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Artificial Intelligence Applications in Medicine: Navigating the Patient Safety Paradox

Dr. Hamdi Alhakimi
Community Medicine Specialist And Medical Statistician

Corresponding author.
Dr. Hamdi Alhakimi

Tel.:
E-mail address: dr.hamdi.hakimi@gmail.com

Abstract

As Artificial Intelligence (AI) penetrates the realm of medicine, the discourse around its impact on patient safety becomes paramount. This comprehensive review delves into the dichotomy of AI applications in medicine, evaluating their potential to enhance patient safety while considering the associated challenges and concerns.
Exploring diverse AI methodologies, including machine learning, natural language processing, and computer vision, the paper investigates their role in medical data analysis, diagnostics, and treatment recommendations. The review critically examines the advantages of AI-driven decision support systems in mitigating errors, optimizing workflows, and augmenting clinical decision-making, while also addressing the ethical considerations, transparency issues, and data privacy concerns inherent in AI applications. Real-world case studies and applications illustrate instances where AI acts as both a proactive tool in identifying patient safety risks and a potential source of concerns. The paper underscores the need for robust regulatory frameworks to ensure responsible AI deployment in medicine.
In conclusion, this review provides a nuanced exploration of AI applications in medicine, navigating the delicate balance between the potential benefits for patient safety and the associated challenges. By offering a comprehensive understanding of the multifaceted impact of AI, this paper serves as a critical resource for healthcare practitioners, researchers, and policymakers aiming to harness the potential of AI while safeguarding patient well-being.

Biography

“Highly experienced and accomplished public health professional with a strong background in epidemiology, biostatistics, and research methodology. Demonstrated expertise in leading and supervising academic programs, conducting groundbreaking research, and implementing effective public health strategies.

Skilled in statistical analysis and proficient in a range of software tools, including SPSS, the R program, and Python. Well-versed in artificial intelligence algorithms such as Neural Networks, Support Vector Machine, and Random Forest.

Additionally, experience with other specific-use software, such as Pajek for Social Network Analysis and Berkeley Madonna software (Mathematical modeling of epidemics). Preparing and analyzing complex data sets, generating actionable insights, and developing innovative models to predict and manage epidemics. Proven ability to collaborate with multidisciplinary teams and deliver comprehensive training sessions. Committed to improving global health outcomes through evidence-based approaches and strategic decision-making.”