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Engineering functional skeletal muscle tissue still remains a major challenge. So far clinically relevant sizes of functional skeletal muscle tissue could not be engineered yet. One of the obstacles to overcome is the development of a suitable scaffold for muscle tissue engineering in vivo, another is the lack of differentiation in expanded adult muscle precursor cells. Materials and different architectures of scaffolds which are used for engineering functional skeletal muscle are presented here as well as approaches to the differentiation challenge. Finally the translation from cell culture over small to large animal models for engineering axially vascularized musculoskeletal tissues will be described.

[english] In the present article the question of an increase in implant based breast reconstruction, as recently reported for the United States of America, is analyzed and the pertinent literature concerning a putative shift of paradigms in implant versus autologous breast reconstruction is discussed.[german] Im vorliegenden Artikel wird die Frage einer möglichen Zunahme an Prothesen-basierter Brustrekonstruktion, wie sie kürzlich für die USA beschrieben wurde, analysiert und die aktuelle Literatur bzgl. eines postulierten Paradigmenwechsels in der Prothesen- versus Eigengewebsrekonstruktion der weiblichen Brust diskutiert.

Exploring the integration of artificial intelligence in clinical settings, this study examined the feasibility of using Generative Pretrained Transformer 4 (GPT-4), a large language model, as a consultation assistant in a hand surgery outpatient clinic. The study involved 10 simulated patient scenarios with common hand conditions, where GPT-4, enhanced through specific prompt engineering techniques, conducted medical history interviews, and assisted in diagnostic processes. A panel of expert hand surgeons, each board-certified in hand surgery, evaluated GPT-4's responses using a Likert Scale across five criteria with scores ranging from 1 (lowest) to 5 (highest). Generative Pretrained Transformer 4 achieved an average score of 4.6, reflecting good performance in documenting a medical history, as evaluated by the hand surgeons. These findings suggest that GPT-4 can effectively document medical histories to meet the standards of hand surgeons in a simulated environment. The findings indicate potential for future application in patient care, but the actual performance of GPT-4 in real clinical settings remains to be investigated. This study provides a preliminary indication that GPT-4 could be a useful consultation assistant in a hand surgery outpatient clinic, but further research is required to explore its reliability and practicality in actual practice. [ABSTRACT FROM AUTHOR]

The role of artificial intelligence (AI) in healthcare is evolving, offering promising avenues for enhancing clinical decision making and patient management. Limited knowledge about lipedema often leads to patients being frequently misdiagnosed with conditions like lymphedema or obesity rather than correctly identifying lipedema. Furthermore, patients with lipedema often present with intricate and extensive medical histories, resulting in significant time consumption during consultations. AI could, therefore, improve the management of these patients. This research investigates the utilization of OpenAI's Generative Pre-Trained Transformer 4 (GPT-4), a sophisticated large language model (LLM), as an assistant in consultations for lipedema patients. Six simulated scenarios were designed to mirror typical patient consultations commonly encountered in a lipedema clinic. GPT-4 was tasked with conducting patient interviews to gather medical histories, presenting its findings, making preliminary diagnoses, and recommending further diagnostic and therapeutic actions. Advanced prompt engineering techniques were employed to refine the efficacy, relevance, and accuracy of GPT-4's responses. A panel of experts in lipedema treatment, using a Likert Scale, evaluated GPT-4's responses across six key criteria. Scoring ranged from 1 (lowest) to 5 (highest), with GPT-4 achieving an average score of 4.24, indicating good reliability and applicability in a clinical setting. This study is one of the initial forays into applying large language models like GPT-4 in specific clinical scenarios, such as lipedema consultations. It demonstrates the potential of AI in supporting clinical practices and emphasizes the continuing importance of human expertise in the medical field, despite ongoing technological advancements. [ABSTRACT FROM AUTHOR]