Author(s):
Vinodpuri Rampuri Gosavi, Bhavna Ambudkar, Rajendra V. Patil, Rameshwar Dadarao Chintamani, Aashish G. Jagneet, Suman Kumar Swarnkar
Email(s):
sumanswarnkar17@gmail.com
DOI:
10.52711/0974-360X.2025.00341 
Address:
Vinodpuri Rampuri Gosavi1, Bhavna Ambudkar2, Rajendra V. Patil3, Rameshwar Dadarao Chintamani4, Aashish G. Jagneet5, Suman Kumar Swarnkar6*
1Department of Electronics and Telecommunications Engg, Sandip Foundation, Sandip Institute of Technology and Research Center(SITRC), Nashik.
2Department of Electronics & Telecommunication Engineering, Symbiosis Institute of Technology, Pune.
3Department of Computer Engineering,
SSVPS Bapusaheb Shivajirao Deore College of Engineering, Dhule (M.S.), India.
4Information Technology, Sanjivani College of Engineering, Kopargaon Ahmednagar, Maharashtra, India.
5Department of Computer Engineering Sandip Institute of Technology and Research Centre(SITRC)
6Shri Shankaracharya Institute of Professional Management and Technology, Raipur, Chhattisgarh, India.
*Corresponding Author
Published In:
Volume - 18,
Issue - 5,
Year - 2025
ABSTRACT:
Personalized drug therapy is pivotal in optimizing patient outcomes by tailoring treatments to individual needs. This study explores an AI-based approach for generating personalized drug therapy recommendations based on a doctor's clinical descriptions. By leveraging natural language processing (NLP) and machine learning algorithms, the system analyzes unstructured clinical notes to identify relevant symptoms, medical history, and diagnostic information. The extracted data is then processed to suggest optimal drug therapies, considering factors such as drug efficacy, potential side effects, and patient-specific conditions like age, allergies, and comorbidities. A comprehensive dataset of clinical notes and drug prescriptions is used to train the AI model, enhancing its ability to learn from real-world medical cases. The proposed system aims to assist healthcare professionals in making more informed decisions, reduce the risk of adverse drug reactions, and improve overall treatment effectiveness. Initial results indicate that the AI-driven model provides accurate and clinically relevant recommendations in line with standard treatment protocols. This research holds significant potential for improving the efficiency and precision of personalized medicine, offering a practical solution for integrating AI into routine healthcare practices. Future work will focus on refining the model and ensuring compliance with ethical and regulatory standards.
Cite this article:
Vinodpuri Rampuri Gosavi, Bhavna Ambudkar, Rajendra V. Patil, Rameshwar Dadarao Chintamani, Aashish G. Jagneet, Suman Kumar Swarnkar. Personalized Drug Therapy Recommendations Based on Doctor's Clinical Descriptions Using AI. Research Journal of Pharmacy and Technology. 2025;18(5):2385-2. doi: 10.52711/0974-360X.2025.00341
Cite(Electronic):
Vinodpuri Rampuri Gosavi, Bhavna Ambudkar, Rajendra V. Patil, Rameshwar Dadarao Chintamani, Aashish G. Jagneet, Suman Kumar Swarnkar. Personalized Drug Therapy Recommendations Based on Doctor's Clinical Descriptions Using AI. Research Journal of Pharmacy and Technology. 2025;18(5):2385-2. doi: 10.52711/0974-360X.2025.00341 Available on: https://www.rjptonline.org/AbstractView.aspx?PID=2025-18-5-64
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