The Progress in Imaging Technologies and Implications for Nursing Practice: A Systematic Review
Bibliographic record
Abstract
Background: The recent years have seen revolutionary advancements in medical imaging technologies, including artificial intelligence (AI), point-of-care ultrasound (POCUS), and hybrid imaging modalities like PET/MRI. These technologies have transformed diagnostic and therapeutic interventions. These technologies provide unprecedented functional and molecular information, with new implications for clinical care beyond traditional radiology. Aim: The aim of this review is to synthesize the literature from 2015 through 2025 to explore how these imaging technology advances are redefining nursing roles, responsibilities, and competencies. Methods: A Systematic literature review from 2015 through 2025 was conducted. The reviews were evaluated to assess the implications of the emerging imaging technologies for nursing practice within various clinical settings. Results: The outcome reflects a paradigm change for the nursing practice from a passive taker of imaging data to an active participant in the imaging cycle. Key implications are the increased responsibility in patient preparation for complex scans, intra-procedural surveillance, bedside data interpretation by POCUS, and the management of clinical alerts based on AI. Such development involves significant educational demands and ethical concerns of data privacy and accountability of algorithms. Conclusion: Advanced imaging technologies are irreversibly transforming nursing practice, demanding an innovative approach. To ensure patient safety and optimal outcomes, the profession must develop standardized curricula for education, foster robust interprofessional relationships, and establish explicit competency frameworks and policies to support nurses in this high-technology practice environment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".