Using simulated patients to mimic 9 established noncommunicable and infectious diseases, we assessed ChatGPT\'s performance in treatment recommendations for common diseases in low- and middle-income countries. ChatGPT had a high level of accuracy in both correct diagnoses (20/27, 74%) and medication prescriptions (22/27, 82%) but a concerning level of unnecessary or harmful medications (23/27, 85%) even with correct diagnoses. ChatGPT performed better in managing noncommunicable diseases than infectious ones. These results highlight the need for cautious AI integration in health care systems to ensure quality and safety.

OBJECTIVE: The integration of digital technology into medical practice is often thrust upon clinicians, with standards and routines developed long after initiation. Clinicians should endeavor towards a basic understanding even of emerging technologies so that they can direct its use. The intent of this review is to describe the current state of rapidly evolving generative artificial intelligence (GAI), and to explore both how pediatric gastroenterology practice may benefit as well as challenges that will be faced.
RESULTS: Although little research demonstrating the acceptance, practice, and outcomes associated with GAI in pediatric gastroenterology is published, there are relevant data adjacent to the specialty and overwhelming potential as professed in the media. Best practice guidelines are widely developed in academic publishing and resources to initiate and improve practical user skills are prevalent. Initial published evidence supports broad acceptance of the technology as part of medical practice by clinicians and patients, describes methods with which higher quality GAI can be developed, and identifies the potential for bias and disparities resulting from its use. GAI is broadly available as a digital tool for incorporation into medical practice and holds promise for improved quality and efficiency of care, but investigation into how GAI can best be used remains at an early stage despite rapid evolution of the technology.

Predictions are made by artificial intelligence, especially through machine learning, which uses algorithms and past knowledge. Notably, there has been an increase in interest in using artificial intelligence, particularly generative AI, in the pharmacovigilance of pharmaceuticals under development, as well as those already in the market. This review was conducted to understand how generative AI can play an important role in pharmacovigilance and improving drug safety monitoring. Data from previously published articles and news items were reviewed in order to obtain information. We used PubMed and Google Scholar as our search engines, and keywords (pharmacovigilance, artificial intelligence, machine learning, drug safety, and patient safety) were used. In toto, we reviewed 109 articles published till 31 January 2024, and the obtained information was interpreted, compiled, evaluated, and conclusions were reached. Generative AI has transformative potential in pharmacovigilance, showcasing benefits, such as enhanced adverse event detection, data-driven risk prediction, and optimized drug development. By making it easier to process and analyze big datasets, generative artificial intelligence has applications across a variety of disease states. Machine learning and automation in this field can streamline pharmacovigilance procedures and provide a more efficient way to assess safety-related data. Nevertheless, more investigation is required to determine how this optimization affects the caliber of safety analyses. In the near future, the increased utilization of artificial intelligence is anticipated, especially in predicting side effects and Adverse Drug Reactions (ADRs).

Generative AI is revolutionizing oncological imaging, enhancing cancer detection and diagnosis. This editorial explores its impact on expanding datasets, improving image quality, and enabling predictive oncology. We discuss ethical considerations and introduce a unique perspective on personalized cancer screening using AI-generated digital twins. This approach could optimize screening protocols, improve early detection, and tailor treatment plans. While challenges remain, generative AI in oncological imaging offers unprecedented opportunities to advance cancer care and improve patient outcomes.

Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI); however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany\'s National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer\'s Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch Institute (RKI).

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OBJECTIVE: The aim of this study was to assess the ChatGPT-4 (ChatGPT) large language model (LLM) on tasks relevant to community pharmacy.
METHODS: ChatGPT was assessed with community pharmacy-relevant test cases involving drug information retrieval, identifying labelling errors, prescription interpretation, decision-making under uncertainty and multidisciplinary consults. Drug information on rituximab, warfarin, and St. John\'s wort was queried. The decision-support scenarios consisted of a subject with swollen eyelids and a maculopapular rash in a subject on lisinopril and ferrous sulfate. The multidisciplinary scenarios required the integration of medication management with recommendations for healthy eating and physical activity/exercise.
RESULTS: The responses from ChatGPT for rituximab, warfarin, and St. John\'s wort were satisfactory and cited drug databases and drug-specific monographs. ChatGPT identified labeling errors related to incorrect medication strength, form, route of administration, unit conversion, and directions. For the patient with inflamed eyelids, the course of action developed by ChatGPT was comparable to the pharmacist\'s approach. For the patient with the maculopapular rash, both the pharmacist and ChatGPT placed a drug reaction to either lisinopril or ferrous sulfate at the top of the differential. ChatGPT provided customized vaccination requirements for travel to Brazil, guidance on management of drug allergies and recovery from a knee injury. ChatGPT provided satisfactory medication management and wellness information for a diabetic on metformin and semaglutide.
CONCLUSIONS: LLMs have the potential to become a powerful tool in community pharmacy. However, rigorous validation studies across diverse pharmacist queries, drug classes and populations, and engineering to secure patient privacy will be needed to enhance LLM utility.

UNASSIGNED: To compare student performance, examiner perceptions and cost of GPT-assisted (generative pretrained transformer-assisted) clinical and professional skills assessment (CPSAs) items against items created using standard methods.
UNASSIGNED: We conducted a prospective, controlled, double-blinded comparison of CPSA items developed using GPT-assistance with those created through standard methods. Two sets of six practical cases were developed for a formative assessment sat by final year medical students. One clinical case in each set was created with GPT-assistance. Students were assigned to one of the two sets.
UNASSIGNED: The results of 239 participants were analysed in the study. There was no statistically significant difference in item difficulty, or discriminative ability between GPT-assisted and standard items. One hundred percent (n = 15) of respondents to an examiner feedback questionnaire felt GPT-assisted cases were appropriately difficult and realistic. GPT-assistance resulted in significant labour cost savings, with a mean reduction of 57% (880 GBP) in labour cost per case when compared to standard case drafting methods.
UNASSIGNED: GPT-assistance can create CPSA items of comparable quality with significantly less cost when compared to standard methods. Future studies could evaluate GPT\'s ability to create CPSA material in other areas of clinical practice, aiming to validate the generalisability of these findings.

OBJECTIVE: Generative Deep Learning has emerged in recent years as a significant player in the Artificial Intelligence field. Synthesizing new data while maintaining the features of reality has revolutionized the field of Deep Learning, proving to be particularly useful in contexts where obtaining data is challenging. The objective of this study is to employ the DoppelGANger algorithm, a cutting-edge approach based on Generative Adversarial Networks for time series, to enhance patient admissions forecasting in a hospital Emergency Department.
METHODS: We employed the DoppelGANger algorithm in a sequential methodology, conditioning generated time series with unique attributes to optimize data utilization. After confirming the successful creation of synthetic data with new attribute values, we adopted the Train-Synthetic-Test-Real framework to ensure the reliability of our synthetic data validation. We then augmented the original series with synthetic data to enhance the Prophet model\'s performance. This process was applied to two datasets derived from the original: one with four years of training followed by one year of testing, and another with three years of training and two years of testing.
RESULTS: The experimental results show that the generative model outperformed Prophet on the forecasting task, improving the SMAPE from 7.30 to 6.99 with the four-year training set, and from 22.84 to 7.41 for the three-year training set, all in daily aggregations. For the data replacement task, the Prophet SMAPE values decreased to 6.84 and 7.18 for four and three-year sets on the same aggregation. Additionally, data augmentation reduced the SMAPE to 6.79 for a one-year test set and achieved 8.56 for the two-year test set, surpassing the performance achieved by the same Prophet model when trained only on real data. Results for the remaining aggregations were consistent.
CONCLUSIONS: The findings of this study suggest that employing a generative algorithm to extend a training dataset can effectively enhance predictive models within the domain of Emergency Department admissions. The improvement can lead to more efficient resource allocation and patient management.

Generative AI models, such as ChatGPT, have significantly impacted healthcare through the strategic use of prompts to enhance precision, relevance, and ethical standards. This perspective explores the application of prompt engineering to tailor outputs specifically for healthcare stakeholders: patients, providers, policymakers, and researchers. A nine-stage process for prompt engineering in healthcare is proposed, encompassing identifying applications, understanding stakeholder needs, designing tailored prompts, iterative testing and refinement, ethical considerations, collaborative feedback, documentation, training, and continuous updates. A literature review focused on \"Generative AI\" or \"ChatGPT,\" prompts, and healthcare informed this study, identifying key prompts through qualitative analysis and expert input. This systematic approach ensures that AI-generated prompts align with stakeholder requirements, offering valuable insights into symptoms, treatments, and prevention, thereby supporting informed decision-making among patients.