ABSTRACT
Esta cronología es una idea del psicoanalista e investigador francés Théo Lucciardi y fue publicada originalmente en el número 3 de la revista LAPSUS NUMÉRIQUE. Su autor ha preparado esta versión actualizada a 2023 especialmente para este número de Aesthethica. La secuencia, que va desde la invención de la rueda hasta la IA generativa, permite detenernos en los grandes hitos del desarrollo científico tecnológico y a la vez advertir ve el grado de aceleración de la última década. Se pueden reconocer allí varios de los temas que integran la agenda contemporánea en materia de bioética y que están presentes en este número de la revista. Algunos de ellos son cruciales para la lectura ético-analítica que proponemos, como la vigencia de la lógica booleana, la actualización del Test de Turing o el porvenir de la IA y el Chat GPT
This chronology is an initiative of the French psychoanalyst and researcher Théo Lucciardi and was originally published in number 3 of the LAPSUS NUMÉRIQUE magazine. Its author has prepared this updated version to 2023 especially for this issue of Aesthethica. The sequence, which goes from the invention of the wheel to generative AI, allows us to stop at the great milestones of technological scientific development and at the same time notice the degree of acceleration of the last decade. Several of the issues that make up the contemporary agenda in bioethics and that are present in this issue of the magazine can be recognized there. Some of them are crucial for the ethical-analytical reading that we propose, such as the validity of Boolean logic, the updating of the Turing Test or the future of AI and Chat GPT
Subject(s)
History, Ancient , History, 21st Century , Scientific Research and Technological Development , Artificial Intelligence , ChronologyABSTRACT
PURPOSE: To investigate the diagnostic performance of an Artificial Intelligence (AI) system for detection of COVID-19 in chest radiographs (CXR), and compare results to those of physicians working alone, or with AI support. MATERIALS AND METHODS: An AI system was fine-tuned to discriminate confirmed COVID-19 pneumonia, from other viral and bacterial pneumonia and non-pneumonia patients and used to review 302 CXR images from adult patients retrospectively sourced from nine different databases. Fifty-four physicians blind to diagnosis, were invited to interpret images under identical conditions in a test set, and randomly assigned either to receive or not receive support from the AI system. Comparisons were then made between diagnostic performance of physicians working with and without AI support. AI system performance was evaluated using the area under the receiver operating characteristic (AUROC), and sensitivity and specificity of physician performance compared to that of the AI system. RESULTS: Discrimination by the AI system of COVID-19 pneumonia showed an AUROC curve of 0.96 in the validation and 0.83 in the external test set, respectively. The AI system outperformed physicians in the AUROC overall (70% increase in sensitivity and 1% increase in specificity, pâ¯<â¯0.0001). When working with AI support, physicians increased their diagnostic sensitivity from 47% to 61% (pâ¯<â¯0.001), although specificity decreased from 79% to 75% (pâ¯=â¯0.007). CONCLUSIONS: Our results suggest interpreting chest radiographs (CXR) supported by AI, increases physician diagnostic sensitivity for COVID-19 detection. This approach involving a human-machine partnership may help expedite triaging efforts and improve resource allocation in the current crisis.
ABSTRACT
Precision medicine plays a key role in urological oncology practice nowadays, with the breakthrough of the poly (ADP-ribose) polymerase inhibitors (PARPi), which play a critical role in different DNA damage repair (DDR) pathways, the immune checkpoint inhibitors, the genomic expression profiles and current genome manipulation-directed targeted therapy. Information and technology (IT) are set to change the way we assess and treat patients and should be reviewed and discussed. The aim of the present article is to demonstrate a detailed revision on precision medicine, including novel therapeutic targets, genomic markers, genomic stratification of urological patients, and the top-notch technological breakthroughs that could change our clinical practice We performed a review of the literature in four different databases (PubMed, Embase, Lilacs, and Scielo) on any information concerning prostate, bladder, kidney and urothelial cancer novel treatments with PARPi, immune checkpoint inhibitors (ICIs), targeted therapy with fibroblast growth factor receptor inhibitors (FGFRi), and theranostics with prostate-specific membrane antigen (PSMA) targeted monoclonal antibodies. Artificial intelligence, machine learning, and deep learning algorithm in urological practice were also part of the search. We included all articles written in English, published within the past 7 years, that discussed outstanding therapies and genomics in urological cancer and artificial intelligence applied to urology. Meanwhile, we excluded articles with lack of a clear methodology and written in any other language than English. One-hundred and twenty-six articles of interest were found; of these, 65 articles that presented novel treatments of urological neoplasms, discussed precision medicine, genomic expression profiles and biomarkers in urology, and latest deep learning and machine learning algorithms as well as the use of artificial intelligence in urological practice were selected. A critical review of the literature is presented in the present article. Urology is a constantly changing specialty with a wide range of therapeutic breakthroughs, a huge understanding of the genomic expression profiles for each urological cancer and a tendency to use cutting-edge technology to treat our patients. All of these major developments must be analyzed objectively, taking into account costs to the health systems, risks and benefits to the patients, and the legal background that comes with them. A critical analysis of these new technologies and pharmacological breakthroughs should be made before considering changing our clinical practice. Nowadays, research needs to be strengthened to help us improve results in assessing and treating our patients
La medicina de precisión juega un rol fundamental en la práctica clínica de la urologia oncológica en la actualidad, con el desarrollo de los inhibidores de la poli (ADP-ribosa) polimerasa (PARPi), que juegan un papel fundamental en las distintas vías del reparo del ADN dañado (RAD), los inhibidores del punto de chequeo inmune (ICI), los perfiles de expresión genómicos, y la terapia blanco-dirigida a la manipulación genómica. El desarrollo tecnológico y la informática están cambiando la forma como evaluamos y tratamos a los pacientes, y se debe discutir y revisar a detalle. El objetivo de este artículo es hacer una revisión detallada acerca de la medicina de precisión, genómica, y los avances tecnológicos en nuestro campo. Realizamos una revisión de la literatura en cuatro bases de datos diferentes (PubMed, Embase, Lilacs, y Scielo), buscando cualquier información relacionada con cáncer de próstata, vejiga, riñón y carcinoma urotelial, tratamientos novedosos con PARPi, ICI, terapia-blanco con inhibidores del receptor del factor de crecimiento de los fibroblastos (FGFRi) y teragnósticos con anticuerpos monoclonales dirigidos al antígeno de membrana específico de la próstata (AMEP). Inteligencia artificial, aprendizaje de máquinas y algoritmos de aprendizaje profundo en la práctica urológica también fueron revisados. Incluimos artículos escritos en inglés, publicados dentro de los últimos 7 años, que abordaran terapias novedosas y genómica en cáncer urológico e inteligencia artificial aplicada a la urología. Excluimos artículos con falta de una metodología adecuada y escritos en cualquier idioma diferente al inglés. En total, 126 artículos de interés fueron encontrados, y, de estos seleccionamos 65 artículos que reportaban tratamientos novedosos para neoplasias urológicas, discutían medicina de precisión y perfiles de expresión genómica y bio-marcadores en urología, algoritmos de aprendizaje profundo, aprendizaje de máquina, y el uso de inteligencia artificial en la práctica urológica. Se hizo una revisión crítica de la literatura que se presenta en este artículo. La urología es una especialidad constantemente en cambio, con un gran rango de avances terapéuticos, un gran conocimiento de los perfiles de expresión genómica para cada cáncer urológico, y una tendencia a utilizar tecnología de punta para estudiar y tratar a nuestros pacientes. Todos estos desarrollos se deben analizar objetivamente, y hay que tener en cuenta los costos al sistema de salud, los riesgos y beneficios para los pacientes, y el contexto legal que implica cada uno. Hasta la fecha, estos avances tecnológicos y farmacológicos se deben analizar con cautela antes de vernos en la posición de cambiar nuestra práctica clínica. Se debe fortalecer la investigación médica para mejorar los resultados en el tratamiento y abordaje de nuestros pacientes.