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The quality of the very early emotional experiences in a child's life are recognized today as foundational in the mental health of becoming an adult. Unsurprisingly, clinicians and health authorities agree on the importance of early detection and treatment of any mental health issues experienced by the preschool child. This is even more important and challenging as young children do not yet have the language skills necessary to express their distress. But do the caregivers in charge of these populations have the best tools at their disposal to carry out that important mission in an efficient and scientifically robust manner reflecting the latest research findings and scientific literature? This is the question we try to answer in this paper. We revisited the theoretical underpinnings behind the concept of mentalization as playing a key role in the ability to understand the other's inner reality. We then carried out an extensive review of scientific papers written over the past 30 years on the subject. We examined mental representations of the adult on the child and analyzed those mentalizations as key inputs. We focused on extracting cross analyses of the mentalizations of one child by several adults with different functions in the life of the child. In this process, we used the PRISMA methodology applied to large international medical databases. We found that the qualitative approach, usually associated with a lengthy process, has been overwhelmingly used in trying to assess patients' mental states and to analyze the mentalizations on the patient, adult or child. Furthermore, cross analyses of mentalizations from more than one adult on a given child were almost nonexistent, despite having high epistemic and clinical potential. There is a dearth of reliable quantitative tools, able to capture and analyze the mentalizations on a patient, that are scientifically robust, transposable, and easy to implement in a clinical setting. The absence of such tools for medical professionals in charge of assessing preschool children contributes, in our view, the low detection and poor treatment of mental disorders in that particularly vulnerable population. We believe there is here an urgent unmet need and that such tools should be designed, validated, and deployed into the target populations as early as possible. [ABSTRACT FROM AUTHOR]

Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

The evaluation of teaching can be an essential driver for curriculum development. Instruments for teaching evaluation are not only used for the purpose of quality assurance but also in the context of medical education research. Therefore, they must meet the common requirements for reliability and validity. This position paper from the GMA Teaching Evaluation Committee discusses strategic and methodological aspects of evaluation in the context of undergraduate medical education and related courses; and formulates recommendations for the further development of evaluation. First, a four-step approach to the design and implementation of evaluations is presented, then methodological and practical aspects are discussed in more detail. The focus here is on target and confounding variables, survey instruments as well as aspects of implementation and data protection. Finally, possible consequences from evaluation data for the four dimensions of teaching quality (structural and procedural aspects, teachers and outcomes) are discussed. [ABSTRACT FROM AUTHOR]