and precision of 94.8%, excelling in pattern recognition from structured clinical data. XGBoost also exhibited competitive results, achieving an F1-score of 91.2%, which made it highly effective for predicting adverse drug reactions (ADR). However, the hybrid model, which combined the strengths of RNN and XGBoost, outperformed all individual models, reaching 94.5% accuracy, 95.2% precision, and 94.0% F1-score.

Table 2: Comparison of Model Performance Metrics for Drug Shortage Prediction

Metric	RNN	CNN	XGBoost	Hybrid (RNN + XGBoost)
Accuracy (%)	92.5	93.2	90.8	94.5
Precision (%)	91	94.8	92.5	95.2
Recall (%)	92.1	91.3	89.9	93.8
F1-Score (%)	91.5	93	91.2	94
Training Time (mins)	30	40	10	45
Inference Time (ms)	200	150	50	100

Clinician feedback indicated a reduction in cognitive workload and decision-making time by 30%, highlighting the usability of the CDSS platform. The incorporation of SHAP and LIME for model explainability fostered trust among healthcare providers, allowing them to understand the reasoning behind the recommendations. Additionally, clinical trials over three months demonstrated a 15% reduction in ADR, emphasizing the system’s impact on improving patient safety. Chronic disease management outcomes also improved by 20%, as the CDSS provided timely, personalized drug suggestions.

Figure 2: Performance Comparison Heatmap of Different Models Across Metrics

Figure 3: Performance Comparison of Different Models Across Key Metrics

Figure 4: Bar Chart of Model Comparison Across Metrics

The system's adoption benefited from the use of federated learning, ensuring data privacy and security by enabling decentralized model training without sharing raw data across institutions. However, challenges such as interoperability issues between different healthcare systems remain, limiting seamless integration. The hybrid model showed the most promise, leveraging RNN’s sequential data-handling capabilities and XGBoost’s predictive power. In conclusion, the AI-based CDSS not only optimized clinical workflows but also improved patient outcomes, setting a benchmark for future developments in AI-driven healthcare systems.

CONCLUSION:

A prime example of AI's revolutionary potential in healthcare is the creation of an AI-based CDSS for medication recommendations. The system's ability to provide precise, individualized medication suggestions—made possible by the combination of models like XGBoost, Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN)—improves patient outcomes while decreasing the likelihood of adverse drug reactions (ADR). The hybrid approach, combining the strengths of RNN and XGBoost, outperformed individual models, achieving the highest accuracy and precision. The use of SHAP and LIME for explainability ensured that healthcare providers could understand the rationale behind the recommendations, fostering trust in the system. Feedback from clinicians demonstrated improved decision-making efficiency, with a 30% reduction in cognitive workload. Clinical trials validated the system’s effectiveness by showing a 15% reduction in ADRs and a 20% improvement in outcomes for chronic disease management. While the system achieved significant results, challenges such as data interoperability and integration with existing Electronic Health Records (EHR) systems remain. However, the adoption of federated learning addressed data privacy concerns by enabling secure, decentralized model training. In conclusion, the AI-based CDSS offers a scalable solution for enhancing clinical workflows and supporting precision medicine through data-driven insights. Future work should focus on addressing interoperability issues and refining hybrid models to further optimize performance. This research underscores the potential of AI in revolutionizing healthcare, paving the way for advanced CDSS tools that empower healthcare providers and improve patient care on a larger scale.

CONFLICT OF INTEREST:

The authors have no conflicts of interest regarding this investigation.

ACKNOWLEDGMENTS:

We acknowledge the Healthcare Professionals, Data Scientists, and Reviewers whose contributions and feedback were essential to the development and enhancement of this research.

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Received on 17.12.2024 Revised on 22.02.2025

Accepted on 30.03.2025 Published on 10.04.2025

Available online from April 12, 2025

Research J. Pharmacy and Technology. 2025;18(4):1854-1860.

DOI: 10.52711/0974-360X.2025.00265

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.