AI-ASSISTED DIAGNOSTICS IN NURSING PRACTICE: IMPACT ON PATIENT ASSESSMENT AND CARE
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Healthcare Technology, Nursing Practice, Patient Assessment
Abstract
The rapid integration of artificial intelligence (AI) into healthcare has transformed clinical decision-making processes, including patient assessment and diagnostic support. In nursing practice, accurate and timely assessment is critical to patient safety and quality of care, yet increasing workload and clinical complexity often challenge nurses’ diagnostic performance. This study aims to examine the impact of AI-assisted diagnostic tools on nursing assessment accuracy, efficiency, and overall patient care outcomes. The research employs a mixed-methods design combining quantitative analysis of assessment performance indicators with qualitative exploration of nurses’ experiences in clinical settings where AI diagnostics are implemented. Data were collected from registered nurses across selected hospital units using standardized assessment records, questionnaires, and semi-structured interviews. The results indicate that AI-assisted diagnostics significantly improve assessment accuracy, reduce time to clinical decision-making, enhance early detection of patient deterioration, and increase nurses’ confidence in clinical judgment. Qualitative findings reveal that nurses perceive AI tools as supportive systems that augment, rather than replace, professional expertise when appropriately integrated into workflows. The study concludes that AI-assisted diagnostics represent a valuable advancement in nursing practice by strengthening patient assessment and promoting safer, more consistent care.
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References
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