Artificial Intelligence: Can Information be Transformed into Intelligence in Surgical Education?

Medical Education and Artificial Intelligence: Web of Science-Based Bibliometric Analysis (2013-2022)

Background

Incremental advancements in artificial intelligence (AI) technology have facilitated its integration into various disciplines. In particular, the infusion of AI into medical education has emerged as a significant trend, with noteworthy research findings. Consequently, a comprehensive review and analysis of the current research landscape of AI in medical education is warranted.

Objective

This study aims to conduct a bibliometric analysis of pertinent papers, spanning the years 2013-2022, using CiteSpace and VOSviewer. The study visually represents the existing research status and trends of AI in medical education.

Methods

Articles related to AI and medical education, published between 2013 and 2022, were systematically searched in the Web of Science core database. Two reviewers scrutinized the initially retrieved papers, based on their titles and abstracts, to eliminate papers unrelated to the topic. The selected papers were then analyzed and visualized for country, institution, author, reference, and keywords using CiteSpace and VOSviewer.

Results

A total of 195 papers pertaining to AI in medical education were identified from 2013 to 2022. The annual publications demonstrated an increasing trend over time. The United States emerged as the most active country in this research arena, and Harvard Medical School and the University of Toronto were the most active institutions. Prolific authors in this field included Vincent Bissonnette, Charlotte Blacketer, Rolando F Del Maestro, Nicole Ledows, Nykan Mirchi, Alexander Winkler-Schwartz, and Recai Yilamaz. The paper with the highest citation was "Medical Students' Attitude Towards Artificial Intelligence: A Multicentre Survey." Keyword analysis revealed that "radiology," "medical physics," "ehealth," "surgery," and "specialty" were the primary focus, whereas "big data" and "management" emerged as research frontiers.

Conclusions

The study underscores the promising potential of AI in medical education research. Current research directions encompass radiology, medical information management, and other aspects. Technological progress is expected to broaden these directions further. There is an urgent need to bolster interregional collaboration and enhance research quality. These findings offer valuable insights for researchers to identify perspectives and guide future research directions.

Impact of artificial intelligence on the training of general surgeons of the future: a scoping review of the advances and challenges

PURPOSE

To explore artificial intelligence's impact on surgical education, highlighting its advantages and challenges.

METHODS

A comprehensive search across databases such as PubMed, Scopus, Scientific Electronic Library Online (SciELO), Embase, Web of Science, and Google Scholar was conducted to compile relevant studies.

RESULTS

Artificial intelligence offers several advantages in surgical training. It enables highly realistic simulation environments for the safe practice of complex procedures. Artificial intelligence provides personalized real-time feedback, improving trainees' skills. It efficiently processes clinical data, enhancing diagnostics and surgical planning. Artificial intelligence-assisted surgeries promise precision and minimally invasive procedures. Challenges include data security, resistance to artificial intelligence adoption, and ethical considerations.

CONCLUSIONS

Stricter policies and regulatory compliance are needed for data privacy. Addressing surgeons' and educators' reluctance to embrace artificial intelligence is crucial. Integrating artificial intelligence into curricula and providing ongoing training are vital. Ethical, bioethical, and legal aspects surrounding artificial intelligence demand attention. Establishing clear ethical guidelines, ensuring transparency, and implementing supervision and accountability are essential. As artificial intelligence evolves in surgical training, research and development remain crucial. Future studies should explore artificial intelligence-driven personalized training and monitor ethical and legal regulations. In summary, artificial intelligence is shaping the future of general surgeons, offering advanced simulations, personalized feedback, and improved patient care. However, addressing data security, adoption resistance, and ethical concerns is vital. Adapting curricula and providing continuous training are essential to maximize artificial intelligence's potential, promoting ethical and safe surgery.

Design and Implementation of Intelligent Monitoring System for Head and Neck Surgery Care Based on Internet of Things (IoT)

As a chronic disease, cervical spondylosis is prone to recurrent attacks as we age if we do not pay attention to protection, which can easily lead to symptoms such as osteophytes and herniated discs. In the early stage of cervical spondylosis, it is possible to alleviate the disease and prevent its aggravation by improving poor cervical posture and increasing cervical activities. This article analyzes the current situation and medical prospect of smart wearable devices with the prevention and treatment of cervical spondylosis in white-collar people as the starting point and smart wearable devices as the focus and provides a detailed analysis of the functions, categories, technologies, and applications of smart wearable devices to provide a technical theoretical basis for the construction of the subsequent research system. For the user's health state, some other physiological parameters are sent to data also through mobile Internet, and the user's physiological information is obtained on the computer database in also, which not only provides the monitoring function for the user's health but also provides the information of medical big data elements for medical and health institutions and so on. This article elaborates the requirement analysis of this system, based on which the system architecture design and module division are elaborated. It provides a practical and theoretical basis for further realizing the seamless integration of IoT technology and nursing information management system and improving its depth and breadth in the application of nursing information management system. From the perspective of the way of quantification of nursing practice activities, real-time monitoring, scientific management, and intelligent decision-making, it provides the basis for achieving the quality of nursing services, reducing errors, reducing labor intensity, and improving work efficiency and clinical research.

Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature

Medical education refers to education and training delivered to medical students in order to become a practitioner. In recent decades, medicine has been radically transformed by scientific and computational/digital advances—including the introduction of new information and communication technologies, the discovery of DNA, and the birth of genomics and post-genomics super-specialties (transcriptomics, proteomics, interactomics, and metabolomics/metabonomics, among others)—which contribute to the generation of an unprecedented amount of data, so-called ‘big data’. While these are well-studied in fields such as medical research and methodology, translational medicine, and clinical practice, they remain overlooked and understudied in the field of medical education. For this purpose, we carried out an integrative review of the literature. Twenty-nine studies were retrieved and synthesized in the present review. Included studies were published between 2012 and 2021. Eleven studies were performed in North America: specifically, nine were conducted in the USA and two studies in Canada. Six studies were carried out in Europe: two in France, two in Germany, one in Italy, and one in several European countries. One additional study was conducted in China. Eight papers were commentaries/theoretical or perspective articles, while five were designed as a case study. Five investigations exploited large databases and datasets, while five additional studies were surveys. Two papers employed visual data analytical/data mining techniques. Finally, other two papers were technical papers, describing the development of software, computational tools and/or learning environments/platforms, while two additional studies were literature reviews (one of which being systematic and bibliometric).The following nine sub-topics could be identified: (I) knowledge and awareness of big data among medical students; (II) difficulties and challenges in integrating and implementing big data teaching into the medical syllabus; (III) exploiting big data to review, improve and enhance medical school curriculum; (IV) exploiting big data to monitor the effectiveness of web-based learning environments among medical students; (V) exploiting big data to capture the determinants and signatures of successful academic performance and counteract/prevent drop-out; (VI) exploiting big data to promote equity, inclusion, and diversity; (VII) exploiting big data to enhance integrity and ethics, avoiding plagiarism and duplication rate; (VIII) empowering medical students, improving and enhancing medical practice; and, (IX) exploiting big data in continuous medical education and learning. These sub-themes were subsequently grouped in the following four major themes/topics: namely, (I) big data and medical curricula; (II) big data and medical academic performance; (III) big data and societal/bioethical issues in biomedical education; and (IV) big data and medical career. Despite the increasing importance of big data in biomedicine, current medical curricula and syllabuses appear inadequate to prepare future medical professionals and practitioners that can leverage on big data in their daily clinical practice. Challenges in integrating, incorporating, and implementing big data teaching into medical school need to be overcome to facilitate the training of the next generation of medical professionals. Finally, in the present integrative review, state-of-art and future potential uses of big data in the field of biomedical discussion are envisaged, with a focus on the still ongoing “Coronavirus Disease 2019” (COVID-19) pandemic, which has been acting as a catalyst for innovation and digitalization.

Application and effects of an early childhood education machine on analgesia and sedation in children after cardiothoracic surgery

OBJECTIVE

To study the effect of an early childhood education machine on sedation and analgesia in children after cardiothoracic surgery.

METHODS

A prospective randomized controlled study was conducted in a provincial hospital in China. Fifty-two patients (aged from 1 to 5 years) underwent cardiothoracic surgery (including: ventricular septal defect, patent ductus arteriosus, atrial septal defect, pulmonary stenosis, pulmonary sequestration and congenital cystic adenomatoid lung malformation) were divided into the study group (n = 26) and the control group (n = 26). The patients in the study group underwent intervention with an early childhood education machine (uniform type) in addition to routine standard treatment and nursing, while the patients in the control group only received routine standard treatment and nursing. Richmond agitation sedation score (RASS) and face, legs, activity, cry, consolability (FLACC) score of all of the patients were evaluated, and the negative emotions (self-rating anxiety scale (SAS) score and self-rating depression scale (SDS) score) of the parents of the two groups were compared.

RESULTS

There was no significant difference in the general clinical data between the two groups. The RASS and FLACC scores in the study group were significantly lower than those in the control group, and the SAS and SDS scores of the parents in the study group were significantly lower than those in the control group.

CONCLUSION

The application of an early childhood education machine for children after cardiothoracic surgery can effectively reduce postoperative agitation, improve sedation and analgesia of the patients, and ease the pessimistic mood of the patients' parents.

Current methods for assessing technical skill in cataract surgery

Surgery is a major source of errors in patient care. Preventing complications from surgical errors in the operating room is estimated to lead to reduction of up to 41 846 readmissions and save $620.3 million per year. It is now established that poor technical skill is associated with an increased risk of severe adverse events postoperatively and traditional models to train surgeons are being challenged by rapid advances in technology, an intensified patient-safety culture, and a need for value-driven health systems. This review discusses the current methods available for evaluating technical skills in cataract surgery and the recent technological advancements that have enabled capture and analysis of large amounts of complex surgical data for more automated objective skills assessment.

[Artificial intelligence and its applications in medicine II: Current importance and practical applications]

Technology and medicine follow a parallel path during the last decades. Technological advances are changing the concept of health and health needs are influencing the development of technology. Artificial intelligence (AI) is made up of a series of sufficiently trained logical algorithms from which machines are capable of making decisions for specific cases based on general rules. This technology has applications in the diagnosis and follow-up of patients with an individualized prognostic evaluation of them. Furthermore, if we combine this technology with robotics, we can create intelligent machines that make more efficient diagnostic proposals in their work. Therefore, AI is going to be a technology present in our daily work through machines or computer programs, which in a more or less transparent way for the user, will become a daily reality in health processes. Health professionals have to know this technology, its advantages and disadvantages, because it will be an integral part of our work. In these two articles we intend to give a basic vision of this technology adapted to doctors with a review of its history and evolution, its real applications at the present time and a vision of a future in which AI and Big Data will shape the personalized medicine that will characterize the 21st century.