The challenge of image interpretation in emergency settings, where high workload and time pressure can affect diagnostic accuracy

The gap

Evidence profile

Sourced from the future work and inline gaps and stated challenges and stated research gap of the source papers, classified as general, spanning 4 journals. Those papers have been cited 3 times in total.

Supporting evidence — 5 representative gaps

• Diagnostic Performance of Artificial Intelligence and Deep Learning for Diabetic Retinopathy Screening: A Systematic Review and Meta-analysis (2026) · Journal of Optometry and Ophthalmology Research · doi

  Several research priorities emerge from this review. Enhanced data collection including detailed patient demographics and clinical characteristics will enable more comprehensive performance evaluation. Large-scale multi-center validation studies will improve algorithm generalizability across diverse populations. Development of human-AI collaboration models that combine Page: 5 of 9 Copyright © Napa Suebsaiphrom, et al.J Opto Opht Res, 2026 Volume 2 | Issue 1www.oaskpublishers.com ophthalmologist expertise with AI assistance can optimize work efficiency and medical resource utilization. Finally, establishing standardized imaging protocols and quality benchmarks will help maximize AI performance across clinical settings.

  generalfuture workevidence 5/5
  Keywords: clinical performance across several priorities emerge review enhanced collection including detailed patient demographics characteristics enable
• The digital revolution in medical imaging: The Role of artificial intelligence (AI) in the future of radiology: A subject review (2026) · JOURNAL OF MEDICAL AND HEALTH SCIENCE · doi

  The radiology AI is not merely new technology; it's a disruption to Precision Radiology. As these and other examples in this review illustrated, the AI impact stretches across the entire imaging value chain, from raw data acquisition/derivation through to complex interpretation of radiomic features allowing for "virtual biopsies" [72]. Whilst concern for AI's inevitability overtaking all radiologists was very much a topic of early discussion, the collective mindset has cert ainly shifted towards Augmented Intelligence. In this scenario, the radiologist continues to be the center of clinical integration, utilizing IA tools to dehumanize repetitive low-energy tasks and focusing their attention on complex diagnostic issue-solving problems and patient engagement [73]. The future achievement of AI in this domain will be based on our ability to address the "black box" nature of algorithms via Explainable AI (XAI), and to establish that models are trained on a diverse, multi-institutional dataset to eradicate demographic bias [74]. Restricted by the regulation system and liability laws, the integration of human precision with machine efficiency would probably be a future direction of diagnostic medicine, which will enable health care become more prospective, accurate, and available [75, 76].

  generalfuture workevidence 5/5
  Keywords: radiology precision complex integration diagnostic future merely technology disruption examples review illustrated impact stretches across
• Test-fairness deep learning with influence score (2026) · PLOS Digital Health · doi

  Future work will explore the application of the fair I-score method in additional medical imaging contexts, assessing its effectiveness in mitigate subgroup performance disparities in broader healthcare AI applications. Future research will focus on extending the fair I-score method to multi-class and multi-modal medical imaging tasks, integrating real-world clinical data, and exploring fairness definitions tailored to healthcare applications to ensure equitable AI-driven diagnostics.

  generalinline gapsevidence 5/5
  Keywords: future fair score medical imaging healthcare applications multi explore application additional contexts assessing effectiveness mitigate
• Artificial intelligence in acute hydrocephalus: Narrowing the diagnostic gap (2026) · European Radiology · doi

  The challenge of image interpretation in emergency settings, where high workload and time pressure can affect diagnostic accuracy. The limitation of current approaches, which may rely on confounding image features or indirect surrogate markers. The need for effective integration of AI-based triage systems into existing radiology workflows and adequate radiologist training.

  generalstated challengesevidence 5/5
  Keywords: challenge image interpretation emergency settings high workload time
• Performance of AI vs radiology residents in the detection of intracranial hemorrhage on emergency CT: a real-world analysis (2026) · European Radiology · cited 3× · doi

  The introduction of AI software in clinical practice is still hampered by various challenges, such as validation of the AI models on various clinical datasets or the lack of generalizability due to different validation methodologies. Most studies have focused on technical performances, rather than evaluating AI in real-world clinical settings, particularly in comparison with radiology residents in emergency contexts.

  generalstated research gapevidence 5/5
  Keywords: introduction software clinical practice still hampered various challenges

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