Artificial Intelligence Screening Research Articles

The Impact of Artificial Intelligence Replacing Humans in Making Human Resource Management Decisions on Fairness: A Case of Resume Screening

A growing number of organizations have used artificial intelligence (AI) to make decisions to replace human resource (HR) workers; yet, the fairness perceptions of the people affected by the decision are still unclear. Given that an organization’s sustainability is significantly influenced by individuals’ perceptions of fairness, this study takes a resume-screening scenario as an example to explore the impact of AI replacing humans on applicants’ perceptions of fairness. This study adopts the method of the online scenario experiment and uses SPSS to analyze the experimental data: 189 and 214 people, respectively, participated in two online scenarios, with two independent variables of decision makers (AI and humans), two dependent variables of procedural and distributive fairness, and two moderating variables of outcome favorability and the expertise of AI. The results show that the applicants tend to view AI screening resumes as less fair than humans. Furthermore, moderating effects exist between the outcome favorability and the expertise of AI. This study reveals the impact of AI substituting for humans in decision-making on fairness. The proposed model can help organizations use AI to screen resumes more effectively. And future research can explore the collaboration between humans and AI to make human resource management decisions.

Cost-effectiveness and cost-utility of a digital technology-driven hierarchical healthcare screening pattern in China

Utilization of digital technologies for cataract screening in primary care is a potential solution for addressing the dilemma between the growing aging population and unequally distributed resources. Here, we propose a digital technology-driven hierarchical screening (DH screening) pattern implemented in China to promote the equity and accessibility of healthcare. It consists of home-based mobile artificial intelligence (AI) screening, community-based AI diagnosis, and referral to hospitals. We utilize decision-analytic Markov models to evaluate the cost-effectiveness and cost-utility of different cataract screening strategies (no screening, telescreening, AI screening and DH screening). A simulated cohort of 100,000 individuals from age 50 is built through a total of 30 1-year Markov cycles. The primary outcomes are incremental cost-effectiveness ratio and incremental cost-utility ratio. The results show that DH screening dominates no screening, telescreening and AI screening in urban and rural China. Annual DH screening emerges as the most economically effective strategy with 341 (338 to 344) and 1326 (1312 to 1340) years of blindness avoided compared with telescreening, and 37 (35 to 39) and 140 (131 to 148) years compared with AI screening in urban and rural settings, respectively. The findings remain robust across all sensitivity analyses conducted. Here, we report that DH screening is cost-effective in urban and rural China, and the annual screening proves to be the most cost-effective option, providing an economic rationale for policymakers promoting public eye health in low- and middle-income countries.

Clinical and genetic studies for a cohort of patients with Leber congenital amaurosis.

Leber congenital amaurosis (LCA) is a group of early-onset retinal degenerative disorders, resulting in blindness in children. This study aimed to describe the clinical and genetic characteristics of a cohort of patients with LCA and to investigate the retinal vascular characteristics in LCA patients. Fifty-two children with LCA were included in the study. All patients underwent detailed ocular examinations. Electroretinography (ERG) was used to evaluate the retinal function. Optical coherence tomography (OCT) was used to assess the structure change of the retina for those patients who were able to cooperate very well. Panel-based next-generation sequencing was performed to identify pathogenic variants in genes associated with LCA. Diameters of the retinal vessels were measured using the EVision AI screening system with an artificial intelligence (AI) technique. An ultrasound Doppler was used to evaluate hemodynamic parameters, including peak systolic velocity (PSV), resistive index (RI), and pulsatility index (PI), in the ophthalmic, central retinal, posterior ciliary, carotid, and internal carotid as well as external carotid arteries in 12 patients aged from 3 to 14years. We detected 75 pathogenic variants from ten genes of RPGRIP1, CEP290, GUCY2D, LCA5, AIPL1, CRB1, RPE65, CRX, RDH12, and TULP1, including 29 novel and 36 previously reported variants in 52 affected children with LCA, with the highest detective rate in RPGRIP1 (26.9%). Fundus appearance is diverse in patients with LCA, ranging from normal to severe peripheral or central retinopathy. Retinal vasculature was evaluated in 12 patients with different gene variants, showing narrowed arteries with an average diameter of 43.6 ± 3.8μm compared to that of 51.7 ± 2.6μm in the normal controls (P < 0.001, n = 12). Meanwhile, their hemodynamic parameters were changed as well in the ophthalmic artery (OA), with a decreased PSV (P = 0.0132, n = 12) and slightly increased PI (P = 0.0488, n = 12) compared to the normal controls. However, the hemodynamic parameters did not change significantly in the other vessels. Blood supply to the eyeball is predicted to be reduced in patients with LCA, presumably due to photoreceptor cell degeneration. The novel identified variants will expand the spectrum of variants in LCA-related genes and be useful for studying the molecular mechanisms of LCA.

An Economic Analysis for the Use of Artificial Intelligence in Screening for Diabetic Retinopathy in Trinidad and Tobago.

This is a systematic review of 25 publications on the topics of the prevalence and cost of diabetic retinopathy (DR) in Trinidad and Tobago, the cost of traditional methods of screening for DR, and the use and cost of artificial intelligence (AI) in screening for DR. Analysis of these publications was used to identify and make estimates for how resources allocated to ophthalmology in public health systems in Trinidad and Tobago can be more efficiently utilized by employing AI in diagnosing treatable DR. DR screening was found to be an effective method of detecting the disease. Screening wasfound to be a universally cost-effective method of disease prevention and for altering the natural history of the disease in the spectrum of low-middle to high-income economies, such as Rwanda, Thailand, China, South Korea, and Singapore. AI and deep learning systems were found to be clinically superior to, or as effective as, human graders in areas where they were deployed, indicating that the systems are clinically safe. They have been shown to improve access to diabetic retinal screening, improve compliance with screening appointments, and prove to be cost-effective, especially in rural areas. Trinidad and Tobago, which is estimated to be disproportionately more affected by the burden of DR when projected out to the mid-21st century, stands to save as much as US$60 million annually from the implementation of an AI-based system to screen for DR versus conventional manual grading.

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

&lt;abstract&gt; &lt;p&gt;Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.&lt;/p&gt; &lt;/abstract&gt;

Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis.

Retrospective studies on artificial intelligence (AI) in screening for diabetic retinopathy (DR) have shown promising results in addressing the mismatch between the capacity to implement DR screening and increasing DR incidence. This review sought to evaluate the diagnostic test accuracy (DTA) of AI in screening for referable diabetic retinopathy (RDR) in real-world settings. We searched CENTRAL, PubMed, CINAHL, Scopus, and Web of Science on 9 February 2023. We included prospective DTA studies assessing AI against trained human graders (HGs) in screening for RDR in patients with diabetes. Two reviewers independently extracted data and assessed methodological quality against QUADAS-2 criteria. We used the hierarchical summary receiver operating characteristics (HSROC) model to pool estimates of sensitivity and specificity and, forest plots and SROC plots to visually examine heterogeneity in accuracy estimates. From our initial search results of 3899 studies, we included 15 studies comprising 17 datasets. Meta-analyses revealed a sensitivity of 95.33% (95%CI: 90.60-100%) and specificity of 92.01% (95%CI: 87.61-96.42%) for patient-level analysis (10 datasets, N = 45,785) while, for the eye-level analysis, sensitivity was 91.24% (95%CI: 79.15-100%) and specificity, 93.90% (95%CI: 90.63-97.16%) (7 datasets, N = 15,390). Subgroup analyses did not provide variations in the diagnostic accuracy of country classification and DR classification criteria. However, a moderate increase was observed in diagnostic accuracy in the primary-level healthcare settings: sensitivity of 99.35% (95%CI: 96.85-100%), specificity of 93.72% (95%CI: 88.83-98.61%) and, a minimal decrease in the tertiary-level healthcare settings: sensitivity of 94.71% (95%CI: 89.00-100%), specificity of 90.88% (95%CI: 83.22-98.53%). Sensitivity analyses did not show any variations in studies that included diabetic macular edema in the RDR definition, nor studies with ≥3 HGs. This review provides evidence, for the first time from prospective studies, for the effectiveness of AI in screening for RDR in real-world settings. The results may serve to strengthen existing guidelines to improve current practices.

Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs.

Significant visual impairment due to glaucoma is largely caused by the disease being detected too late. To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG). Cross-sectional study. Color fundus photographs (CFPs) of 113 893 eyes of 60 357 individuals were obtained from EyePACS, California, United States, from a population screening program for diabetic retinopathy. Carefully selected graders (ophthalmologists and optometrists) graded the images. To qualify, they had to pass the European Optic Disc Assessment Trial optic disc assessment with ≥ 85% accuracy and 92% specificity. Of 90 candidates, 30 passed. Each image of the EyePACS set was then scored by varying random pairs of graders as "RG," "no referable glaucoma (NRG)," or "ungradable (UG)." In case of disagreement, a glaucoma specialist made the final grading. Referable glaucoma was scored if visual field damage was expected. In case of RG, graders were instructed to mark up to 10 relevant glaucomatous features. Qualitative features in eyes with RG. The performance of each grader was monitored; if the sensitivity and specificity dropped below 80% and 95%, respectively (the final grade served as reference), they exited the study and their gradings were redone by other graders. In all, 20 graders qualified; their mean sensitivity and specificity (standard deviation [SD]) were 85.6% (5.7) and 96.1% (2.8), respectively. The 2 graders agreed in 92.45% of the images (Gwet's AC2, expressing the inter-rater reliability, was 0.917). Of all gradings, the sensitivity and specificity (95% confidence interval) were 86.0 (85.2-86.7)% and 96.4 (96.3-96.5)%, respectively. Of all gradable eyes (n= 111 183; 97.62%) the prevalence of RG was 4.38%. The most common features of RG were the appearance of the neuroretinal rim (NRR) inferiorly and superiorly. A large data set of CFPs was put together of sufficient quality to develop AI screening solutions for glaucoma. The most common features of RG were the appearance of the NRR inferiorly and superiorly. Disc hemorrhages were a rare feature of RG. Proprietary or commercial disclosure may be found after the references.

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda

BackgroundEvidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.MethodsConsented participants were screened for DR using retinal imaging with AI interpretation from March 2021 to...

Remote diagnostic imaging using artificial intelligence for diagnosing hip dysplasia in infants: Results from a mixed-methods feasibility pilot study.

Infant hip dysplasia or Developmental Dysplasia of the Hip (DDH) occurs in 1-2% of births worldwide and leads to hip arthritis if untreated. We sought to evaluate the feasibility of implementing an artificial intelligence-enhanced portable ultrasound tool for infant hip dysplasia (DDH) screening in primary care, through determining its effectiveness in practice and evaluating patient and provider feedback. A US-FDA-cleared artificial intelligence (AI) screening device for DDH (MEDO-Hip) was added to routine well-child visits from age 6 to 10 weeks. A total of 306 infants were screened during a 1-year pilot study within three family medicine clinics in Alberta, Canada. Patient and provider satisfaction were quantified using the System Usability Survey (SUS), while provider perceptions were further investigated through semi-structured interviews. Provider and user surveys commonly identified best features of the tool as immediate diagnosis, offering reassurance/knowledge and avoiding travel, and noted technical glitches most frequently as a barrier. A total of 369 scans of 306 infants were performed from Feb 1, 2021 until Mar 31, 2022. Eighty percent of hips scanned were normal on initial scans, 14% of scans required a follow-up study in the primary care clinic, and DDH cases were identified and treated at the expected 2% rate (6 infants). It is feasible to implement a point-of-care ultrasound AI screening tool in primary care to screen for infants with DDH. Beyond improved screening and detection, this innovation was well accepted by patients and fee-for-service providers with a culture and history of innovation.

Readiness for Artificial Intelligence in Biobanking

Readiness for Artificial Intelligence in Biobanking

An overview of artificial intelligence use in diabetic retinopathy treatment: a narrative review

Background: Artificial intelligence (AI), usually called machine intelligence, is a scientific field that allows robots thinking like people. The most common diseases that cause visual impairment and blindness, such as age-related macular degeneration (ARMD), cataract, glaucoma, and diabetic retinopathy (DR), have recently been subject to deep learning-based on AI screening and prediction models. Aim: to discuss the core ideas of AI and its apply to DR, as well as to evaluate the currently faced issues and the future of ophthalmology. Methods: In this review, English studies from common databases such as Pubmed/MEDLINE, Google Scholar, Web of Science, Scopus, and the Cochrane Library with the keywords "Machine Learning," "Artificial Intelligence," "Deep Learning," combined with keywords, involving "diabetic retinopathy" were involved. The end date for this review is April 2022. Scientific novelty: This paper illustrates the core AI concepts and their application in diabetic retinopathy. The current ophthalmology issues and future opportunities, offering undiscovered knowledge in this field are also analysed. It will raise community knowledge of employing AI and reveal new capabilities in the analysis of ocular disorders to present the fundamental idea of AI regarding its therapeutic applications. Conclusion: Medical professionals can make quick and precise decisions using AI technologies in orded to analyse massive volumes of data, such as physiological imaging and clinical presentations. It is believed that over time, AI systems will become more precise and successful at predicting the onset and course of DR.

Artificial intelligence in screening for obstructive sleep apnoea syndrome (OSAS): a narrative review

Artificial intelligence in screening for obstructive sleep apnoea syndrome (OSAS): a narrative review

AI telemedicine screening in ophthalmology: health economic considerations

AI telemedicine screening in ophthalmology: health economic considerations

Diabetic retinopathy: Looking forward to 2030.

Diabetic retinopathy (DR) is the major ocular complication of diabetes mellitus, and is a problem with significant global health impact. Major advances in diagnostics, technology and treatment have already revolutionized how we manage DR in the early part of the 21st century. For example, the accessibility of imaging with optical coherence tomography, and the development of anti-vascular endothelial growth factor (VEGF) treatment are just some of the landmark developments that have shaped the DR landscape over the last few decades. Yet, there are still more exciting advances being made. Looking forward to 2030, many of these ongoing developments are likely to further transform the field. First, epidemiologic projections show that the global burden of DR is not only increasing, but also shifting from high-income countries towards middle- and low-income areas. Second, better understanding of disease pathophysiology is placing greater emphasis on retinal neural dysfunction and non-vascular aspects of diabetic retinal disease. Third, a wealth of information is becoming available from newer imaging modalities such as widefield imaging systems and optical coherence tomography angiography. Fourth, artificial intelligence for screening, diagnosis and prognostication of DR will become increasingly accessible and important. Fifth, new pharmacologic agents targeting other non-VEGF-driven pathways, and novel therapeutic strategies such as gene therapy are being developed for DR. Finally, the classification system for diabetic retinal disease will need to be continually updated to keep pace with new developments. In this article, we discuss these major trends in DR that we expect to see in 2030 and beyond.

Leveraging artificial intelligence for the management of postoperative delirium following cardiac surgery

BACKGROUND Postoperative delirium is a highly relevant complication of cardiac surgery. It is associated with worse outcomes and considerably increased costs of care. A novel approach of monitoring patients with machine learning enabled prediction software could trigger pre-emptive implementation of mitigation strategies as well as timely intervention. OBJECTIVE This study evaluates the predictive accuracy of an artificial intelligence (AI) model for anticipating postoperative delirium by comparing it to established standards and measures of risk and vulnerability. DESIGN Retrospective predictive accuracy study. SETTING Records were gathered from a database for anaesthesia quality assurance at a specialised heart surgery centre in Germany. PATIENTS Between January and July 2021, 131 patients had been enrolled into the database and had data available for AI prediction modelling. After exclusion of incomplete follow-ups, a subset of 114 was included in the statistical analysis. MAIN OUTCOME MEASURES Delirium was diagnosed with the Confusion Assessment Method for the ICU (CAM-ICU) over three days postoperatively with specific follow-up visits. AI predictions were also compared with risk assessment through a frailty screening, a Shulman Clock Drawing Test, and using a checklist of predisposing factors including comorbidity, reduced mobility, and substance abuse. RESULTS Postoperative delirium was diagnosed in 23.7% of patients. Postoperative AI screening exhibited reasonable performance with an area under the receiver operating curve (AUROC) of 0.79, 95% confidence interval (CI), 0.69–0.87. But pre-operative prediction was weak for all methods (AUROC range from 0.55 to 0.66). There were significant associations with postoperative delirium: open heart surgery versus endovascular valve replacement (33.3% vs. 10.4%, P &lt; 0.01), postinterventional hospitalisation (12.8 vs. 8.6 days, P &lt; 0.01), and length of ICU stay (1.7 vs. 0.3 days, P &lt; 0.01) were all significantly associated with postoperative delirium. CONCLUSION AI is a promising approach with considerable potential and delivered noninferior results compared with the usual approach of structured evaluation of risk factors and questionnaires. Since these established methods do not provide the desired confidence level, improved AI may soon deliver a better performance. TRIAL REGISTRATION None.

Impact of Artificial Intelligence Assessment of Diabetic Retinopathy on Referral Service Uptake in a Low-Resource Setting: The RAIDERS Randomized Trial.

This trial was designed to determine if artificial intelligence (AI)-supported diabetic retinopathy (DR) screening improved referral uptake in Rwanda. The Rwanda Artificial Intelligence for Diabetic Retinopathy Screening (RAIDERS) study was an investigator-masked, parallel-group randomized controlled trial. Patients ≥ 18 years of age with known diabetes who required referral for DR based on AI interpretation. The RAIDERS study screened for DR using retinal imaging with AI interpretation implemented at 4 facilities from March 2021 through July 2021. Eligible participants were assigned randomly (1:1) to immediate feedback of AI grading (intervention) or communication of referral advice after human grading was completed 3 to 5 days after the initial screening (control). Difference between study groups in the rate of presentation for referral services within 30 days of being informed of the need for a referral visit. Of the 823 clinic patients who met inclusion criteria, 275 participants (33.4%) showed positive findings for referable DR based on AI screening and were randomized for inclusion in the trial. Study participants (mean age, 50.7 years; 58.2% women) were randomized to the intervention (n= 136 [49.5%]) or control (n= 139 [50.5%]) groups. No significant intergroup differences were found at baseline, and main outcome data were available for analyses for 100% of participants. Referral adherence was statistically significantly higher in the intervention group (70/136 [51.5%]) versus the control group (55/139 [39.6%]; P= 0.048), a 30.1% increase. Older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.05; P < 0.0001), male sex (OR, 2.07; 95% CI, 1.22-3.51; P= 0.007), rural residence (OR, 1.79; 95% CI, 1.07-3.01; P= 0.027), and intervention group (OR, 1.74; 95% CI, 1.05-2.88; P= 0.031) were statistically significantly associated with acceptance of referral in multivariate analyses. Immediate feedback on referral status based on AI-supported screening was associated with statistically significantly higher referral adherence compared with delayed communications of results from human graders. These results provide evidence for an important benefit of AI screening in promoting adherence to prescribed treatment for diabetic eye care in sub-Saharan Africa.

Leveraging Artificial Intelligence to Enhance Peer Review: Missed Liver Lesions on Computed Tomographic Pulmonary Angiography.

The aim of this study was to use artificial intelligence (AI) to facilitate peer review for detection of missed suspicious liver lesions (SLLs) on CT pulmonary angiographic (CTPA) examinations. This retrospective study included 1 month of consecutive CTPA examinations from a multisite teleradiology practice. Visual classification (VC) software analyzed images for the presence (+) or absence (-) of SLLs (>1 cm, >20 Hounsfield units). Separately, a natural language processing (NLP) algorithm evaluated corresponding reports for description (+) of an SLL or lack thereof (-). Studies containing possible missed SLLs (VC+/NLP-) were reviewed by three abdominal radiologists in a two-step adjudication process to confirm if an SLL was missed by the interpreting radiologist. The number of VC+/NLP- cases, the number of images needing radiologist review, and the number of cases with confirmed missed SLLs were recorded. Interobserver agreement for SLLs was calculated for the radiologist readers. A total of 2,573 CTPA examinations were assessed, and 136 were classified as potentially containing missed SLLs (VC+/NLP-). After radiologist review, 13 cases with missed SLLs were confirmed, representing 0.5% of analyzed CT studies. Using AI, the ratio of CT studies requiring review to missed SLLs identified was 10:1; the ratio without the help of AI would be at least 66:1. Among the 136 cases reviewed by radiologists, interobserver agreement for SLLs was excellent (κ= 0.91). AI can accelerate meaningful peer review by rapidly assessing thousands of examinations to identify potentially clinically significant errors. Although radiologist involvement is necessary, the amount of effort required after initial AI screening is dramatically reduced.

Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes-A Systematic Review.

(1) Background: AI-based solutions could become crucial for the prediction of pregnancy disorders and complications. This study investigated the evidence for applying artificial intelligence methods in obstetric pregnancy risk assessment and adverse pregnancy outcome prediction. (2) Methods: Authors screened the following databases: Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar. This study included all the evaluative studies comparing artificial intelligence methods in predicting adverse pregnancy outcomes. The PROSPERO ID number is CRD42020178944, and the study protocol was published before this publication. (3) Results: AI application was found in nine groups: general pregnancy risk assessment, prenatal diagnosis, pregnancy hypertension disorders, fetal growth, stillbirth, gestational diabetes, preterm deliveries, delivery route, and others. According to this systematic review, the best artificial intelligence application for assessing medical conditions is ANN methods. The average accuracy of ANN methods was established to be around 80-90%. (4) Conclusions: The application of AI methods as a digital software can help medical practitioners in their everyday practice during pregnancy risk assessment. Based on published studies, models that used ANN methods could be applied in APO prediction. Nevertheless, further studies could identify new methods with an even better prediction potential.

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Feasibility of using artificial intelligence for screening COVID-19 patients in ParaguayViabilidade do uso de inteligência artificial na triagem de pacientes com COVID-19 no Paraguai

RESUMENObjetivo.Estudiar la factibilidad de utilización de la inteligencia artificial como método sensible y específico para el cribado de COVID-19 en pacientes con afecciones respiratorias empleando imágenes de tórax obtenidas con tomógrafo y una plataforma de telemedicina.Métodos.Entre marzo del 2020 y junio del 2021 se realizó un estudio observacional descriptivo multicéntrico de factibilidad basada en inteligencia artificial (IA) para el cribado de COVID-19 en imágenes de tórax de pacientes con afecciones respiratorias que acudieron a hospitales públicos. El diagnóstico de las imágenes tomográficas de tórax se realizó a través de la plataforma de IA; luego, se comparó con el diagnóstico molecular (RT-PCR) para determinar la concordancia entre ambos y analizar su factibilidad para el cribado de pacientes con sospecha de COVID-19. Las imágenes y los resultados diagnóstico se enviaron a través de una plataforma de telemedicina.Resultados.Se realizó el cribado de 3 514 pacientes con sospecha diagnóstica de COVID-19, en 14 hospitales a nivel nacional. La mayoría de los pacientes tenían entre 27 y 59 años, seguidos por los mayores de 60 años. La edad promedio fue de 48,6 años; el 52,8% eran de sexo masculino. Los hallazgos más frecuentes fueron neumonía grave, neumonía bilateral con derrame pleural, enfisema pulmonar bilateral y opacidad difusa en vidrio esmerilado, entre otros. Se determinó un promedio de 93% de concordancia y 7% de discordancia entre las imágenes analizadas mediante IA y la RT-PCR. La sensibilidad y especificidad del sistema de IA, obtenidas comparando el resultado del cribado obtenido por IA con la RT-PCR, fueron de 93% y 80% respectivamente.Conclusiones.Es viable la utilización de IA sensible y específica para la detección rápida estratificada de COVID-19 en pacientes con afecciones respiratorias utilizando imágenes obtenidas mediante tomografía de tórax y una plataforma de telemedicina en los hospitales públicos de Paraguay.