Artificial Intelligence in the Healthcare System: An Overview

Attitudes and perceptions of Thai medical students regarding artificial intelligence in radiology and medicine

INTRODUCTION

Artificial Intelligence (AI) has made a profound impact on the medical sector, particularly in radiology. The integration of AI knowledge into medical education is essential to equip future healthcare professionals with the skills needed to effectively leverage these advancements in their practices. Despite its significance, many medical schools have yet to incorporate AI into their curricula. This study aims to assess the attitudes of medical students in Thailand toward AI and its application in radiology, with the objective of better planning for its inclusion.

METHODS

Between February and June 2022, we conducted a survey in two Thai medical schools: Chiang Mai University in Northern Thailand and Prince of Songkla University in Southern Thailand. We employed 5-point Likert scale questions (ranging from strongly agree to strongly disagree) to evaluate students' opinions on three main aspects: (1) their understanding of AI, (2) the inclusion of AI in their medical education, and (3) the potential impact of AI on medicine and radiology.

RESULTS

Our findings revealed that merely 31% of medical students perceived to have a basic understanding of AI. Nevertheless, nearly all students (93.6%) recognized the value of AI training for their careers and strongly advocated for its inclusion in the medical school curriculum. Furthermore, those students who had a better understanding of AI were more likely to believe that AI would revolutionize the field of radiology (p = 0.02), making it more captivating and impactful (p = 0.04).

CONCLUSION

Our study highlights a noticeable gap in the understanding of AI among medical students in Thailand and its practical applications in healthcare. However, the overwhelming consensus among these students is their readiness to embrace the incorporation of AI training into their medical education. This enthusiasm holds the promise of enhancing AI adoption, ultimately leading to an improvement in the standard of healthcare services in Thailand, aligning with the country's healthcare vision.

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.

Artificial Intelligence Technologies in Cardiology

As the world produces exabytes of data, there is a growing need to find new methods that are more suitable for dealing with complex datasets. Artificial intelligence (AI) has significant potential to impact the healthcare industry, which is already on the road to change with the digital transformation of vast quantities of information. The implementation of AI has already achieved success in the domains of molecular chemistry and drug discoveries. The reduction in costs and in the time needed for experiments to predict the pharmacological activities of new molecules is a milestone in science. These successful applications of AI algorithms provide hope for a revolution in healthcare systems. A significant part of artificial intelligence is machine learning (ML), of which there are three main types-supervised learning, unsupervised learning, and reinforcement learning. In this review, the full scope of the AI workflow is presented, with explanations of the most-often-used ML algorithms and descriptions of performance metrics for both regression and classification. A brief introduction to explainable artificial intelligence (XAI) is provided, with examples of technologies that have developed for XAI. We review important AI implementations in cardiology for supervised, unsupervised, and reinforcement learning and natural language processing, emphasizing the used algorithm. Finally, we discuss the need to establish legal, ethical, and methodical requirements for the deployment of AI models in medicine.

Analysis of Publication Activity and Research Trends in the Field of AI Medical Applications: Network Approach

Artificial intelligence (AI) has revolutionized numerous industries, including medicine. In recent years, the integration of AI into medical practices has shown great promise in enhancing the accuracy and efficiency of diagnosing diseases, predicting patient outcomes, and personalizing treatment plans. This paper aims at the exploration of the AI-based medicine research using network approach and analysis of existing trends based on PubMed. Our findings are based on the results of PubMed search queries and analysis of the number of papers obtained by the different search queries. Our goal is to explore how are the AI-based methods used in healthcare research, which approaches and techniques are the most popular, and to discuss the potential reasoning behind the obtained results. Using analysis of the co-occurrence network constructed using VOSviewer software, we detected the main clusters of interest in AI-based healthcare research. Then, we proceeded with the thorough analysis of publication activity in various categories of medical AI research, including research on different AI-based methods applied to different types of medical data. We analyzed the results of query processing in the PubMed database over the past 5 years obtained via a specifically designed strategy for generating search queries based on the thorough selection of keywords from different categories of interest. We provide a comprehensive analysis of existing applications of AI-based methods to medical data of different modalities, including the context of various medical fields and specific diseases that carry the greatest danger to the human population.

Surgical Safety Checklist

Objectives: the surgical safety checklist (SSC) is a document that is intended to increase patient safety in the operating theater by eliminating avoidable errors. The original document has been published in English by the WHO which recommends its obligatory use. The document’s name is often distorted when translated into European languages, for instance into the “surgical control list”. This article aims to assess the consequences of the distortion of the originally intended meaning for the completion of SSC in the operating theater. Methods: we compared the exactness of the meaning of translation in 29 European languages based on Google translator. Particular attention was paid to the presence of essential words such as “checklist” and “safety” in the translation.Results: we found that in 15 out of the 29 languages, the translation of these two words was incorrect, particularly in Slavic languages. The most often mistranslation was the “control card” or “control list”, which was a misnomer.Conclusions: the translation of the SSC name into native languages is inadequate in about one-half of the cases, which may jeopardize its proper use by team members of the operating theater, and thus the patient perioperative safety.

Medical Records: A Historical Narrative

The history of medical records is thousand-year-long, with earlier roots in ancient civilizations. Until the 19th century, medical records mainly served educational purposes, later assuming other roles such as in insurance or legal procedures. This article comprehensively describes and reviews the development of medical records from ancient to modern times in Europe and North America, reflecting alterations and adaptations compliant with the mental and technological capabilities of a given period. We searched PubMed and Google Scholar databases to collect pertinent articles. English articles or those having English abstracts were considered. The search terms included "Medical Records," "Health Records," "History of Medicine," and "eHealth" and covered the last hundred years. References were also picked out from the identified articles. Overall, 600 articles were identified, 158 of which were judged thematically relevant. The general conclusion is that medical records undergo a revolutionary change from paper-based to electronic format, which reflects the development of eHealth systems. The migration process to eHealth records involves the use of artificial intelligence (AI) algorithms that streamline medical services by using faster and simpler working methods. AI benefits both patients and providers as it improves patient management and communication among medical centers, spares resources, identifies contamination or infections, and limits health costs. These advantages have become pointedly apparent during the recent COVID-19 scourge.

Surgical Safety Checklist: Polychromatic or Achromatic Design

The Surgical Safety Checklist (SSC) has been created based on the recommendations of the WHO and obligatorily introduced worldwide. SSC is used to increase the patient's safety and reduce complications while in the hospital, especially in the perioperative period. The original SSC template was of a multicolor polychromatic design. However, an achromatic black-and-white or gray-gray design on plain printer paper appears often used in clinical practice. This review aims to assess the level of SSC use in the polychromatic versus achromatic versions and the pros and cons of using either in practice. We used the Google browser for the identification and collection of SSC graphic images available as of June 2021 using the following search commands: "surgical safety checklist WHO" or "surgical safety checklist" or "SSC WHO." The commands were repeated in 103 languages representing the five continents with the back answers provided in 41 languages. The successive top 10 thematically relevant images or fewer if not available in the cases of some foreign languages were considered for analysis, providing a mean of 5 ±2 images per language. The numbers of achromatic and polychromatic two-color or multicolor images were calculated. The number of images corresponding to the respective color designs ranged as follows: 0-6 (27.6%), 0-9 (41.6%), and 0-6 (27.6%) We conclude that polychromatic imaging of SSC documents predominates in practical use. The polychromatic SSC design catches the doctor's eye, which likely increases the effectiveness of completing the document.

Artificial Intelligence and Precision Medicine: A Perspective

This article aims to present how the advanced solutions of artificial intelligence and precision medicine work together to refine medical management. Multi-omics seems the most suitable approach for biological analysis of data on precision medicine and artificial intelligence. We searched PubMed and Google Scholar databases to collect pertinent articles appearing up to 5 March 2021. Genetics, oncology, radiology, and the recent coronavirus disease (COVID-19) pandemic were chosen as representative fields addressing the cross-compliance of artificial intelligence (AI) and precision medicine based on the highest number of articles, topicality, and interconnectedness of the issue. Overall, we identified and perused 1572 articles. AI is a breakthrough that takes part in shaping the Fourth Industrial Revolution in medicine and health care, changing the long-time accepted diagnostic and treatment regimens and approaches. AI-based link prediction models may be outstandingly helpful in the literature search for drug repurposing or finding new therapeutical modalities in rapidly erupting wide-scale diseases such as the recent COVID-19.