Producción Científica

This review aims to explore the growing impact of machine learning and deep learning algorithms in the medical field, with a specific focus on the critical issues of explainability and interpretability associated with black-box algorithms. While machine learning algorithms are increasingly employed for medical analysis and diagnosis, their complexity underscores the importance of understanding how these algorithms explain and interpret data to take informed decisions. This review comprehensively analyzes challenges and solutions presented in the literature, offering an overview of the most recent techniques utilized in this field. It also provides precise definitions of interpretability and explainability, aiming to clarify the distinctions between these concepts and their implications for the decision-making process. Our analysis, based on 448 articles and addressing seven research questions, reveals an exponential growth in this field over the last decade. The psychological dimensions of public perception underscore the necessity for effective communication regarding the capabilities and limitations of artificial intelligence. Researchers are actively developing techniques to enhance interpretability, employing visualization methods and reducing model complexity. However, the persistent challenge lies in finding the delicate balance between achieving high performance and maintaining interpretability. Acknowledging the growing significance of artificial intelligence in aiding medical diagnosis and therapy, and the creation of interpretable artificial intelligence models is considered essential. In this dynamic context, an unwavering commitment to transparency, ethical considerations, and interdisciplinary collaboration is imperative to ensure the responsible use of artificial intelligence. This collective commitment is vital for establishing enduring trust between clinicians and patients, addressing emerging challenges, and facilitating the informed adoption of these advanced technologies in medicine.

Purpose To explore gender distribution in authorship and citation parameters of articles published in five optometry journals included in the Ophthalmology category of Journal Citation Reports. Methods The Scopus database was used to retrieve all citable articles published in 2011 and 2021 in Optometry and Vision Science, Ophthalmic and Physiological Optics, Clinical Experimental Optometry, Contact Lens and Anterior Eye, and Eye and Contact Lens. Gender of the first, last and single authors of all articles, and citation parameters of articles published in 2011 up to May 2023 were determined. Gender of the editor-in-chief and members of the editorial board of these journals was investigated (May 2023). Results Only one journal had a female editor-in-chief and three journals had more males than females in their editorial board. In 2011 and 2021, respectively, 40.1% and 48.0% of articles had female as the first authors, and 32.7% and 39.6% had female as the last authors. Gender parity was observed in one journal for the first author and none for the last author in 2011, and in three journals for the first author and one for the last author in 2021. Regarding combinations of male (M) and female (F) first and last authorship positions, the authors of articles in 2011 were MM (44.5%), FM (22.8%), FF (17.3%) and MF (15.4%), and MM (34.6%), FM (25.8%), FF (22.1%) and MF (17.5%) in 2021. Differences between 2011 and 2021 were statistically significant. The proportion of authorship combinations did not show statistically significant differences among journals in 2011 or in 2021. Neither citation nor self-citation were influenced by gender. Conclusions Gender disparities persist in optometry journals, with females being under-represented in senior and leadership positions. Increasing the awareness of gender disparity in authorship is a necessary step towards ensuring fairness in science in general, and optometry in particular.

Neuromyelitis optica is an inflammatory demyelinating disease of the central nervous system that differs from multiple sclerosis. Over the past 20 years, the search for biomarkers for neuromyelitis optica has been ongoing. Here, we used a bibliometric approach to analyze the main research focus in the field of biomarkers for neuromyelitis optica. Research in this area is consistently increasing, with China and the United States leading the way on the number of studies conducted. The Mayo Clinic is a highly reputable institution in the United States, and was identified as the most authoritative institution in this field. Furthermore, Professor Wingerchuk from the Mayo Clinic was the most authoritative expert in this field. Keyword analysis revealed that the terms “neuromyelitis optica” (261 times), “multiple sclerosis” (220 times), “neuromyelitis optica spectrum disorder” (132 times), “aquaporin 4” (99 times), and “optical neuritis” (87 times) were the most frequently used keywords in literature related to this field. Comprehensive analysis of the classical literature showed that the majority of publications provide conclusive research evidence supporting the use of aquaporin-4-IgG and neuromyelitis optica-IgG to effectively diagnose and differentiate neuromyelitis optica from multiple sclerosis. Furthermore, aquaporin-4-IgG has emerged as a highly specific diagnostic biomarker for neuromyelitis optica spectrum disorder. Myelin oligodendrocyte glycoprotein-IgG is a diagnostic biomarker for myelin oligodendrocyte glycoprotein antibody-associated disease. Recent biomarkers for neuromyelitis optica include cerebrospinal fluid immunological biomarkers such as glial fibrillary acidic protein, serum astrocyte damage biomarkers like FAM19A5, serum albumin, and gamma-aminobutyric acid. The latest prospective clinical trials are exploring the potential of these biomarkers. Preliminary results indicate that glial fibrillary acidic protein is emerging as a promising candidate biomarker for neuromyelitis optica spectrum disorder. The ultimate goal of future research is to identify non-invasive biomarkers with high sensitivity, specificity, and safety for the accurate diagnosis of neuromyelitis optica.

Introduction The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods The study collected data from the Web of Science database on articles containing the keyword “ChatGPT” published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as “patient” (16.7%), “research” (12%), and “performance” (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion The prominence of India in ChatGPT research, despite lower research funding, indicates the platform’s popularity and highlights the importance of monitoring its use for potential medical misinformation. China’s interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus’ success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion ChatGPT’s presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges.