Introduction
Integrating artificial intelligence (AI) into healthcare has led to transformative improvements in diagnostics, treatment, and personalized care. However, the presence of bias in AI systems can undermine these advances and have potentially harmful consequences for patients. This essay will examine the trend of identifying and avoiding bias in AI, its relevance for Integrated Nursing Services (INS) practice, and recommendations for managing these impacts as an INS leader.
Discussion
As AI becomes more embedded in healthcare, it is crucial to identify and address these biases to ensure equitable and safe care for all patients. Bias in AI systems may occur from diverse sources, such as data that reflects only certain groups of people, errors in the software or algorithms, or a failure to account for the needs and experiences of patients from different backgrounds (Challen et al., 2019; Gudis et al., 2023). Nurses increasingly rely on AI-driven tools for patient monitoring, risk assessment, and treatment planning (Gudis et al., 2023). Consequently, biased AI systems can lead to misdiagnoses and inappropriate treatments. For INS practice, this means understanding the potential for biased algorithms to influence decision-making and mitigating these risks.
INS leaders must create preventive steps to manage bias using AI. First, they should promote education and awareness of AI bias among nursing staff (Gudis et al., 2023). This includes providing training on how AI algorithms work and the potential sources of bias. Second, it is crucial to ensure that the data included in developing AI models represent broad range of patient demographics (Norori et al., 2021). Third, INS leaders should collaborate with AI developers to establish rigorous evaluation and validation processes for AI-driven tools (Challen et al., 2019). Fourth, promoting transparency in AI development and adopting open science practices can help identify and address biases in AI algorithms (Norori et al., 2021). INS leaders should advocate for sharing data, algorithms, and evaluation methodologies, enabling a more collaborative and rigorous approach.
Conclusion
The trend of identifying and avoiding bias in AI is essential for ensuring equitable, safe, and high-quality healthcare. As AI becomes more integrated into INS practice, nursing leaders must take proactive steps to identify, address, and manage the potential biases in AI systems. By fostering education, promoting diverse representation, establishing rigorous evaluation processes, and advocating for transparency and open science, INS leaders can help ensure AI’s responsible and equitable use in healthcare.
References
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019). Artificial intelligence, bias and clinical safety. BMJ Quality & Safety, 28(3), 231–237. Web.
Gudis, D. A., McCoul, E. D., Marino, M. J., & Patel, Z. M. (2023). Avoiding bias in artificial intelligence. International Forum of Allergy & Rhinology, 13(3), 193–195. Web.
Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., & Tzovara, A. (2021). Addressing bias in big data and AI for health care: A call for open science. Patterns (New York, N.Y.), 2(10), 100347. Web.