## 5 Results: Quantitative Analysis and Most Important Publications
### A Deeper Discussion of Papers in MainReview\({}_{New}\)
The authors analyzed the model architecture, focusing on the integration of transformers and their positioning within the segmentation model, as well as modifications made by researchers to enhance the model's performance. The study compared the segmentation results using the Dice evaluation metric, revealing that Unet-based transformer models are favored, with transformers typically positioned in the encoder, as corroborated by 93 referenced works. The authors assert that their paper represents the first systematic review of medical image segmentation using transformer blocks. The review by [PERSON] [42] focused on the ethical design of large language models, highlighting the risks of these tools being used to propagate misinformation and harmful content on a massive scale. The paper underscores the potential for _LLM_s to revolutionize healthcare and medical data workflows, while also addressing the operational principles, risks, and limitations associated with their use. The review advocates for a multidimensional framework, encompassing ethical, technical, and cultural considerations, to guide the responsible deployment of these technologies. It emphasizes the need for collaboration among all stakeholders--developers, regulators, and users--to ensure that _LLM_s are harnessed effectively for innovative and reliable applications in complex, evidence-driven fields such as healthcare. The study by [PERSON] [6] is a systematic review conducted according to PRISMA guidelines to evaluate the utility and limitations of ChatGPT. [PERSON] analyzed English-language records from PubMed/MEDLINE and Google Scholar, including both published research and preprints, ultimately incorporating 60 studies. The review identifies significant improvements across various fields; however, it also highlights several concerns, such as ethical issues, copyright challenges, transparency, legal risks, and problems related to bias, plagiarism, hallucinated content, limited domain knowledge, incorrect citations, cybersecurity vulnerabilities, and the potential for infodemics. Notably, the review does not focus on technical aspects of ChatGPT's implementation. The paper by [PERSON] et al. [9] focused on the impact of GPT-4 on various healthcare applications, including radiology reporting, patient summaries, clinical decision support, and antimicrobial guidance. The study demonstrates that GPT-4 holds significant promise in enhancing these areas, but emphasizes the importance of adhering to ethical principles such as benefcience, transparency, and privacy. These principles are crucial to mitigate potential risks and ensure the responsible and equitable use of AI technologies in healthcare. The authors propose the development of a regulatory framework that involves collaboration between society, healthcare institutions, and AI developers to address the legal, humanistic, algorithmic, and information ethics concerns. The paper stresses that balancing the benefits and risks of GPT-4, as AI technology evolves rapidly, is essential for its effective and ethical integration into healthcare systems. The study by [PERSON] [43] offers a comprehensive overview of ChatGPT's origins, technological foundations, and its diverse applications in sectors like customer service, healthcare, and education. ChatGPT is portrayed as a transformative tool capable of reshaping traditional workflows by enhancing efficiency and improving accessibility in these domains. The review also critically addressed the challenges associated with its use, such as ethical concerns, algorithmic biases, data limitations, and safety issues. [PERSON] underscored the necessity of implementing robust mitigation strategies to ensure the responsible and effective deployment of the technology. Future directions for ChatGPT were explored, particularly its integration with emerging technologies such as augmented reality (AR) and the Internet of Things (IoT), which could help bridge the digital divide.