Exploring Healthcare Professionals' Understanding And Experiences Of Artificial Intelligence Technology Use In The Delivery Of Healthcare

Abstract

Artificial intelligence (AI) stands at the forefront of modern technological innovation, offering transformative potential across various domains, including healthcare. In essence, AI represents a paradigm shift, transcending mere task automation to embody technologies that augment patient care within diverse healthcare ecosystems. However, the realization of AI's potential necessitates diligent attention to prevailing challenges, encompassing issues of data privacy, algorithmic bias, and the indispensable role of human judgment and expertise. This abstract provides a comprehensive overview of the multifaceted landscape of AI implementation in clinical practice, synthesizing existing literature, identifying gaps in understanding, and proposing avenues for future research and collaboration.

Mitigating the challenges inherent in integrating AI into clinical practice demands a multidisciplinary approach. Innovative data annotation methods and the development of rigorous AI techniques and models emerge as critical components in addressing these challenges. Collaboration between computer scientists and healthcare providers is pivotal, ensuring the creation of practical and usable AI technologies that align with clinical needs. By amalgamating ethical inclusivity, software development best practices, and implementation science principles, the AI community can forge an integrated framework for AI implementation and maintenance. Moreover, fostering collaboration across healthcare settings to share data and validate AI outcomes emerges as indispensable for AI's success in clinical practice. Comprehensive training and education initiatives, commencing at the undergraduate level and extending throughout practitioners' careers, are imperative to ensure proficient adaptation to AI technologies, thereby mitigating legal, ethical, and interpretational risks.

The convergence of big data and AI in healthcare underscores the critical need for robust data protection legislation. Such legislation is essential for safeguarding individual privacy and mitigating ethical risks associated with the utilization of AI in clinical settings. Implementing predictive algorithms to detect and prevent cyber threats is pivotal for maintaining data privacy and system integrity, particularly in the face of escalating cyberattacks. While AI presents inherent risks, it also offers substantial benefits, including task streamlining, enhanced efficiency, and research support. Therefore, a balanced approach that prioritizes ethical awareness, transparency, and accountability is necessary to harness the full potential of AI in healthcare.

As healthcare organizations navigate the intricate landscape of AI integration, adopting a holistic approach to addressing legal, ethical, and cybersecurity considerations will foster a climate conducive to AI-driven innovation and patient-centered care. This abstract highlights the significance of addressing these multifaceted challenges in the successful implementation of AI within diverse healthcare ecosystems, providing a roadmap for future research and collaboration in this dynamic field.