Revolutionizing addiction treatment through artificial intelligence-based Clinical Decision Support System (CDSS) for improved patient outcomes and clinical decision-making.
Developing an artificial intelligence-based Clinical Decision Support System (CDSS) for the treatment of alcohol and nicotine use disorders at .
To develop an AI-based Clinical Decision Support System (CDSS) for the treatment of alcohol and nicotine use disorders (AUD & NUD) and measure the accuracy of its recommendations regarding the locus of care (inpatient or outpatient) and choice of medicines.
To revolutionize addiction treatment by providing healthcare professionals with AI-powered decision support that improves treatment outcomes, reduces errors, and enhances patient care quality.
Improved clinical decision-making, better patient outcomes, reduced treatment costs, and enhanced healthcare delivery for individuals suffering from alcohol and nicotine use disorders.
Comprehensive approach to developing and validating our AI-based Clinical Decision Support System.
Comprehensive collection of clinical data from patients with alcohol and nicotine use disorders, including treatment history, outcomes, and clinical parameters.
Development of machine learning algorithms to analyze clinical data and provide evidence-based recommendations for treatment decisions.
Rigorous validation of AI recommendations against clinical expertise and established treatment protocols to ensure accuracy and reliability.
Our intelligent system provides evidence-based recommendations for treatment decisions in alcohol and nicotine use disorders.
AI-powered recommendations for determining whether patients should receive inpatient or outpatient treatment based on clinical assessment.
Intelligent medication recommendations based on patient history, severity, and evidence-based treatment protocols.
Continuous monitoring and adjustment of treatment plans based on patient progress and clinical outcomes.
Secure and efficient data annotation tools for clinical research with privacy compliance.
Our comprehensive annotation platform ensures HIPAA compliance while enabling efficient data processing for AI training and evaluation.
Our annotation environment (Label Studio) enables annotators to carefully review clinical notes and remove HIPAA-sensitive information such as patient names, ages, and other identifiers.
A dedicated Platform supports Q&A data collection through manual annotation. This allows annotators to label responses for training and evaluation while ensuring data privacy and compliance.
All data handling follows strict privacy regulations
Round-the-clock availability with enterprise security
Our multidisciplinary team combines expertise in psychiatry, AI/ML, and clinical research to advance addiction treatment.
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Interested in our research or want to collaborate? Contact our team for more information about the AI-CDSS project.
research@.ac.in
aicdss@.ac.in
+91 (080) 2699 5000
+91 (080) 2699 5001