## 5 Results: Quantitative Analysis and Most Important Publications
### A Deeper Discussion of Papers in MainReview\({}_{New}\)  
Given the limited number of papers included in \(MainReview_{New}\), we provide a comprehensive discussion of each. Table 11 reports the month of publication and the thematic areas of the publishing journal, as classified by Scimago Research Centers, which maintain the widely recognized Journal & Country Rank (SJR),  
[PERSON] et al. [37] reviewed the transformative impact of generative algorithms on the implementation of artificial intelligence (AI) across academia, business, and broader societal contexts. The paper highlights that the advent of generative AI tools, such as ChatGPT, Jasper, and DALL-E, represents a significant milestone in accelerating AI adoption, attributing this progress to their user-friendly design, intuitive interfaces, and advanced performance capabilities. The study adopts a Business Model Innovation perspective to analyze generative AI tools, offering two main contributions: (1) the formulation of six comprehensive propositions elucidating GAI's impact on business practices and (2) an examination of three industry use cases, specifically software engineering, healthcare, and financial services. The scoping review methodology supports the qualitative content analysis, leveraging a broad dataset comprising 513 data points. Ref. [10] reviewed studies published following the release of ChatGPT, with a particular emphasis on its adoption among the general public and medical professionals. It highlights the \"productization\" of advanced technologies, enabling the accessibility of artificial intelligence (AI) tools to non-technical users, while stressing the critical precautions necessary for healthcare researchers. The authors conducted a systematic review of publications addressing ChatGPT's applications in healthcare, offering perspectives targeted at general audiences, medical practitioners, and NLP researchers. Articles were retrieved from the PubMed database using the keyword \"ChatGPT\", with inclusion criteria and a structured taxonomy employed to filter and organize the results. The study concludes that domain-specific NLP models trained on biomedical datasets continue to represent the preferred choice for critical clinical applications. The authors propose a two-dimensional taxonomy focusing on application domains and user types, categorizing the reviewed publications accordingly. Additionally, a tagging system (Level 1 to Level 3) is applied to denote the depth and specificity of each work. In [10], the authors analyzed the multifaceted applications of large language models (_LLM_s), such as ChatGPT, within the domains of biomedicine and healthcare. The study focuses on tasks including biomedical information retrieval, question answering, medical text summarization, information extraction, and medical education, evaluating whether _LLM_s possess the transformative potential to revolutionize these fields or whether the intrinsic complexities of the biomedical domain pose distinctive challenges. The authors conduct a comprehensive literature review, concluding that while _LLM_s have surpassed previous state-of-the-art methods in text generation tasks, their advancements in other applications remain incremental. Furthermore, the study identifies several risks and challenges associated with employing _LLM_s in biomedicine and healthcare, such as the generation of fabricated information in model outputs and significant legal and privacy concerns linked to the handling of sensitive patient data. The study proposed by [PERSON] et al. [38] belongs to a different context since it qualitatively discuss the impact of AI and seems focused on medical matters rather than technical or information. This paper does not provide a systematic review of publications but discusses the impact of large language model on a single specific medicine field such as dental medicine. Moreover, the approach is not extensive, but it discusses the main problems related with medicine matters.