AI-Driven Precision Identification of Rare Disease Patients and Effectiveness Analysis of Personalized Marketing Strategies

Zhenghao Pan

doi:10.54254/2755-2721/2025.LD27179

Applied and Computational EngineeringOpen access

AI-Driven Precision Identification of Rare Disease Patients and Effectiveness Analysis of Personalized Marketing Strategies

Research Article

Open Access

AI-Driven Precision Identification of Rare Disease Patients and Effectiveness Analysis of Personalized Marketing Strategies

Zhenghao Pan ^1*

¹ Emerging Media Studies, Boston University, MA, USA

^*Corresponding author: ais65541@gmail.com

Published on 24 September 2025

ACE Vol.184

ISSN (Print): 2755-273X

ISSN (Online): 2755-2721

ISBN (Print): 978-1-80590-307-9

ISBN (Online): 978-1-80590-308-6

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Abstract

Rare diseases affect millions of patients worldwide, presenting significant challenges in patient identification and targeted marketing of therapeutic interventions. This research proposes an artificial intelligence-driven framework for precise identification of rare disease patients and develops personalized marketing strategies to enhance treatment accessibility. The study integrates multi-source data including electronic health records, social media patterns, and search behaviors to construct machine learning models capable of identifying potential rare disease patients with 89.2% accuracy across five disease categories. The personalized marketing strategies demonstrated a 73% improvement in patient engagement rates and 68% increase in treatment awareness compared to traditional broadcasting approaches. The framework addresses critical gaps in rare disease patient outreach while maintaining ethical standards for data privacy. Results indicate substantial potential for AI-enhanced precision marketing to improve healthcare resource allocation efficiency and accelerate therapeutic adoption among rare disease populations, contributing to reduced diagnostic delays and enhanced patient outcomes.

Keywords:

Artificial Intelligence, Rare Disease Identification, Precision Marketing, Patient Engagement

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References

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References

[1]. Brasil, S., Pascoal, C., Francisco, R., dos Reis Ferreira, V., A. Videira, P., & Valadão, G. (2019). Artificial intelligence (AI) in rare diseases: is the future brighter?. Genes, 10(12), 978.

[4]. Budėnaitė, M., Correia, R. F., & Venciūtė, D. (2024). The Influence of Artificial Intelligence on Advertising. In AI Innovation in Services Marketing (pp. 134-149). IGI Global.

[6]. Lee, G. H., Lee, K. J., Jeong, B., & Kim, T. (2024). Developing personalized marketing service using generative AI. IEEE Access, 12, 22394-22402.

[7]. Wojtara, M., Rana, E., Rahman, T., Khanna, P., & Singh, H. (2023). Artificial intelligence in rare disease diagnosis and treatment. Clinical and Translational Science, 16(11), 2106-2111.

[8]. Visibelli, A., Roncaglia, B., Spiga, O., & Santucci, A. (2023). The impact of artificial intelligence in the odyssey of rare diseases. Biomedicines, 11(3), 887.

[10]. Fuster-Barceló, C., Cámara, C., & Peris-López, P. (2024). ECG-Based Patient Identification: A Comprehensive Evaluation Across Health and Activity Conditions. IEEE Access.

[15]. Groft, S. C., Posada, M., & Taruscio, D. (2021). Progress, challenges and global approaches to rare diseases. Acta paediatrica, 110(10), 2711-2716.

Cite this article

Pan,Z. (2025). AI-Driven Precision Identification of Rare Disease Patients and Effectiveness Analysis of Personalized Marketing Strategies. Applied and Computational Engineering,184,50-64.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of CONF-MLA 2025 Symposium: Intelligent Systems and Automation: AI Models, IoT, and Robotic Algorithms

ISBN: 978-1-80590-307-9(Print) / 978-1-80590-308-6(Online)

Editor: Hisham AbouGrad

Conference website: https://www.confmla.org/

Conference date: 17 November 2025

Series: Applied and Computational Engineering

Volume number: Vol.184

ISSN: 2755-2721(Print) / 2755-273X(Online)

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