[A look into the neighboring discipline: eHealth in oncology]. / Ein Blick in die Nachbardisziplin: eHealth in der Onkologie.

Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.

ChatGPT vs pharmacy students in the pharmacotherapy time-limit test: A comparative study in Thailand.

OBJECTIVES: ChatGPT is an innovative artificial intelligence designed to enhance human activities and serve as a potent tool for information retrieval. This study aimed to evaluate the performance and limitation of ChatGPT on fourth-year pharmacy student examination. METHODS: This cross-sectional study was conducted on February 2023 at the Faculty of Pharmacy, Chiang Mai University, Thailand. The exam contained 16 multiple-choice questions and 2 short-answer questions, focusing on classification and medical management of shock and electrolyte disorders. RESULTS: Out of the 18 questions, ChatGPT provided 44% (8 out of 18) correct responses. In contrast, the students provided a higher accuracy rate with 66% (12 out of 18) correctly answered questions. The findings of this study underscore that while AI exhibits proficiency, it encounters limitations when confronted with specific queries derived from practical scenarios, on the contrary with pharmacy students who possess the liberty to explore and collaborate, mirroring real-world scenarios. CONCLUSIONS: Users must exercise caution regarding its reliability, and interpretations of AI-generated answers should be approached judiciously due to potential restrictions in multi-step analysis and reliance on outdated data. Future advancements in AI models, with refinements and tailored enhancements, offer the potential for improved performance.

The Evaluation of Generative AI Should Include Repetition to Assess Stability.

The increasing interest in the potential applications of generative artificial intelligence (AI) models like ChatGPT in health care has prompted numerous studies to explore its performance in various medical contexts. However, evaluating ChatGPT poses unique challenges due to the inherent randomness in its responses. Unlike traditional AI models, ChatGPT generates different responses for the same input, making it imperative to assess its stability through repetition. This commentary highlights the importance of including repetition in the evaluation of ChatGPT to ensure the reliability of conclusions drawn from its performance. Similar to biological experiments, which often require multiple repetitions for validity, we argue that assessing generative AI models like ChatGPT demands a similar approach. Failure to acknowledge the impact of repetition can lead to biased conclusions and undermine the credibility of research findings. We urge researchers to incorporate appropriate repetition in their studies from the outset and transparently report their methods to enhance the robustness and reproducibility of findings in this rapidly evolving field.

[The revolution of AI in drug development]. / L'intelligence artificielle, une révolution dans le développement des médicaments.

Artificial intelligence and machine learning enable the construction of predictive models, which are currently used to assist in decision-making throughout the process of drug discovery and development. These computational models can be used to represent the heterogeneity of a disease, identify therapeutic targets, design and optimize drug candidates, and evaluate the efficacy of these drugs on virtual patients or digital twins. By combining detailed patient characteristics with the prediction of potential drug-candidate properties, artificial intelligence promotes the emergence of a "computational" precision medicine, allowing for more personalized treatments, better tailored to patient specificities with the aid of such predictive models. Based on such new capabilities, a mixed reality approach to the development of new drugs is being adopted by the pharmaceutical industry, which integrates the outputs of predictive virtual models with real-world empirical studies.

Title: L'intelligence artificielle, une révolution dans le développement des médicaments. Abstract: L'intelligence artificielle (IA) et l'apprentissage automatique produisent des modèles prédictifs qui aident à la prise de décisions dans le processus de découverte de nouveaux médicaments. Cette modélisation par ordinateur permet de représenter l'hétérogénéité d'une maladie, d'identifier des cibles thérapeutiques, de concevoir et optimiser des candidats-médicaments et d'évaluer ces médicaments sur des patients virtuels, ou des jumeaux numériques. En facilitant à la fois une connaissance détaillée des caractéristiques des patients et en prédisant les propriétés de multiples médicaments possibles, l'IA permet l'émergence d'une médecine de précision « computationnelle ¼ offrant des traitements parfaitement adaptés aux spécificités des patients.