Background: The introduction of lung cancer screening (LCS) programmes will lead to a surge in imaging volumes and place greater demands on radiologists to provide timely and accurate interpretation. This increased workload risks overburdening a limited radiologist workforce, delaying diagnosis, and worsening burnout. Advancements in artificial intelligence (AI) models offer the potential to detect and classify pulmonary nodules without a loss in diagnostic performance. Methods: A systematic review of AI performance in lung cancer detection on computed tomography (CT) scans was conducted. Multiple databases like Medline, Embase, PubMed, and Cochrane were searched within a 12-year range from 1 January 2010 to 21 December 2022. Results: Fourteen studies were selected for this systematic review, with seven in the detection subgroup and eight in the classification subgroup. Compared to radiologists’ performance in the respective articles, the AI models demonstrated a higher sensitivity (86.0–98.1% against 68–76%) but lower specificity (77.5–87% against 87–91.7%) for the detection of lung nodules. In classifying the malignancy of lung nodules, AI models generally showed a greater sensitivity (60.58–93.3% against 76.27–88.3%), specificity (64–95.93% against 61.67–84%), and accuracy (64.96–92.46% against 73.31–85.57%) over radiologists. Conclusion: AI models for the detection and classification of pulmonary lesions on CT have the potential to augment CT thorax interpretation while maintaining diagnostic accuracy and could potentially be harnessed to overcome challenges in the implementation of lung cancer screening programmes.

Background: Sex disparities in the delivery of care and in-hospital outcomes following ST-segment elevation myocardial infarction (STEMI) remain a global concern. We performed an updated global meta-analysis comparing clinical outcomes between males and females with STEMI. Methods: This study was conducted according to PRISMA guidelines (PROSPERO CRD42024612510). We searched major electronic databases from 1 ^st January 2000 to 7 ^th November 2024. Pairwise meta-analysis was performed on outcomes related to time-to-therapy, in-hospital outcomes, and optimal therapy, with subgroup analyses of geographic regions. Meta-regression was performed to evaluate heterogeneity. Results: A total of 69 studies, involving 891,585 patients (262,773 females; 628,401 males) were included. Males were younger (MD -7.387 years), less likely to have diabetes, hypertension, and prior stroke, had significantly lower risk of in-hospital mortality (RR 0.56, 95%CI 0.53 to 0.61, P<0.05) and major bleeding (RR 0.59, 95%CI 0.50 to 0.71, P<0.05). Males had significantly shorter time to first medical contact (MD - 32.42mins), door-to-balloon (MD -6.17mins) and door-to-needle (MD -5.53mins) times, more likely to undergo PCI (OR 1.34, 95%CI 1.20 to 1.48, P<0.05), receive P2Y12 inhibitors (OR 1,52, 95%CI 1.04 to 2.23, P=0.03), GP IIb/IIIa inhibitors (OR 1.30, 95%CI 1.14 to 1.49, P<0.05), and ACE inhibitors (OR 1.41, 95%CI 0.92 to 2.17, P=0.11). No differences were observed for aspirin and beta-blocker use. Meta-regression showed female sex (β = 1.40) and DM (β = 0.78) were positively correlated with mortality. Conclusion: Females with STEMI experience longer treatment delays, worse in-hospital outcomes, and suboptimal care. There is an urgent need to address sex disparities in STEMI care.

The integration of artificial intelligence (AI) into healthcare represents a paradigm shift in both clinical practice and administrative operations. This narrative review examines the transformative role of large language models (LLMs) and generative AI across two intersecting domains. Firstly, clinical applications, including early disease detection, medical imaging, personalized medicine, and chronic disease monitoring. Secondly, administrative functions, encompassing automated documentation, electronic health record (EHR) enhancement, conversational agents, and patient flow optimization. Through a health informatics lens, oncology is used as an illustrative setting, rather than a formal case study, to examine how AI systems engage with clinical reasoning, workflow integration, and enterprise-scale deployment. The trajectory from IBM Watson for Oncology to contemporary platforms such as Tempus and PathAI reflects both the progress achieved and the persistent challenges of scalability, interpretability, and equitable data representation. Emerging LLM-based tools, including ambient documentation systems, have been reported to reduce administrative burden in some studies, though the evidence base remains nascent and context-dependent. Critical limitations are identified across ethical, regulatory, and practical dimensions. Some of these limitations include algorithmic opacity, automation bias, dataset inequity, and the risk that automated note generation may attenuate the cognitive rigor inherent to clinical documentation. Future directions emphasize the development of multimodal and human-centered AI systems evaluated through external and prospective validation and reported in line with guidelines like CONSORT-AI and SPIRIT-AI, alongside robust post-deployment monitoring. Ultimately, oncology’s experience illustrates potentially transferable principles for responsible AI adoption across healthcare specialties, suggesting that sustainable integration depends not solely on technical performance, but on transparency, governance, and meaningful clinician–AI collaboration.