Leveraging Artificial Intelligence for Personalized Contraceptive Counseling: A Theoretical and Empirical Exploration

FANUEL MUSOTSI, Population Services Kenya
Joshua Limo, Population Service Kenya

Title: Leveraging Artificial Intelligence for Personalized Contraceptive Counseling: A Theoretical and Empirical Exploration Authors: Fanuel Musotsi¹, Joshua Limo² Institutional Affiliations: ¹Population Services Kenya Corresponding Author: Fanuel Musotsi Background: The integration of Artificial Intelligence (AI) into reproductive health services offers a transformative potential to enhance personalized contraceptive counselling. This study explores the application of AI algorithms in tailoring contraceptive advice based on individual health data and preferences, addressing the growing demand for personalised healthcare solutions. Methodology: A mixed-methods approach was employed, combining quantitative analysis of health data from diverse populations with qualitative interviews with healthcare providers and patients. Machine learning models were developed to process and analyze electronic health records, generating personalized contraceptive recommendations. Results: The application of AI in contraceptive counselling demonstrated a significant improvement in patient satisfaction and adherence to contraceptive methods. However, challenges such as data privacy concerns and the need for ethical AI integration were identified. Conclusion: AI offers promising opportunities for personalised contraceptive counselling, improving decision making and improving reproductive health outcomes. Addressing the identified challenges is essential for the successful integration of AI into reproductive health services, ensuring the ethical and effective use of this technology.

Keywords: Fertility, Children, Adolescents, and Youth, Sexual and Reproductive Health and Rights, Family Planning and Contraception

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