Assessing ChatGPT's theoretical knowledge and prescriptive accuracy in bacterial infections: a comparative study with infectious diseases residents and specialists.

OBJECTIVES: Advancements in Artificial Intelligence(AI) have made platforms like ChatGPT increasingly relevant in medicine. This study assesses ChatGPT's utility in addressing bacterial infection-related questions and antibiogram-based clinical cases. METHODS: This study involved a collaborative effort involving infectious disease (ID) specialists and residents. A group of experts formulated six true/false, six open-ended questions, and six clinical cases with antibiograms for four types of infections (endocarditis, pneumonia, intra-abdominal infections, and bloodstream infection) for a total of 96 questions. The questions were submitted to four senior residents and four specialists in ID and inputted into ChatGPT-4 and a trained version of ChatGPT-4. A total of 720 responses were obtained and reviewed by a blinded panel of experts in antibiotic treatments. They evaluated the responses for accuracy and completeness, the ability to identify correct resistance mechanisms from antibiograms, and the appropriateness of antibiotics prescriptions. RESULTS: No significant difference was noted among the four groups for true/false questions, with approximately 70% correct answers. The trained ChatGPT-4 and ChatGPT-4 offered more accurate and complete answers to the open-ended questions than both the residents and specialists. Regarding the clinical case, we observed a lower accuracy from ChatGPT-4 to recognize the correct resistance mechanism. ChatGPT-4 tended not to prescribe newer antibiotics like cefiderocol or imipenem/cilastatin/relebactam, favoring less recommended options like colistin. Both trained- ChatGPT-4 and ChatGPT-4 recommended longer than necessary treatment periods (p-value = 0.022). CONCLUSIONS: This study highlights ChatGPT's capabilities and limitations in medical decision-making, specifically regarding bacterial infections and antibiogram analysis. While ChatGPT demonstrated proficiency in answering theoretical questions, it did not consistently align with expert decisions in clinical case management. Despite these limitations, the potential of ChatGPT as a supportive tool in ID education and preliminary analysis is evident. However, it should not replace expert consultation, especially in complex clinical decision-making.

Pulmonary Kaposi Sarcoma without Respiratory Symptoms and Skin Lesions in an HIV-Naïve Patient: A Case Report and Literature Review.

Kaposi sarcoma (KS) is a multifocal lympho-angioproliferative, mesenchymal low-grade tumor associated with a γ2-herpesvirus, named Kaposi sarcoma-associated virus or human herpesvirus (KSHV/HHV8). The lung is considered a usual anatomical location of KS, despite being infrequent, often in association with extensive mucocutaneous lesions and very uncommonly as an isolated event. We report a case of a pulmonary KS (pKS) in a human immunodeficiency virus (HIV) naïve patient, which was atypical due to a lack of cutaneous involvement and an absence of respiratory symptoms. The pKS was initially identified as a tumoral suspected nodular lesion and only after immunohistochemical analysis was it characterized as KS. Furthermore, the diagnosis of pKS led to the discovery of the HIV-seropositive status of the patient, previously unknown. Our report underlines the importance of considering pKS even without skin lesions and as a first manifestation of HIV infection. We also reviewed literature on the current knowledge about pKS in people living with HIV (PLWH) to underline how one of the most common HIV/acquired immunodeficiency syndrome (AIDS) associated tumors can have a challenging localization and be difficult to recognize.