Pancreatic fluid collections (PFCs) are a frequent complication of pancreatitis. It is important to classify PFCs to guide management. The revised Atlanta criteria classifies PFCs as acute or chronic, with chronic fluid collections subdivided into pseudocysts and walled-off pancreatic necrosis (WOPN). Establishing adequate nutritional support is an essential step in the management of PFCs. Early attempts at oral feeding can be trialed in patients with mild pancreatitis. Enteral feeding should be implemented in patients with moderate to severe pancreatitis. Jejunal feeding remains the preferred route of enteral nutrition. Symptomatic PFCs require drainage; options include surgical, percutaneous, or endoscopic approaches. With the advent of newer and more advanced endoscopic tools and expertise, and an associated reduction in health care costs, minimally invasive endoscopic drainage has become the preferable approach. An endoscopic ultrasonography-guided approach using a seldinger technique is the preferred endoscopic approach. Both plastic stents and metal stents are efficacious and safe; however, metal stents may offer an advantage, especially in infected pseudocysts and in WOPN. Direct endoscopic necrosectomy is often required in WOPN. Lumen apposing metal stents that allow for direct endoscopic necrosectomy and debridement through the stent lumen are preferred in these patients. Endoscopic retrograde cholangio pancreatography with pancreatic duct (PD) exploration should be performed concurrent to PFC drainage. PD disruption is associated with an increased severity of pancreatitis, an increased risk of recurrent attacks of pancreatitis and long-term complications, and a decreased rate of PFC resolution after drainage. Any pancreatic ductal disruption should be bridged with endoscopic stenting. However, with improving pathophysiologic under standing and improving diagnostic tools, it became clear that a more detailed organizational system was required. More specifically, one that distinguished between collections containing fluid alone vs those arising from necrosis and/or containing solid components. As such, a new classification system was developed known as the revised Atlanta criteria [4] . Similar to the original Atlanta Criteria, PFCs are classified as acute (< 4 wk after the pancreatitis episode) or chronic (> 4 wk after the pancreatitis episode). However, in the revised criteria, both acute and chronic collections are further subdivided based on the presence of necrosis within the collection. Acute collections are divided into: acute peripancreatic fluid collections (APFC) and acute necrotic collections (ANC); chronic fluid collections are divided into: pseudocysts or walledoff pancreatic necrosis (WOPN). These new classifications are important because the treatment and management varies depending on the type of collection. ENTERAL FEEDINGThe first step in the management of any PFC is ensuring adequate nutritional support. In mild to moderate acute pancreatitis, oral feeding can be initiated when...
This paper investigates the application of Large Language Models (LLMs), specifically OpenAI's ChatGPT-3.5, ChatGPT-4.0, Google Bard, and Microsoft Bing, in simplifying radiology reports, thus potentially enhancing patient understanding. We examined 254 anonymized radiology reports from diverse examination types and used three different prompts to guide the LLMs' simplification processes. The resulting simplified reports were evaluated using four established readability indices. All LLMs significantly simplified the reports, but performance varied based on the prompt used and the specific model. The ChatGPT models performed best when additional context was provided (i.e., specifying user as a patient or requesting simplification at the 7th grade level). Our findings suggest that LLMs can effectively simplify radiology reports, although improvements are needed to ensure accurate clinical representation and optimal readability. These models have the potential to improve patient health literacy, patient-provider communication, and ultimately, health outcomes.
UNSTRUCTURED AI systems hold immense promise for medicine. We should certainly celebrate their possibilities and accomplishments. Nevertheless, we need to confront the challenges of mitigating potential harms, while amplifying benefits. In medicine, there are ample opportunities for harm and misinformation, so there is a need for caution. Once released, we cannot control how information from AI systems is used, but we can emphasize to users that the technology remains in an early phase of development and that answers should not be considered the same as advice from clinical experts. Moreover, there is a need for further research to understand the output of AI systems when used in response to medical questions. While these considerations are not unique to the use of AI in medicine, the potential for imminent harm to individuals makes it particularly important to carefully evaluate and manage the use of AI in this field. The release of a powerful tool such as ChatGPT will instill awe, but in medicine, it needs to elicit appropriate action to evaluate its capabilities, mitigate its harms, and facilitate its optimal use.
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