TY  - JOUR
AU  - Nadeem, Maryam
AU  - Aslam, Fiza
AU  - Rehman, Muhammad Aftab Ur
AU  - Aslam, Saba
PY  - 2025
DA  - 2025/06/29
TI  - Role of Social Factors in the Adoption of AI-Driven Personalized Healthcare
JO  - Journal of Artificial Intelligence in Bioinformatics
T2  - Journal of Artificial Intelligence in Bioinformatics
JF  - Journal of Artificial Intelligence in Bioinformatics
VL  - 1
IS  - 1
SP  - 30
EP  - 40
DO  - 10.62762/JAIB.2025.345522
UR  - https://www.icck.org/article/abs/JAIB.2025.345522
KW  - personalized medicine
KW  - AI adoption factors
KW  - healthcare
KW  - trust AI
AB  - Artificial intelligence (AI) is transforming personalized medicine through its potential. However, implementing AI-driven healthcare solutions remains inconsistent because of certain social factors, i.e., cultural beliefs, trust issues, and major accessibility elements. This study focuses on the key social determinants involved in the acceptance and implementation of AI-driven medicine, with a prime focus on hurdles such as algorithmic bias, transparency issues, and public skepticism. A quantitative approach was employed in the research, and survey data were collected among healthcare professionals, policymakers, and patients. Statistical analyses were performed, including chi-square tests and multiple regression modeling. It examined relationships between social factors and AI adoption rates. The findings presented that familiarity with AI positively influences its acceptance However, the concerns about non-transparent algorithms and cultural resistance continue to hinder its adoption. Nearly 48% of respondents exhibited low attitudes toward AI-driven healthcare. On the other hand, 68% showed concerns about data security. Furthermore, socioeconomic disparities impact accessibility to a great extent, with lower-income groups reporting limited exposure to AI-powered medical solutions. The study also identified transparency as both a facilitator and a barrier, with clinicians hesitant to rely on AI systems due to the lack of explainability. Addressing all these barriers through targeted education, trust-building initiatives, and ethical oversight can increase the equitable integration of AI-driven personalized medicine in diverse settings of healthcare settings.
SN  - 3068-7535
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Nadeem2025Role,
  author = {Maryam Nadeem and Fiza Aslam and Muhammad Aftab Ur Rehman and Saba Aslam},
  title = {Role of Social Factors in the Adoption of AI-Driven Personalized Healthcare},
  journal = {Journal of Artificial Intelligence in Bioinformatics},
  year = {2025},
  volume = {1},
  number = {1},
  pages = {30-40},
  doi = {10.62762/JAIB.2025.345522},
  url = {https://www.icck.org/article/abs/JAIB.2025.345522},
  abstract = {Artificial intelligence (AI) is transforming personalized medicine through its potential. However, implementing AI-driven healthcare solutions remains inconsistent because of certain social factors, i.e., cultural beliefs, trust issues, and major accessibility elements. This study focuses on the key social determinants involved in the acceptance and implementation of AI-driven medicine, with a prime focus on hurdles such as algorithmic bias, transparency issues, and public skepticism. A quantitative approach was employed in the research, and survey data were collected among healthcare professionals, policymakers, and patients. Statistical analyses were performed, including chi-square tests and multiple regression modeling. It examined relationships between social factors and AI adoption rates. The findings presented that familiarity with AI positively influences its acceptance However, the concerns about non-transparent algorithms and cultural resistance continue to hinder its adoption. Nearly 48\% of respondents exhibited low attitudes toward AI-driven healthcare. On the other hand, 68\% showed concerns about data security. Furthermore, socioeconomic disparities impact accessibility to a great extent, with lower-income groups reporting limited exposure to AI-powered medical solutions. The study also identified transparency as both a facilitator and a barrier, with clinicians hesitant to rely on AI systems due to the lack of explainability. Addressing all these barriers through targeted education, trust-building initiatives, and ethical oversight can increase the equitable integration of AI-driven personalized medicine in diverse settings of healthcare settings.},
  keywords = {personalized medicine, AI adoption factors, healthcare, trust AI},
  issn = {3068-7535},
  publisher = {Institute of Central Computation and Knowledge}
}