Objective: The rapid digitisation of healthcare data and the sheer volume being generated means that artificial intelligence (AI) is becoming a new reality in the practice of medicine. For this reason, describing the perception of primary care (PC) healthcare professionals on the use of AI as a healthcare tool and its impact in radiology is crucial to ensure its successful implementation. Methods: Observational cross-sectional study, using the validated Shinners Artificial Intelligence Perception survey, aimed at all PC medical and nursing professionals in the health region of Central Catalonia. Results: The survey was sent to 1068 health professionals, of whom 301 responded. And 85.7% indicated that they understood the concept of AI but there were discrepancies in the use of this tool; 65.8% indicated that they had not received any AI training and 91.4% that they would like to receive training. The mean score for the professional impact of AI was 3.62 points out of 5 (standard deviation (SD) = 0.72), with a higher score among practitioners who had some prior knowledge of and interest in AI. The mean score for preparedness for AI was 2.76 points out of 5 (SD = 0.70), with higher scores for nursing and those who use or do not know if they use AI. Conclusions: The results of this study show that the majority of professionals understood the concept of AI, perceived its impact positively, and felt prepared for its implementation. In addition, despite being limited to a diagnostic aid, the implementation of AI in radiology was a high priority for these professionals.

Objective: The primary objective was to investigate the relationship between periodontitis and hypertension in two independent large surveys. The secondary objective was to ascertain whether systemic inflammation had a mediation effect in the association.Methods: This cross‐sectional study analysed representative samples of the US (n = 3460; NHANES 2009/10) and Korean (n = 4539; 2015 KNHANES VI‐3) populations. The association between periodontitis (exposure), hypertension (outcome) and inflammatory markers [C‐reactive protein (CRP) and white blood cell counts (WBC)] (mediators) was assessed using multivariate linear and logistic regression models and mediation analysis.Results: Participants with periodontitis were more likely to have hypertension (NHANES: OR = 1.3, 95% CI: 1.0–1.6, P = 0.025; KNHANES: OR = 1.2, 95% CI: 1.0–1.4, P = 0.041) and actual systolic blood pressure ≥ 140 mmHg (NHANES: OR = 1.6, 95% CI: 1.1–2.3, P < 0.001; KNHANES: OR = 1.3, 95% CI :1.0–1.6, P < 0.031) than those without the disease. These associations were independent of age, gender, BMI, education level, smoking, alcohol consumption, creatinine, physical activity, presence of other comorbidities and confirmed in participants not taking antihypertensive medications. Diagnosis of periodontitis was directly associated with WBC (in both surveys: NHANES: β ± SE = 0.3 ± 0.1, P < 0.004; KNHANES: β ± SE = 0.3 ± 0.1, P < 0.001) and with CRP levels (in one survey: NHANES: β ± SE = 0.1 ± 0.03, P < 0.007; KNHANES: β ± SE = 0.1 ± 0.04, P > 0.213). Mediation analyses confirmed that CRP acted as a mediator in the association between periodontitis and hypertension in both populations (mediated effect: NHANES: β ± SE = 0.010 ± 0.003, P < 0.001; KNHANES: β ± SE = 0.003 ± 0.001, P = 0.015). WBC acted as a mediator in the KNHANES (mediated effect: β ± SE = 0.004 ± 0.001, P = 0.004) whilst in the NHANES, its effect was dependent of CRP inclusion in the model (mediated effect WBC + CRP: β ± SE = 0.002 ± 0.001, P = 0.001).Conclusions: These findings suggest that periodontitis is closely linked to hypertension and systemic inflammation is, in part, a mediator of this association.

Recent evidence suggests hypertension and periodontitis are closely linked but limited data is available on the nature of the association. We aimed to investigate the relationship between periodontitis and mean arterial blood pressure in a sample of otherwise systemically healthy individuals. A case-control study including 250 cases (participants with periodontitis) and 250 controls (without periodontitis) was designed from a register of clinical trials conducted between 2000 and 2018 in a university setting. Cases were age, sex, and body mass index balanced with controls. Linear, logistic regression, and mediation models were planned to test the association between various periodontal measures and arterial blood pressure. We further investigated the role of systemic inflammation assessed by hs-CRP (high-sensitivity C-reactive protein) and white cell counts. Cases presented with 3.36 mm Hg (95% CI, 0.91–5.82, P =0.007) higher mean systolic blood pressure and 2.16 mm Hg (95% CI, 0.24–4.08, P =0.027) higher diastolic blood pressure than controls. Diagnosis of periodontitis was associated with mean systolic blood pressure (β=3.46±1.25, P =0.005) and greater odds of systolic blood pressure ≥140 mm Hg (odds ratio, 2.3 [95% CI, 1.15–4.60], P =0.018) independent of common cardiovascular risk factors. Similar findings were observed when continuous measures of periodontal status were modeled against systolic blood pressure. Measures of systemic inflammation although elevated in periodontitis were not found to be mediators of the association between periodontitis and arterial blood pressure values. Periodontitis is linked to higher systolic blood pressure in otherwise healthy individuals. Promotion of periodontal and systemic health strategies in the dental and medical setting could help reduce the burden of hypertension and its complications.

The prevalence of dermatological conditions in primary care, coupled with challenges such as dermatologist shortages and rising consultation costs, highlights the need for innovative solutions. Artificial intelligence (AI) holds promise for improving the diagnostic analysis of skin lesion images, potentially enhancing patient care in primary settings. This systematic review following PRISMA guidelines examined primary studies (2012–2022) assessing AI algorithms’ diagnostic accuracy for skin diseases in primary care. Studies were screened for eligibility based on their availability in the English language and exclusion criteria, with risk of bias evaluated using QUADAS-2. PubMed, Scopus, and Web of Science were searched. Fifteen studies (2019–2022), primarily from Europe and the USA, focusing on diagnostic accuracy were included. Sensitivity ranged from 58% to 96.1%, with accuracies varying from 0.41 to 0.93. AI applications encompassed triage and diagnostic support across diverse skin conditions in primary care settings, involving both patients and primary care professionals. While AI demonstrates potential for enhancing the accuracy of skin disease diagnostics in primary care, further research is imperative to address study heterogeneity and ensure algorithm reliability across diverse populations. Future investigations should prioritise robust dataset development and consider representative patient samples. Overall, AI may improve dermatological diagnosis in primary care, but careful consideration of algorithm limitations and implementation strategies is required.

Background: Artificial Intelligence (AI) has been developing for decades, but in recent years its use in the field of health care has experienced an exponential increase. Currently, there is little doubt that these tools have transformed clinical practice. Therefore, it is important to know how the population perceives its implementation to be able to propose strategies for acceptance and implementation and to improve or prevent problems arising from future applications. Objective: This study aims to describe the population’s perception and knowledge of the use of AI as a health support tool and its application to radiology through a validated questionnaire, in order to develop strategies aimed at increasing acceptance of AI use, reducing possible resistance to change and identifying possible sociodemographic factors related to perception and knowledge. Methods: A cross-sectional observational study was conducted using an anonymous and voluntarily validated questionnaire aimed at the entire population of Catalonia aged 18 years or older. The survey addresses 4 dimensions defined to describe users’ perception of the use of AI in radiology, (1) “distrust and accountability,” (2) “personal interaction,” (3) “efficiency,” and (4) “being informed,” all with questions in a Likert scale format. Results closer to 5 refer to a negative perception of the use of AI, while results closer to 1 express a positive perception. Univariate and bivariate analyses were performed to assess possible associations between the 4 dimensions and sociodemographic characteristics. Results: A total of 379 users responded to the survey, with an average age of 43.9 (SD 17.52) years and 59.8% (n=226) of them identified as female. In addition, 89.8% (n=335) of respondents indicated that they understood the concept of AI. Of the 4 dimensions analyzed, “distrust and accountability” obtained a mean score of 3.37 (SD 0.53), “personal interaction” obtained a mean score of 4.37 (SD 0.60), “efficiency” obtained a mean score of 3.06 (SD 0.73) and “being informed” obtained a mean score of 3.67 (SD 0.57). In relation to the “distrust and accountability” dimension, women, people older than 65 years, the group with university studies, and the population that indicated not understanding the AI concept had significantly more distrust in the use of AI. On the dimension of “being informed,” it was observed that the group with university studies rated access to information more positively and those who indicated not understanding the concept of AI rated it more negatively. Conclusions: The majority of the sample investigated reported being familiar with the concept of AI, with varying degrees of acceptance of its implementation in radiology. It is clear that the most conflictive dimension is “personal interaction,” whereas “efficiency” is where there is the greatest acceptance, being the dimension in which there are the best expectations for the implementation of AI in radiology.