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<span id="docs-internal-guid-acff2cf2-7fff-8430-2d34-504b66875518"><span>El concepto de infravivienda puede variar notablemente dependiendo del contexto urbano y social de cada lugar, éste se suele referir a aquel tipo de edificación y/o vivienda cuyos niveles de habitabilidad se encuentran por debajo de unos estándares mínimos establecidos, generalmente, en textos normativos. Su aparición a menudo va asociada a procesos de urbanización irregulares y al uso de vivienda de espacios no concebidos para ello, relacionándose, a menudo con situaciones de vulnerabilidad socioresidencial. El caso que aquí se presenta se centra en el barrio del Carmel en Barcelona, caracterizado por un crecimiento urbano en pendiente y la sospecha de la existencia situaciones de infravivienda. Se plantean como objetivos profundizar en el conocimiento de las condiciones de habitabilidad y estado de conservación de las viviendas del barrio para realizar un diagnóstico global de él. Para ello se diseña y emplea una metodología de estudio basada en una combinación de explotación de datos a partir de indicadores y campañas in situ. Los resultados permiten la detección de situaciones de infravivienda, la caracterización de las principales situaciones que la provocan y la categorización y priorización en la necesidad de actuación e intervención por parte de programas públicos de ayuda a la rehabilitación.</span></span>

BACKGROUND Chest X-rays are the most commonly used type of X-rays today, accounting for up to 26% of all radiographic tests performed. However, chest radiography is a complex imaging modality to interpret; several studies have reported discrepancies in chest X-ray interpretations among emergency physicians and radiologists. It is of vital importance to be able to offer a fast and reliable diagnosis for this kind of X-ray, using artificial intelligence (AI) to support the clinician. Oxipit has developed an AI algorithm for reading chest X-rays, available through a web platform called ChestEye. This platform is an automatic computer-aided diagnosis (CAD) system where a reading of the inserted chest X-ray is performed and an automatic report is returned with a capacity to detect 75 pathologies, covering 90% of diagnoses. OBJECTIVE The overall objective of the study is to perform a validation with prospective data of the ChestEye algorithm as a diagnostic aid. We wish to validate the algorithm for a single pathology and multiple pathologies by evaluating the accuracy, sensitivity and specificity of the algorithm. METHODS A prospective study will be carried out to compare the diagnosis of the reference radiologist for the users attending the primary care centre in the Osona region (Spain), with the diagnosis of the ChestEye AI algorithm. RESULTS Patient recruitment began in February 2022 on a rolling basis until the target sample was reached. It is hoped to obtain sufficient evidence to demonstrate that the use of AI in the reading of chest X-rays can be a good tool for diagnostic support. However, there is a decreasing number of radiology professionals and, therefore, it is necessary to develop and validate tools to support professionals who have to interpret these tests. CONCLUSIONS If the results of the validation of the model are satisfactory, it could be implemented as a support tool and allow an increase in the accuracy and speed of diagnosis, patient safety and agility in the primary care system, and reduce the costs of unnecessary tests. CLINICALTRIAL 21/288