Summary: This study delves into the potential application of OpenAI’s Generative Pretrained Transformer 4 (GPT-4) in plastic surgery, with a particular focus on procedures involving the hand and arm. GPT-4, a cutting-edge artificial intelligence (AI) model known for its advanced chat interface, was tested on nine surgical scenarios of varying complexity. To optimize the performance of GPT-4, prompt engineering techniques were used to guide the model’s responses and improve the relevance and accuracy of its output. A panel of expert plastic surgeons evaluated the responses using a Likert scale to assess the model’s performance, based on five distinct criteria. Each criterion was scored on a scale of 1 to 5, with 5 representing the highest possible score. GPT-4 demonstrated a high level of performance, achieving an average score of 4.34 across all cases, consistent across different complexities. The study highlights the ability of GPT-4 to understand and respond to complicated surgical scenarios. However, the study also identifies potential areas for improvement. These include refining the prompts used to elicit responses from the model and providing targeted training with specialized, up-to-date sources. This study demonstrates a new approach to exploring large language models and highlights potential future applications of AI. These could improve patient care, refine surgical outcomes, and even change the way we approach complex clinical scenarios in plastic surgery. However, the intrinsic limitations of AI in its current state, together with the potential ethical considerations and the inherent uncertainty of unanticipated issues, serve to reiterate the indispensable role and unparalleled value of human plastic surgeons.

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.

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.

CAMP acts as a second messenger in many cellular processes. Three protein types mainly mediate cAMP-induced effects: PKA, exchange protein directly activated by cAMP (Epac), and cyclic nucleotide–modulated channels (cyclic nucleotide–gated or hyperpolarization-activated and cyclic nucleotide–modulated (HCN) channels). Discrimination among these cAMP signaling pathways requires specific targeting of only one protein. Previously, cAMP modifications at position N⁶ of the adenine ring (PKA) and position 2′-OH of the ribose (Epac) have been used to produce target-selective compounds. However, cyclic nucleotide–modulated ion channels were usually outside of the scope of these previous studies. These channels are widely distributed, so possible channel cross-activation by PKA- or Epac-selective agonists warrants serious consideration. Here we demonstrate the agonistic effects of three PKA-selective cAMP derivatives, N⁶-phenyladenosine-3′,5′-cyclic monophosphate (N⁶-Phe-cAMP), N⁶-benzyladenosine-3′,5′-cyclic monophosphate (N⁶-Bn-cAMP), and N⁶-benzoyl-adenosine-3′,5′-cyclic monophosphate (N⁶-Bnz-cAMP), on murine HCN2 pacemaker channels. Electrophysiological characterization in Xenopus oocytes revealed that these derivatives differ in apparent affinities depending on the modification type but that their efficacy and effects on HCN2 activation kinetics are similar to those of cAMP. Docking experiments suggested a pivotal role of Arg-635 at the entrance of the binding pocket in HCN2, either causing stabilizing cation–π interactions with the aromatic ring in N⁶-Phe-cAMP or N⁶-Bn-cAMP or a steric clash with the aromatic ring in N⁶-Bnz-cAMP. A reduced apparent affinity of N⁶-Phe-cAMP toward the variants R635A and R635E strengthened that notion. We conclude that some PKA activators also effectively activate HCN2 channels. Hence, when studying PKA-mediated cAMP signaling with cAMP derivatives in a native environment, activation of HCN channels should be considered.

For half a century, the Mallet Scale (MS) has been utilized to assess upper limb function in patients with obstetric brachial plexus palsy (OBPP). However, the correct use of the MS requires trained personnel and the MS does not measure compensatory movements. For this reason, new methods are needed to compensate for these weaknesses. This study introduces an innovative method for objective functional motion analysis using accelerometers to measure upper limb movements in thirty patients with obstetric brachial plexus lesions. Five triaxial accelerometers were positioned on the chest and each upper limb. They recorded acceleration signals during repetitive everyday tasks: hand-to-mouth (HM), hand-to-neck (HN), and hand-to-spine (HS). From these signals, 54 features were extracted and subjected to linear correlation tests to identify 5 suitable features. An algorithm was then developed to categorize patients into five groups and compute an individual movement performance score (iMPScore) assessing the patient’s upper extremity function. By using the iMPScore more than 75% of all participants have been classified correctly with respect to their MS category. Identification of MS I category patients in general and assessing upper extremity function of MS I to III in HS tasks were most challenging. We conclude that the introduced approach is a valuable tool for gauging movement limitation of upper limbs in patients with obstetric brachial plexus palsy. Compared to other clinically established methods, it becomes possible to record and even quantify the extent of compensatory movements. In this way, an objective, user- and patient-friendly method is offered, which supports significantly physicians and therapists in their evaluation of OBPP.