Computer technologies to integrate medical treatments to manage multimorbidity.

The high prevalence of multimorbid cases is a challenge for Health-Care Systems today. Clinical practice guidelines are the means to register and transmit the available evidence-based medical knowledge concerning concrete diseases. Several computer languages have been defined to represent this knowledge in a way that computers could use to help physicians in the daily practice of medicine. The generation of guidelines for all possible multimorbidities entails several issues that are difficult to address. Consequently, numerous medical informatics technologies have appeared merging computer information structures in a way that the treatment knowledge about single diseases could be combined in order to deliver health-care to patients suffering from multimorbidity. This paper proposes a classification of the most promising current technologies addressing this issue and provides an analysis of their maturity, strengths, and weaknesses. We conclude with an enumeration of ten relevant issues to consider when developing such technologies.

Evaluating the Efficacy of ChatGPT in Navigating the Spanish Medical Residency Entrance Examination (MIR): Promising Horizons for AI in Clinical Medicine.

The rapid progress in artificial intelligence, machine learning, and natural language processing has led to increasingly sophisticated large language models (LLMs) for use in healthcare. This study assesses the performance of two LLMs, the GPT-3.5 and GPT-4 models, in passing the MIR medical examination for access to medical specialist training in Spain. Our objectives included gauging the model's overall performance, analyzing discrepancies across different medical specialties, discerning between theoretical and practical questions, estimating error proportions, and assessing the hypothetical severity of errors committed by a physician. MATERIAL AND METHODS: We studied the 2022 Spanish MIR examination results after excluding those questions requiring image evaluations or having acknowledged errors. The remaining 182 questions were presented to the LLM GPT-4 and GPT-3.5 in Spanish and English. Logistic regression models analyzed the relationships between question length, sequence, and performance. We also analyzed the 23 questions with images, using GPT-4's new image analysis capability. RESULTS: GPT-4 outperformed GPT-3.5, scoring 86.81% in Spanish (p < 0.001). English translations had a slightly enhanced performance. GPT-4 scored 26.1% of the questions with images in English. The results were worse when the questions were in Spanish, 13.0%, although the differences were not statistically significant (p = 0.250). Among medical specialties, GPT-4 achieved a 100% correct response rate in several areas, and the Pharmacology, Critical Care, and Infectious Diseases specialties showed lower performance. The error analysis revealed that while a 13.2% error rate existed, the gravest categories, such as "error requiring intervention to sustain life" and "error resulting in death", had a 0% rate. CONCLUSIONS: GPT-4 performs robustly on the Spanish MIR examination, with varying capabilities to discriminate knowledge across specialties. While the model's high success rate is commendable, understanding the error severity is critical, especially when considering AI's potential role in real-world medical practice and its implications for patient safety.

Sistemas de entrega de fármacos autoemulsificables: una plataforma de desarrollo alternativa para la industria farmacéutica colombiana / Self-emulsifying drug delivery systems (SEDDS): an alternative development platform for the Colombian pharmaceutical industry

RESUMEN Los grandes avances tecnológicos en la industria farmacéutica, que involucran el uso de la química combinatoria y el cribado de alto rendimiento, han conllevado al descubrimiento de muchas entidades químicas candidatas a fármacos que presentan baja solubilidad acuosa, debido a su elevada complejidad molecular, lo que hace difícil el desarrollo de productos con estas sustancias. Los sistemas de entrega de fármacos autoemulsificables (SEDDS) han generado un interés para el desarrollo farmacéutico porque son una alternativa efectiva para mejorar la biodisponibilidad de fármacos poco solubles en agua. Para describir el estado de conocimiento sobre estos sistemas se realizó una revisión sistemática en diferentes bases de datos sobre la literatura relacionada con los SEDDS a nivel nacional e internacional, logrando así describir los aspectos más relevantes sobre los SEDDS (tipos, composición, mecanismos para aumentar biodisponibilidad, caracterización, formulaciones). A pesar de las numerosas investigaciones realizadas durante los últimos años que muestran el potencial de los SEDDS para mejorar la biodisponibilidad de los fármacos poco solubles en agua, se pudo evidenciar que solo algunas sustancias activas han sido incluidas en estos sistemas y comercializadas exitosamente, esto debido a algunas limitaciones que indican la necesidad de un mayor entendimiento sobre estos sistemas.

SUMMARY The great technological advances within the pharmaceutical industry that involve the use of combinatorial chemistry and high-throughput screening have led to the discovery of many chemical entities that are candidates for drugs that have poor water solubility due to their high molecular complexity, which makes it difficult the development of products with these substances. Self-emulsifying Drug Delivery Systems (SEDDS) have gained an interest in pharmaceutical development, showing to be an effective alternative to improve the poorly water-soluble drugs' bioavailability. In order to describe the state of knowledge about these systems, a systematic review was carried out in different databases about the literature related to SEDDS at a national and international level, describing the most relevant issues about SEDDS (types, composition, mechanisms to improve bioavailability, characterization, formulations). Despite the several investigations carried out during past years showing SEDDS potential to improve the bioavailability of poorly water-soluble drugs, it was evident that only a few active substances have been included in these systems and successfully commercialized, due to some limitations that indicate the need for a greater understanding about these systems.