Application of Artificial Intelligence in Medicine: An Overview

Attitudes Toward AI Usage in Patient Health Care: Evidence From a Population Survey Vignette Experiment

BACKGROUND

The integration of artificial intelligence (AI) holds substantial potential to alter diagnostics and treatment in health care settings. However, public attitudes toward AI, including trust and risk perception, are key to its ethical and effective adoption. Despite growing interest, empirical research on the factors shaping public support for AI in health care (particularly in large-scale, representative contexts) remains limited.

OBJECTIVE

This study aimed to investigate public attitudes toward AI in patient health care, focusing on how AI attributes (autonomy, costs, reliability, and transparency) shape perceptions of support, risk, and personalized care. In addition, it examines the moderating role of sociodemographic characteristics (gender, age, educational level, migration background, and subjective health status) in these evaluations. Our study offers novel insights into the relative importance of AI system characteristics for public attitudes and acceptance.

METHODS

We conducted a factorial vignette experiment with a probability-based survey of 3030 participants from Germany's general population. Respondents were presented with hypothetical scenarios involving AI applications in diagnosis and treatment in a hospital setting. Linear regression models assessed the relative influence of AI attributes on the dependent variables (support, risk perception, and personalized care), with additional subgroup analyses to explore heterogeneity by sociodemographic characteristics.

RESULTS

Mean values between 4.2 and 4.4 on a 1-7 scale indicate a generally neutral to slightly negative stance toward AI integration in terms of general support, risk perception, and personalized care expectations, with responses spanning the full scale from strong support to strong opposition. Among the 4 dimensions, reliability emerges as the most influential factor (percentage of explained variance [EV] of up to 10.5%). Respondents expect AI to not only prevent errors but also exceed current reliability standards while strongly disapproving of nontraceable systems (transparency is another important factor, percentage of EV of up to 4%). Costs and autonomy play a comparatively minor role (percentage of EVs of up to 1.5% and 1.3%), with preferences favoring collaborative AI systems over autonomous ones, and higher costs generally leading to rejection. Heterogeneity analysis reveals limited sociodemographic differences, with education and migration background influencing attitudes toward transparency and autonomy, and gender differences primarily affecting cost-related perceptions. Overall, attitudes do not substantially differ between AI applications in diagnosis versus treatment.

CONCLUSIONS

Our study fills a critical research gap by identifying the key factors that shape public trust and acceptance of AI in health care, particularly reliability, transparency, and patient-centered approaches. Our findings provide evidence-based recommendations for policy makers, health care providers, and AI developers to enhance trust and accountability, key concerns often overlooked in system development and real-world applications. The study highlights the need for targeted policy and educational initiatives to support the responsible integration of AI in patient care.

The Role of Artificial Intelligence in Drug Discovery and Pharmaceutical Development: A Paradigm Shift in the History of Pharmaceutical Industries

In today's world, with an increasing patient population, the need for medications is increasing rapidly. However, the current practice of drug development is time-consuming and requires a lot of investment by the pharmaceutical industries. Currently, it takes around 8-10 years and $3 billion of investment to develop a medication. Pharmaceutical industries and regulatory authorities are continuing to adopt new technologies to improve the efficiency of the drug development process. However, over the decades the pharmaceutical industries were not able to accelerate the drug development process. The pandemic (COVID-19) has taught the pharmaceutical industries and regulatory agencies an expensive lesson showing the need for emergency preparedness by accelerating the drug development process. Over the last few years, the pharmaceutical industries have been collaborating with artificial intelligence (AI) companies to develop algorithms and models that can be implemented at various stages of the drug development process to improve efficiency and reduce the developmental timelines significantly. In recent years, AI-screened drug candidates have entered clinical testing in human subjects which shows the interest of pharmaceutical companies and regulatory agencies. End-end integration of AI within the drug development process will benefit the industries for predicting the pharmacokinetic and pharmacodynamic profiles, toxicity, acceleration of clinical trials, study design, virtual monitoring of subjects, optimization of manufacturing process, analyzing and real-time monitoring of product quality, and regulatory preparedness. This review article discusses in detail the role of AI in various avenues of the pharmaceutical drug development process, its limitations, regulatory and future perspectives.

External Validation of a Novel Landmark-Based Deep Learning Automated Tibial Slope Measurement Algorithm Applied on Short Radiographs Obtained in Patients With ACL Injuries

Background

Deep learning algorithms can aid medical decision-making by performing routine tasks without any human error. Reading of standardized radiographs lends itself well to a computerized approach. The posterior tibial slope is increasingly recognized as a factor in lower leg biomechanics. Slope readings should, therefore, be readily available when considering knee ligament or knee replacement surgery.

Purpose/Hypothesis

The purpose was to externally validate a deep learning model developed for posterior tibial slope readings by applying an independent data set, not included in initial development, for testing the reliability of the model, compared with human performance testing. It was hypothesized that a computerized approach would yield a reliability similar to that of human analyses.

Study Design

Descriptive laboratory study.

Methods

A consecutive series of lateral knee radiographs obtained in patients undergoing anterior cruciate ligament surgery were eligible for inclusion. Two independent experienced clinicians individually assessed the tibial slope measurement to establish the interreader reliability. Furthermore, all images were processed by the newly developed model for the automated readings. Intrarater and interrater reliability were thereafter established between readers and between manual and automated readings, measured by intraclass correlation coefficients (ICCs). Time consumption between methods was noted. Extreme differences between the 2 methods were analyzed for potential errors.

Results

A total of 289 radiographs were included in the study and therefore analyzed by both the manual and the automated method. A mean tibial slope of 9.7° (SD, 2.7°; range, 3.0°-19.1°) was found. The interrater and intrarater measurements between the independent measurers for the 2-circle method were 0.86 and 0.92. Furthermore, the intrarater agreement of the model was 1.00, while an ICC between 0.73 and 0.80 was found when comparing automated with manual measurement. The mean time consumption for manual readings was 52.5 seconds, while for automated readings it was 28.2 seconds.

Conclusion

In this external validation of a newly developed model for automated readings of tibial slope measures, a perfect intrarater reliability and a good interrater reliability were seen. Although the model needs further refinement in reporting the tibial slope as compared with a gold standard manual measurement, it clearly demonstrates the elimination of human variance with repeat readings and less time consumption than that with human effort.

Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with this trend and continually update their knowledge. This narrative review discusses the application of artificial intelligence in the field of musculoskeletal imaging. For image generation, we focused on the clinical application of deep learning reconstruction and the recently emerging MRI-based cortical bone imaging. For automated diagnostic support, we provided an overview of qualitative diagnosis, including classifications essential for daily practice, and quantitative diagnosis, which can serve as imaging biomarkers for treatment decision making and prognosis prediction. Finally, we discussed current issues in the use of AI, the application of AI in the diagnosis of rare diseases, and the role of AI-based diagnostic imaging in preventive medicine as part of our outlook for the future.

Exploring the complex nature of implementation of Artificial intelligence in clinical practice: an interview study with healthcare professionals, researchers and Policy and Governance Experts

Artificial Intelligence (AI)-based tools have shown potential to optimize clinical workflows, enhance patient quality and safety, and facilitate personalized treatment. However, transitioning viable AI solutions to clinical implementation remains limited. To understand the challenges of bringing AI into clinical practice, we explored the experiences of healthcare professionals, researchers, and Policy and Governance Experts in hospitals. We conducted a qualitative study with thirteen semi-structured interviews (mean duration 52.1 ± 5.4 minutes) with healthcare professionals, researchers, and Policy and Governance Experts, with prior experience on AI development in hospitals. The interview guide was based on value, application, technology, governance, and ethics from the Innovation Funnel for Valuable AI in Healthcare, and the discussions were analyzed through thematic analysis. Six themes emerged: (1) demand-pull vs. tech-push: AI development focusing on innovative technologies may face limited success in large-scale clinical implementation. (2) Focus on generating knowledge, not solutions: Current AI initiatives often generate knowledge without a clear path for implementing AI models once proof-of-concept is achieved. (3) Lack of multidisciplinary collaboration: Successful AI initiatives require diverse stakeholder involvement, often hindered by late involvement and challenging communication. (4) Lack of appropriate skills: Stakeholders, including IT departments and healthcare professionals, often lack the required skills and knowledge for effective AI integration in clinical workflows. (5) The role of the hospital: Hospitals need a clear vision for integrating AI, including meeting preconditions in infrastructure and expertise. (6) Evolving laws and regulations: New regulations can hinder AI development due to unclear implications but also enforce standardization, emphasizing quality and safety in healthcare. In conclusion, this study highlights the complexity of AI implementation in clinical settings. Multidisciplinary collaboration is essential and requires facilitation. Balancing divergent perspectives is crucial for successful AI implementation. Hospitals need to assess their readiness for AI, develop clear strategies, standardize development processes, and foster better collaboration among stakeholders.

Large Language Models in Biochemistry Education: Comparative Evaluation of Performance

Background

Recent advancements in artificial intelligence (AI), particularly in large language models (LLMs), have started a new era of innovation across various fields, with medicine at the forefront of this technological revolution. Many studies indicated that at the current level of development, LLMs can pass different board exams. However, the ability to answer specific subject-related questions requires validation.

Objective

The objective of this study was to conduct a comprehensive analysis comparing the performance of advanced LLM chatbots-Claude (Anthropic), GPT-4 (OpenAI), Gemini (Google), and Copilot (Microsoft)-against the academic results of medical students in the medical biochemistry course.

Methods

We used 200 USMLE (United States Medical Licensing Examination)-style multiple-choice questions (MCQs) selected from the course exam database. They encompassed various complexity levels and were distributed across 23 distinctive topics. The questions with tables and images were not included in the study. The results of 5 successive attempts by Claude 3.5 Sonnet, GPT-4-1106, Gemini 1.5 Flash, and Copilot to answer this questionnaire set were evaluated based on accuracy in August 2024. Statistica 13.5.0.17 (TIBCO Software Inc) was used to analyze the data's basic statistics. Considering the binary nature of the data, the chi-square test was used to compare results among the different chatbots, with a statistical significance level of P<.05.

Results

On average, the selected chatbots correctly answered 81.1% (SD 12.8%) of the questions, surpassing the students' performance by 8.3% (P=.02). In this study, Claude showed the best performance in biochemistry MCQs, correctly answering 92.5% (185/200) of questions, followed by GPT-4 (170/200, 85%), Gemini (157/200, 78.5%), and Copilot (128/200, 64%). The chatbots demonstrated the best results in the following 4 topics: eicosanoids (mean 100%, SD 0%), bioenergetics and electron transport chain (mean 96.4%, SD 7.2%), hexose monophosphate pathway (mean 91.7%, SD 16.7%), and ketone bodies (mean 93.8%, SD 12.5%). The Pearson chi-square test indicated a statistically significant association between the answers of all 4 chatbots (P<.001 to P<.04).

Conclusions

Our study suggests that different AI models may have unique strengths in specific medical fields, which could be leveraged for targeted support in biochemistry courses. This performance highlights the potential of AI in medical education and assessment.

Learning-Based Models for Predicting IVIG Resistance and Coronary Artery Lesions in Kawasaki Disease: A Review of Technical Aspects and Study Features

Kawasaki disease (KD) is a common pediatric vasculitis, with coronary artery lesions (CALs) representing its most severe complication. Early identification of high-risk patients, including those with disease resistant to first-line treatments, is essential to guide personalized therapeutic approaches. Given the limited reliability of current scoring systems, there has been growing interest in the development of new prognostic models based on machine learning algorithms and artificial intelligence (AI). AI has the potential to revolutionize the management of KD by improving patient stratification and supporting more targeted treatment strategies. This narrative review examines recent applications of AI in stratifying patients with KD, with a particular focus on the ability of models to predict intravenous immunoglobulin resistance and the risk of CALs. We analyzed studies published between January 2019 and April 2024 that incorporated AI-based predictive models. In total, 21 papers met the inclusion criteria and were subject to technical and statistical review; 90% of these were conducted in patients from Asian hospitals. Most of the studies (18/21; 85.7%) were retrospective, and two-thirds included fewer than 1000 patients. Significant heterogeneity in study design and parameter selection was observed across the studies. Resistance to intravenous immunoglobulin emerged as a key factor in AI-based models for predicting CALs. Only five models demonstrated a sensitivity > 80%, and four studies provided access to the underlying algorithms and datasets. Challenges such as small sample sizes, class imbalance, and the need for multicenter validation currently limit the clinical applicability of machine-learning-based predictive models. The effectiveness of AI models is heavily influenced by the quantity and quality of data, labeling accuracy, and the completeness of the training datasets. Additionally, issues such as noise and missing data can negatively affect model performance and generalizability. These limitations highlight the need for rigorous validation and open access to model code to ensure transparency and reproducibility. Collaboration and data sharing will be essential for refining AI algorithms, improving patient stratification, and optimizing treatment strategies.

Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert

Artificial intelligence (AI) is increasingly being utilized to augment the practice of emergency medicine due to rapid technological advances and breakthroughs. AI applications have been used to enhance triage systems, predict disease-specific risk, estimate staffing needs, forecast patient decompensation, and interpret imaging findings in the emergency department setting. This article aims to help readers without formal training become informed end-users of AI in emergency medicine. The authors will briefly discuss the principles and key terminology of AI, the reasons for its rising popularity, its potential applications in the emergency department setting, and its limitations. Additionally, resources for further self-studying will also be provided.

Applying artificial intelligence to uncover the genetic landscape of coagulation factors

Artificial intelligence (AI) is rapidly advancing our ability to identify and interpret genetic variants associated with coagulation factor deficiencies. This review introduces AI, with a specific focus on machine learning (ML) methods, and examines its applications in the field of coagulation genetics over the past decade. We observed a significant increase in AI-related publications, with a focus on hemophilia A and B. ML approaches have shown promise in predicting the functional impact of genetic variants and establishing genotype-phenotype correlations, exemplified by tools like "Hema-Class" for factor VIII variants. However, some challenges remain, including the need to expand variant selection beyond missense mutations (which is now the standard of most studies). For the future, the integration of AI in calling, detecting, and interpreting genetic variants can significantly improve our ability to process large-scale genomic data. In this frame, we discuss various AI/ML-based tools for genetic variant detection and interpretation, highlighting their strengths and limitations. As the field evolves, the synergistic application of multiple AI models, coupled with rigorous validation strategies, will be crucial in advancing our understanding of coagulation disorders and for personalizing treatment approaches.

Comparison of ChatGPT-4o, Google Gemini 1.5 Pro, Microsoft Copilot Pro, and Ophthalmologists in the management of uveitis and ocular inflammation: A comparative study of large language models

PURPOSE

The aim of this study was to compare the latest large language models (LLMs) ChatGPT-4o, Google Gemini 1.5 Pro and Microsoft Copilot Pro developed by three different companies, with each other and with a group of ophthalmologists, to reveal the strengths and weaknesses of LLMs against each other and against ophthalmologists in the field of uveitis and ocular inflammation.

METHODS

Using a personal OphthoQuestions (www.ophthoquestions.com) account, a total of 100 questions from 201 questions on uveitis and ocular inflammation out of a total of 4551 questions on OphthoQuestions, including questions involving multimodal imaging, were included in the study using the randomization feature of the website. In November 2024, ChatGPT-4o, Microsoft Copilot Pro, and Google Gemini 1.5 Pro were asked the same 100 questions: 80 multiple-choice and 20 open-ended questions. Each question was categorized as either true or false. A statistical comparison of the accuracy rates was performed.

RESULTS

Among the 100 questions, ChatGPT-4o, Google Gemini 1.5 Pro, Microsoft Copilot Pro, and the human group (ophthalmologists) answered 80 (80.00%), 81 (81.00%), 80 (80.00%) and 72 (72.00%) questions, respectively, correctly. In the statistical comparisons between the groups for multiple-choice questions, no significant difference was found between the correct and incorrect response rates of the three LLMs and the human group (P=0.207, Cochran's Q test). In the statistical comparisons of responses to open-ended questions, there was no significant difference between the correct and incorrect response rates of the three LLMs and the human group (P=0.392, Cochran's Q test).

CONCLUSION

Although ChatGPT-4o, Google Gemini 1.5 Pro , and Microsoft Copilot Pro answered higher percentages of questions correctly than the human group, the LLMs were not statistically superior to each other or to the human group in the management of uveitis and ocular inflammation.

Risk-based evaluation of machine learning-based classification methods used for medical devices

BACKGROUND

In the future, more medical devices will be based on machine learning (ML) methods. In general, the consideration of risks is a crucial aspect for evaluating medical devices. Accordingly, risks and their associated costs should be taken into account when assessing the performance of ML-based medical devices. This paper addresses the following three research questions towards a risk-based evaluation with a focus on ML-based classification models.

METHODS

First, we analyzed how often risk-based metrics are currently utilized in the context of ML-based classification models. This was performed using a literature research based on a sample of recent scientific publications. Second, we introduce an approach for evaluating such models where expected risks and associated costs are integrated into the corresponding performance metrics. Additionally, we analyze the impact of different risk ratios on the resulting overall performance. Third, we elaborate how such risk-based approaches relate to regulatory requirements in the field of medical devices. A set of use case scenarios were utilized to demonstrate necessities and practical implications, in this regard.

RESULTS

First, it was shown that currently most scientific publications do not include risk-based approaches for measuring performance. Second, it was demonstrated that risk-based considerations have a substantial impact on the outcome. The relative increase of the resulting overall risks can go up to 196% when the ratio between different types of risks (false negatives vs. false positives) changes by a factor of 10.0. Third, we elaborated that risk-based considerations need to be included into the assessment of ML-based medical devices, according to the relevant EU regulations and standards. In particular, this applies when a substantial impact on the clinical outcome / in terms of the risk-benefit relationship occurs.

CONCLUSION

In summary, we demonstrated the necessity of a risk-based approach for the evaluation of medical devices which include ML-based classification methods. We showed that currently many scientific papers in this area do not include risk considerations. We developed basic steps towards a risk-based assessment of ML-based classifiers and elaborated consequences that could occur, when these steps are neglected. And, we demonstrated the consistency of our approach with current regulatory requirements in the EU.

Comparative assessment of three AI platforms in answering USMLE Step 1 anatomy questions or identifying anatomical structures on radiographs

The application of artificial intelligence (AI) in education has gained great attention recently. Integration of AI tools in anatomy teaching is currently engaging researchers and academics worldwide. Several AI chatbots have been generated, the most popular being ChatGPT (OpenAI: San Francisco, California, USA). Since its first public release in November 2022, several research papers have pointed to its potential role in anatomy education. However, it is not yet known whether it will prove superior to other available AI tools in this role. This article sheds some light on the current status of research concerning AI applications in anatomy education and compares the performances of three well-known chatbots (ChatGPT, Gemini, and Claude) in answering anatomy questions. A total of 23 questions were used as prompts for each chatbot. These questions comprised 10 knowledge-based, 10 analysis-based USMLE Step 1-type, and three radiographs. ChatGPT was the most accurate of the three, scoring 100% accuracy. However, in terms of comprehensiveness, Claude was the best; it gave very organized anatomical responses. Gemini performed less well than the other two, with a scored accuracy of 60% and less scientific explanations. On the basis of these findings, this study recommends the incorporation of Claude and ChatGPT in anatomy education, but not Gemini, at least in its current state.

ChatGPT vs. surgeons on pancreatic cancer queries: accuracy & empathy evaluated by patients and experts

BACKGROUND

Artificial intelligence (AI) offers potential support in patient-clinician interactions, but its impact on such communication remains unexplored.

METHODS

In this study, ChatGPT was compared with two pancreatic surgeons in responding to ten pancreatic cancer surgery-related questions, co-designed with the Patient Advisory Board of the Surgical Society's Study Center. A blind evaluation of these responses, considering content congruency and clarity for non-specialists, was conducted by patients and surgeons.

RESULTS

From June 23 to July 21, 2023, 24 patients and 25 surgeons participated, of which eleven patients and ten surgeons completed the survey in full. Utilizing a quantitative scale from 1 (strong-disagreement) to 5 (full-agreement), consensus was observed among patients and specialists concerning the content delivered by ChatGPT. The metrics for comprehensibility to a non-specialist audience consistently showed positive reception. In the evaluation of empathetic resonance, ChatGPT's responses mirrored those of the surgeons in the patient's view. A significant proportion ranked Surgeon 1's contributions foremost, followed closely by ChatGPT.

DISCUSSION

This study demonstrates that surgeons and ChatGPT answer common queries from patients regarding pancreatic cancer surgery comparable regarding reliability, lay comprehension and empathy as evaluated by patients and surgical experts. These findings highlight the potential of AI in enhancing patient-provider interactions.

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review

INTRODUCTION

Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology.

METHODS

We utilized PubMed to search for peer-reviewed research articles published between 2016 and 2021 with the query type "("deep learning" or "machine learning" or "neural network" or "artificial intelligence") and ("neoplas$" or "cancer$" or "tumor$" or "tumour$")." We included clinical trials and original research studies and excluded reviews and meta-analyses. Oncology-related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies.

RESULTS

Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%-93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. "White" vs. "non-White" or "White" vs. "Black").

CONCLUSIONS

We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non-White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology.

Artificial Intelligence in Breast Reconstruction: A Narrative Review

Breast reconstruction following mastectomy or sectorectomy significantly impacts the quality of life and psychological well-being of breast cancer patients. Since its inception in the 1950s, artificial intelligence (AI) has gradually entered the medical field, promising to transform surgical planning, intraoperative guidance, postoperative care, and medical research. This article examines AI applications in breast reconstruction, supported by recent studies. AI shows promise in enhancing imaging for tumor detection and surgical planning, improving microsurgical precision, predicting complications such as flap failure, and optimizing postoperative monitoring. However, challenges remain, including data quality, safety, algorithm transparency, and clinical integration. Despite these shortcomings, AI has the potential to revolutionize breast reconstruction by improving preoperative planning, surgical precision, operative efficiency, and patient outcomes. This review provides a foundation for further research as AI continues to evolve and clinical trials expand its applications, offering greater benefits to patients and healthcare providers.

Artificial intelligence integration in surgery through hand and instrument tracking: a systematic literature review

Objective

This systematic literature review of the integration of artificial intelligence (AI) applications in surgical practice through hand and instrument tracking provides an overview of recent advancements and analyzes current literature on the intersection of surgery with AI. Distinct AI algorithms and specific applications in surgical practice are also examined.

Methods

An advanced search using medical subject heading terms was conducted in Medline (via PubMed), SCOPUS, and Embase databases for articles published in English. A strict selection process was performed, adhering to PRISMA guidelines.

Results

A total of 225 articles were retrieved. After screening, 77 met inclusion criteria and were included in the review. Use of AI algorithms in surgical practice was uncommon during 2013-2017 but has gained significant popularity since 2018. Deep learning algorithms (n = 62) are increasingly preferred over traditional machine learning algorithms (n = 15). These technologies are used in surgical fields such as general surgery (n = 19), neurosurgery (n = 10), and ophthalmology (n = 9). The most common functional sensors and systems used were prerecorded videos (n = 29), cameras (n = 21), and image datasets (n = 7). The most common applications included laparoscopic (n = 13), robotic-assisted (n = 13), basic (n = 12), and endoscopic (n = 8) surgical skills training, as well as surgical simulation training (n = 8).

Conclusion

AI technologies can be tailored to address distinct needs in surgical education and patient care. The use of AI in hand and instrument tracking improves surgical outcomes by optimizing surgical skills training. It is essential to acknowledge the current technical and social limitations of AI and work toward filling those gaps in future studies.

AI-powered techniques in anatomical imaging: Impacts on veterinary diagnostics and surgery

BACKGROUND

Artificial intelligence (AI) is rapidly transforming veterinary diagnostic imaging, offering improved accuracy, speed, and efficiency in analyzing complex anatomical structures. AI-powered systems, including deep learning and convolutional neural networks, show promise in interpreting medical images from various modalities like X-rays, ultrasounds, CT scans, and MRI/mammography.

STUDY DESIGN

Narrative review OBJECTIVE: This review aims to explore the innovations and challenges of AI-enabled imaging tools in veterinary diagnostics and surgery, highlighting their potential impact on diagnostic accuracy, surgical risk mitigation, and personalized veterinary healthcare.

METHODS

We reviewed recent literature on AI applications in veterinary diagnostic imaging, focusing on their benefits, limitations, and future directions.

CONCLUSION

AI-enabled imaging tools hold immense potential for revolutionizing veterinary diagnostics and surgery. By enhancing diagnostic accuracy, enabling precise surgical planning, and supporting personalized treatment strategies, AI can significantly improve animal health outcomes. However, addressing challenges related to data privacy, algorithm bias, and integration into clinical workflows is crucial for the widespread adoption and success of these transformative technologies.

The multiple uses of artificial intelligence in exercise programs: a narrative review

Background

Artificial intelligence is based on algorithms that enable machines to perform tasks and activities that generally require human intelligence, and its use offers innovative solutions in various fields. Machine learning, a subset of artificial intelligence, concentrates on empowering computers to learn and enhance from data autonomously; this narrative review seeks to elucidate the utilization of artificial intelligence in fostering physical activity, training, exercise, and health outcomes, addressing a significant gap in the comprehension of practical applications.

Methods

Only Randomized Controlled Trials (RCTs) published in English were included. Inclusion criteria: all RCTs that use artificial intelligence to program, supervise, manage, or assist physical activity, training, exercise, or health programs. Only studies published from January 1, 2014, were considered. Exclusion criteria: all the studies that used robot-assisted, robot-supported, or robotic training were excluded.

Results

A total of 1772 studies were identified. After the first stage, where the duplicates were removed, 1,004 articles were screened by title and abstract. A total of 24 studies were identified, and finally, after a full-text review, 15 studies were identified as meeting all eligibility criteria for inclusion. The findings suggest that artificial intelligence holds promise in promoting physical activity across diverse populations, including children, adolescents, adults, older adult, and individuals with disabilities.

Conclusion

Our research found that artificial intelligence, machine learning and deep learning techniques were used: (a) as part of applications to generate automatic messages and be able to communicate with users; (b) as a predictive approach and for gesture and posture recognition; (c) as a control system; (d) as data collector; and (e) as a guided trainer.

Factors Associated With the Accuracy of Large Language Models in Basic Medical Science Examinations: Cross-Sectional Study

Background

Artificial intelligence (AI) has become widely applied across many fields, including medical education. Content validation and its answers are based on training datasets and the optimization of each model. The accuracy of large language model (LLMs) in basic medical examinations and factors related to their accuracy have also been explored.

Objective

We evaluated factors associated with the accuracy of LLMs (GPT-3.5, GPT-4, Google Bard, and Microsoft Bing) in answering multiple-choice questions from basic medical science examinations.

Methods

We used questions that were closely aligned with the content and topic distribution of Thailand's Step 1 National Medical Licensing Examination. Variables such as the difficulty index, discrimination index, and question characteristics were collected. These questions were then simultaneously input into ChatGPT (with GPT-3.5 and GPT-4), Microsoft Bing, and Google Bard, and their responses were recorded. The accuracy of these LLMs and the associated factors were analyzed using multivariable logistic regression. This analysis aimed to assess the effect of various factors on model accuracy, with results reported as odds ratios (ORs).

Results

The study revealed that GPT-4 was the top-performing model, with an overall accuracy of 89.07% (95% CI 84.76%-92.41%), significantly outperforming the others (P<.001). Microsoft Bing followed with an accuracy of 83.69% (95% CI 78.85%-87.80%), GPT-3.5 at 67.02% (95% CI 61.20%-72.48%), and Google Bard at 63.83% (95% CI 57.92%-69.44%). The multivariable logistic regression analysis showed a correlation between question difficulty and model performance, with GPT-4 demonstrating the strongest association. Interestingly, no significant correlation was found between model accuracy and question length, negative wording, clinical scenarios, or the discrimination index for most models, except for Google Bard, which showed varying correlations.

Conclusions

The GPT-4 and Microsoft Bing models demonstrated equal and superior accuracy compared to GPT-3.5 and Google Bard in the domain of basic medical science. The accuracy of these models was significantly influenced by the item's difficulty index, indicating that the LLMs are more accurate when answering easier questions. This suggests that the more accurate models, such as GPT-4 and Bing, can be valuable tools for understanding and learning basic medical science concepts.

ChatGPT is an Unreliable Source of Peer-Reviewed Information for Common Total Knee and Hip Arthroplasty Patient Questions

Background: Advances in artificial intelligence (AI), machine learning, and publicly accessible language model tools such as ChatGPT-3.5 continue to shape the landscape of modern medicine and patient education. ChatGPT's open access (OA), instant, human-sounding interface capable of carrying discussion on myriad topics makes it a potentially useful resource for patients seeking medical advice. As it pertains to orthopedic surgery, ChatGPT may become a source to answer common preoperative questions regarding total knee arthroplasty (TKA) and total hip arthroplasty (THA). Since ChatGPT can utilize the peer-reviewed literature to source its responses, this study seeks to characterize the validity of its responses to common TKA and THA questions and characterize the peer-reviewed literature that it uses to formulate its responses. Methods: Preoperative TKA and THA questions were formulated by fellowship-trained adult reconstruction surgeons based on common questions posed by patients in the clinical setting. Questions were inputted into ChatGPT with the initial request of using solely the peer-reviewed literature to generate its responses. The validity of each response was rated on a Likert scale by the fellowship-trained surgeons, and the sources utilized were characterized in terms of accuracy of comparison to existing publications, publication date, study design, level of evidence, journal of publication, journal impact factor based on the clarivate analytics factor tool, journal OA status, and whether the journal is based in the United States. Results: A total of 109 sources were cited by ChatGPT in its answers to 17 questions regarding TKA procedures and 16 THA procedures. Thirty-nine sources (36%) were deemed accurate or able to be directly traced to an existing publication. Of these, seven (18%) were identified as duplicates, yielding a total of 32 unique sources that were identified as accurate and further characterized. The most common characteristics of these sources included dates of publication between 2011 and 2015 (10), publication in The Journal of Bone and Joint Surgery (13), journal impact factors between 5.1 and 10.0 (17), internationally based journals (17), and journals that are not OA (28). The most common study designs were retrospective cohort studies and case series (seven each). The level of evidence was broadly distributed between Levels I, III, and IV (seven each). The averages for the Likert scales for medical accuracy and completeness were 4.4/6 and 1.92/3, respectively. Conclusions: Investigation into ChatGPT's response quality and use of peer-reviewed sources when prompted with archetypal pre-TKA and pre-THA questions found ChatGPT to provide mostly reliable responses based on fellowship-trained orthopedic surgeon review of 4.4/6 for accuracy and 1.92/3 for completeness despite a 64.22% rate of citing inaccurate references. This study suggests that until ChatGPT is proven to be a reliable source of valid information and references, patients must exercise extreme caution in directing their pre-TKA and THA questions to this medium.

Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects

The medical metaverse is a combination of medicine, computer science, information technology and other cutting-edge technologies. It redefines the method of information interaction about doctor-patient communication, medical education and research through the integration of medical data, knowledge and services in a virtual environment. Artificial intelligence (AI) is a discipline that uses computer technology to study and develop human intelligence. AI has infiltrated every aspect of medical metaverse and is deeply integrated with the technologies that build medical metaverse, such as large language models (LLMs), digital twins, blockchain and extended reality (including VR/AR/XR). AI has become an integral part of the medical metaverse building process. Moreover, AI also provides richer medical metaverse functions, including diagnosis, education, and consulting. This paper aims to introduce how AI supports the development of medical metaverse, including its specific application scenarios, shortcomings and future development. Our goal is to contribute to the advancement of more sophisticated and intelligent medical methods.

Physicians' ethical concerns about artificial intelligence in medicine: a qualitative study: "The final decision should rest with a human"

Background/aim

Artificial Intelligence (AI) is the capability of computational systems to perform tasks that require human-like cognitive functions, such as reasoning, learning, and decision-making. Unlike human intelligence, AI does not involve sentience or consciousness but focuses on data processing, pattern recognition, and prediction through algorithms and learned experiences. In healthcare including neuroscience, AI is valuable for improving prevention, diagnosis, prognosis, and surveillance.

Methods

This qualitative study aimed to investigate the acceptability of AI in Medicine (AIIM) and to elucidate any technical and scientific, as well as social and ethical issues involved. Twenty-five doctors from various specialties were carefully interviewed regarding their views, experience, knowledge, and attitude toward AI in healthcare.

Results

Content analysis confirmed the key ethical principles involved: confidentiality, beneficence, and non-maleficence. Honesty was the least invoked principle. A thematic analysis established four salient topic areas, i.e., advantages, risks, restrictions, and precautions. Alongside the advantages, there were many limitations and risks. The study revealed a perceived need for precautions to be embedded in healthcare policies to counter the risks discussed. These precautions need to be multi-dimensional.

Conclusion

The authors conclude that AI should be rationally guided, function transparently, and produce impartial results. It should assist human healthcare professionals collaboratively. This kind of AI will permit fairer, more innovative healthcare which benefits patients and society whilst preserving human dignity. It can foster accuracy and precision in medical practice and reduce the workload by assisting physicians during clinical tasks. AIIM that functions transparently and respects the public interest can be an inspiring scientific innovation for humanity.

AI-Assisted Detection and Localization of Spinal Metastatic Lesions

OBJECTIVES

The integration of machine learning and radiomics in medical imaging has significantly advanced diagnostic and prognostic capabilities in healthcare. This study focuses on developing and validating an artificial intelligence (AI) model using U-Net architectures for the accurate detection and segmentation of spinal metastases from computed tomography (CT) images, addressing both osteolytic and osteoblastic lesions.

METHODS

Our methodology employs multiple variations of the U-Net architecture and utilizes two distinct datasets: one consisting of 115 polytrauma patients for vertebra segmentation and another comprising 38 patients with documented spinal metastases for lesion detection.

RESULTS

The model demonstrated strong performance in vertebra segmentation, achieving Dice Similarity Coefficient (DSC) values between 0.87 and 0.96. For metastasis segmentation, the model achieved a DSC of 0.71 and an F-beta score of 0.68 for lytic lesions but struggled with sclerotic lesions, obtaining a DSC of 0.61 and an F-beta score of 0.57, reflecting challenges in detecting dense, subtle bone alterations. Despite these limitations, the model successfully identified isolated metastatic lesions beyond the spine, such as in the sternum, indicating potential for broader skeletal metastasis detection.

CONCLUSIONS

The study concludes that AI-based models can augment radiologists' capabilities by providing reliable second-opinion tools, though further refinements and diverse training data are needed for optimal performance, particularly for sclerotic lesion segmentation. The annotated CT dataset produced and shared in this research serves as a valuable resource for future advancements.

The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review

BACKGROUND

Large volumes of data increasing over time lead to a shortage of radiologists' time. The use of systems based on artificial intelligence (AI) offers opportunities to relieve the burden on radiologists. The AI systems are usually optimized for a radiological area. Radiologists must understand the basic features of its technical function in order to be able to assess the weaknesses and possible errors of the system and use the strengths of the system. This "explainability" creates trust in an AI system and shows its limits.

METHOD

Based on an expanded Medline search for the key words "radiology, artificial intelligence, referring physician interaction, patient interaction, job satisfaction, communication of findings, expectations", subjective additional relevant articles were considered for this narrative review.

RESULTS

The use of AI is well advanced, especially in radiology. The programmer should provide the radiologist with clear explanations as to how the system works. All systems on the market have strengths and weaknesses. Some of the optimizations are unintentionally specific, as they are often adapted too precisely to a certain environment that often does not exist in practice - this is known as "overfitting". It should also be noted that there are specific weak points in the systems, so-called "adversarial examples", which lead to fatal misdiagnoses by the AI even though these cannot be visually distinguished from an unremarkable finding by the radiologist. The user must know which diseases the system is trained for, which organ systems are recognized and taken into account by the AI, and, accordingly, which are not properly assessed. This means that the user can and must critically review the results and adjust the findings if necessary. Correctly applied AI can result in a time savings for the radiologist. If he knows how the system works, he only has to spend a short amount of time checking the results. The time saved can be used for communication with patients and referring physicians and thus contribute to higher job satisfaction.

CONCLUSION

Radiology is a constantly evolving specialty with enormous responsibility, as radiologists often make the diagnosis to be treated. AI-supported systems should be used consistently to provide relief and support. Radiologists need to know the strengths, weaknesses, and areas of application of these AI systems in order to save time. The time gained can be used for communication with patients and referring physicians.

KEY POINTS

· Explainable AI systems help to improve workflow and to save time.. · The physician must critically review AI results, under consideration of the limitations of the AI.. · The AI system will only provide useful results if it has been adapted to the data type and data origin.. · The communicating radiologist interested in the patient is important for the visibility of the discipline..

CITATION FORMAT

· Stueckle CA, Haage P. The radiologist as a physician - artificial intelligence as a way to overcome tension between the patient, technology, and referring physicians - a narrative review. Fortschr Röntgenstr 2024; 196: 1115 - 1123.

The Intraoperative Role of Artificial Intelligence Within General Surgery: A Systematic Review

The role of artificial intelligence has been explored in many industries across the world. The medical field is no exception with studies regarding its use for development of algorithms in cancer screening and its diagnostic utility in clinical radiology. This study aims to review current literature on intraoperative use of artificial intelligence within general surgery to identify the latest developments, the major challenges and the trajectory of this field. A literature search was done on PubMed on May 28, 2024, using the terms: ((artificial intelligence) AND (general surgery)). Only publications in English and studies involving human subjects were considered. Exclusion criteria included duplicate papers, irrelevant titles, abstracts, themes, and non-English papers. A literature search on PubMed yielded 13 relevant articles. Among these, five articles focused on intraoperative guidance, four addressed surgical education and training, and four were survey-based exploring perceptions regarding artificial intelligence. Key themes included the development of artificial intelligence-based autonomous actions during surgery and its role in enhancing surgical training. Limitations identified included restricted data availability, ethical concerns, and a lack of validation tools, which pose significant obstacles to progress in this area. Despite existing limitations, the potential for integrating artificial intelligence into general surgery is promising. Careful attention is needed to overcome challenges and maximize its benefits.

Investigating the role of artificial intelligence in predicting perceived dysphonia level

PURPOSE

This study aims to investigate the role of one of these models in the field of voice pathology and compare its performance in distinguishing the perceived dysphonia level.

METHODS

Demographic information, voice self-assessments, and acoustic measurements related to a sample of 50 adult dysphonic outpatients were presented to ChatGPT and Perplexity AI chatbots, which were interrogated for the perceived dysphonia level.

RESULTS

The agreement between the auditory-perceptual assessment by experts and ChatGPT and Perplexity AI chatbots, as determined by Cohen's Kappa, was not statistically significant (p = 0.429). There was also a low positive correlation (rs = 0.30, p = 0.03) between the diagnosis made by ChatGPT and Perplexity AI chatbots (rs = 0.30, p = 0.03).

CONCLUSION

It seems that AI could not play a vital role in helping the voice care teams determine the perceptual level of dysphonia.

Genome-wide pan-GPCR cell libraries accelerate drug discovery

G protein-coupled receptors (GPCRs) are pivotal in mediating diverse physiological and pathological processes, rendering them promising targets for drug discovery. GPCRs account for about 40% of FDA-approved drugs, representing the most successful drug targets. However, only approximately 15% of the 800 human GPCRs are targeted by market drugs, leaving numerous opportunities for drug discovery among the remaining receptors. Cell expression systems play crucial roles in the GPCR drug discovery field, including novel target identification, structural and functional characterization, potential ligand screening, signal pathway elucidation, and drug safety evaluation. Here, we discuss the principles, applications, and limitations of widely used cell expression systems in GPCR-targeted drug discovery, GPCR function investigation, signal pathway characterization, and pharmacological property studies. We also propose three strategies for constructing genome-wide pan-GPCR cell libraries, which will provide a powerful platform for GPCR ligand screening, and facilitate the study of GPCR mechanisms and drug safety evaluation, ultimately accelerating the process of GPCR-targeted drug discovery.

The Application of Deep Learning to Accurately Identify the Dimensions of Spinal Canal and Intervertebral Foramen as Evaluated by the IoU Index

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Optical neural networks: progress and challenges

Artificial intelligence has prevailed in all trades and professions due to the assistance of big data resources, advanced algorithms, and high-performance electronic hardware. However, conventional computing hardware is inefficient at implementing complex tasks, in large part because the memory and processor in its computing architecture are separated, performing insufficiently in computing speed and energy consumption. In recent years, optical neural networks (ONNs) have made a range of research progress in optical computing due to advantages such as sub-nanosecond latency, low heat dissipation, and high parallelism. ONNs are in prospect to provide support regarding computing speed and energy consumption for the further development of artificial intelligence with a novel computing paradigm. Herein, we first introduce the design method and principle of ONNs based on various optical elements. Then, we successively review the non-integrated ONNs consisting of volume optical components and the integrated ONNs composed of on-chip components. Finally, we summarize and discuss the computational density, nonlinearity, scalability, and practical applications of ONNs, and comment on the challenges and perspectives of the ONNs in the future development trends.

A quantitative analysis of artificial intelligence research in cervical cancer: a bibliometric approach utilizing CiteSpace and VOSviewer

Background

Cervical cancer, a severe threat to women's health, is experiencing a global increase in incidence, notably among younger demographics. With artificial intelligence (AI) making strides, its integration into medical research is expanding, particularly in cervical cancer studies. This bibliometric study aims to evaluate AI's role, highlighting research trends and potential future directions in the field.

Methods

This study systematically retrieved literature from the Web of Science Core Collection (WoSCC), employing VOSviewer and CiteSpace for analysis. This included examining collaborations and keyword co-occurrences, with a focus on the relationship between citing and cited journals and authors. A burst ranking analysis identified research hotspots based on citation frequency.

Results

The study analyzed 927 articles from 2008 to 2024 by 5,299 authors across 81 regions. China, the U.S., and India were the top contributors, with key institutions like the Chinese Academy of Sciences and the NIH leading in publications. Schiffman, Mark, featured among the top authors, while Jemal, A, was the most cited. 'Diagnostics' and 'IEEE Access' stood out for publication volume and citation impact, respectively. Keywords such as 'cervical cancer,' 'deep learning,' 'classification,' and 'machine learning' were dominant. The most cited article was by Berner, ES; et al., published in 2008.

Conclusions

AI's application in cervical cancer research is expanding, with a growing scholarly community. The study suggests that AI, especially deep learning and machine learning, will remain a key research area, focusing on improving diagnostics and treatment. There is a need for increased international collaboration to maximize AI's potential in advancing cervical cancer research and patient care.

Will the Artificial Intelligence Touch Substitute for the Human Touch?

Nowadays, artificial intelligence is used in many fields to diagnose and treat different diseases. Robots are also useful tools that substitute for human work. Despite robots being used also for touch therapy, can they substitute for the human touch? Human touch has a strong social component, and it is necessary for the correct development of newborns and the treatment of pathological situations. To substitute human touch, it is necessary to integrate robots with artificial intelligence as well as with sensors that mimic human skin. Today, the question remains without answer: Can human touch be substituted with AI in its social and affiliative components?

Assessment of ChatGPT generated educational material for head and neck surgery counseling

BACKGROUND

ChatGPT is becoming very popular as an information source for the public. The adequacy of ChatGPT generated patient counseling material has not yet been extensively assessed.

METHODS

ChatGPT was presented with perioperative counseling and complication questions regarding five different procedure, and accuracy of responses was assessed. The chat was then asked to present an explanation of each procedure, and quality of the responses were compared to online educational material.

RESULTS

ChatGPT responses were comprehensive when discussing counseling points commonly discussed by a provider prior to a procedure. Responses to questions on surgical complications were less accurate and comprehensive. In comparison to online educational material, ChatGPT scored at or above the median SAM and PEMAT scores for all procedures.

CONCLUSIONS

ChatGPT did well addressing basic counseling points during the perioperative period, although it did not perform as well when addressing surgical complications. Chat response quality was comparable to currently available online educational material.

Automated Interpretation of Lung Sounds by Deep Learning in Children With Asthma: Scoping Review and Strengths, Weaknesses, Opportunities, and Threats Analysis

BACKGROUND

The interpretation of lung sounds plays a crucial role in the appropriate diagnosis and management of pediatric asthma. Applying artificial intelligence (AI) to this task has the potential to better standardize assessment and may even improve its predictive potential.

OBJECTIVE

This study aims to objectively review the literature on AI-assisted lung auscultation for pediatric asthma and provide a balanced assessment of its strengths, weaknesses, opportunities, and threats.

METHODS

A scoping review on AI-assisted lung sound analysis in children with asthma was conducted across 4 major scientific databases (PubMed, MEDLINE Ovid, Embase, and Web of Science), supplemented by a gray literature search on Google Scholar, to identify relevant studies published from January 1, 2000, until May 23, 2023. The search strategy incorporated a combination of keywords related to AI, pulmonary auscultation, children, and asthma. The quality of eligible studies was assessed using the ChAMAI (Checklist for the Assessment of Medical Artificial Intelligence).

RESULTS

The search identified 7 relevant studies out of 82 (9%) to be included through an academic literature search, while 11 of 250 (4.4%) studies from the gray literature search were considered but not included in the subsequent review and quality assessment. All had poor to medium ChAMAI scores, mostly due to the absence of external validation. Identified strengths were improved predictive accuracy of AI to allow for prompt and early diagnosis, personalized management strategies, and remote monitoring capabilities. Weaknesses were the heterogeneity between studies and the lack of standardization in data collection and interpretation. Opportunities were the potential of coordinated surveillance, growing data sets, and new ways of collaboratively learning from distributed data. Threats were both generic for the field of medical AI (loss of interpretability) but also specific to the use case, as clinicians might lose the skill of auscultation.

CONCLUSIONS

To achieve the opportunities of automated lung auscultation, there is a need to address weaknesses and threats with large-scale coordinated data collection in globally representative populations and leveraging new approaches to collaborative learning.

General surgery educational resources for Jordanian medical students

Background

To outline the resources deemed most beneficial to medical students during their general surgery clerkship, as well as to examine their link to students' general surgery scores and the usage of artificial intelligence in general surgery study.

Methods

A retrospective survey of Jordanian medical students from six universities was done between March and June 2023 using a 7-item questionnaire covering questions concerning general surgery study methods and scores. Descriptive statistics were used to evaluate demographic data. Chi-square is used to evaluate categorical data, with a P value <0.05 deemed significant.

Results

The average age of respondents was 23.3 years, and 54.2 % of the respondents were females, 47.8 % were from Mutah University. Most students (48.2 %) relied on tutor lectures. Students who studied through instructor lectures had the highest grades (9 % excellent, 17 % very good), followed by students who studied using surgery textbooks (6.8 % and 14.6 %, respectively). The relationship between the study method and academic achievement was statistically significant (P < 0.05).

Conclusions

Traditional face-to-face learning with instructor lectures and surgery textbooks is still the most efficient approach to attain the greatest scores. Medical students are still underutilizing artificial intelligence.

Attitude of aspiring orthopaedic surgeons towards artificial intelligence: a multinational cross-sectional survey study

INTRODUCTION

The purpose of this study was to evaluate the perspectives of aspiring orthopaedic surgeons on artificial intelligence (AI), analysing how gender, AI knowledge, and technical inclination influence views on AI. Additionally, the extent to which recent AI advancements sway career decisions was assessed.

MATERIALS AND METHODS

A digital survey was distributed to student members of orthopaedic societies across Germany, Switzerland, and Austria. Subgroup analyses explored how gender, AI knowledge, and technical inclination shape attitudes towards AI.

RESULTS

Of 174 total respondents, 86.2% (n = 150) intended to pursue a career in orthopaedic surgery and were included in the analysis. The majority (74.5%) reported 'basic' or 'no' knowledge about AI. Approximately 29.3% believed AI would significantly impact orthopaedics within 5 years, with another 35.3% projecting 5-10 years. AI was predominantly seen as an assistive tool (77.8%), without significant fear of job displacement. The most valued AI applications were identified as preoperative implant planning (85.3%), administrative tasks (84%), and image analysis (81.3%). Concerns arose regarding skill atrophy due to overreliance (69.3%), liability (68%), and diminished patient interaction (56%). The majority maintained a 'neutral' view on AI (53%), though 32.9% were 'enthusiastic'. A stronger focus on AI in medical education was requested by 81.9%. Most participants (72.8%) felt recent AI advancements did not alter their career decisions towards or away from the orthopaedic specialty. Statistical analysis revealed a significant association between AI literacy (p = 0.015) and technical inclination (p = 0.003). AI literacy did not increase significantly during medical education (p = 0.091).

CONCLUSIONS

Future orthopaedic surgeons exhibit a favourable outlook on AI, foreseeing its significant influence in the near future. AI literacy remains relatively low and showed no improvement during medical school. There is notable demand for improved AI-related education. The choice of orthopaedics as a specialty appears to be robust against the sway of recent AI advancements.

LEVEL OF EVIDENCE

Cross-sectional survey study; level IV.

The Role and Applications of Artificial Intelligence in Dental Implant Planning: A Systematic Review

Artificial intelligence (AI) is revolutionizing dentistry, offering new opportunities to improve the precision and efficiency of implantology. This literature review aims to evaluate the current evidence on the use of AI in implant planning assessment. The analysis was conducted through PubMed and Scopus search engines, using a combination of relevant keywords, including "artificial intelligence implantology", "AI implant planning", "AI dental implant", and "implantology artificial intelligence". Selected articles were carefully reviewed to identify studies reporting data on the effectiveness of AI in implant planning. The results of the literature review indicate a growing interest in the application of AI in implant planning, with evidence suggesting an improvement in precision and predictability compared to traditional methods. The summary of the obtained findings by the included studies represents the latest AI developments in implant planning, demonstrating its application for the automated detection of bones, the maxillary sinus, neuronal structure, and teeth. However, some disadvantages were also identified, including the need for high-quality training data and the lack of standardization in protocols. In conclusion, the use of AI in implant planning presents promising prospects for improving clinical outcomes and optimizing patient management. However, further research is needed to fully understand its potential and address the challenges associated with its implementation in clinical practice.

Artificial intelligence-assisted ultrasound-guided regional anaesthesia: An explorative scoping review

Purpose

The present study reviews the available scientific literature on artificial intelligence (AI)-assisted ultrasound-guided regional anaesthesia (UGRA) and evaluates the reported intraprocedural parameters and postprocedural outcomes.

Methods

A literature search was performed on 19 September 2023, using the Medline, EMBASE, CINAHL, Cochrane Library and Google Scholar databases by experts in electronic searching. All study designs were considered with no restrictions regarding patient characteristics or cohort size. Outcomes assessed included the accuracy of AI-model tracking, success at the first attempt, differences in outcomes between AI-assisted and unassisted UGRA, operator feedback and case-report data.

Results

A joint adaptive median binary pattern (JAMBP) has been applied to improve the tracking procedure, while a particle filter (PF) is involved in feature extraction. JAMBP combined with PF was most accurate on all images for landmark identification, with accuracy scores of 0.83, 0.93 and 0.93 on original, preprocessed and filtered images, respectively. Evaluation of first-attempt success of spinal needle insertion revealed first-attempt success in most patients. When comparing AI application versus UGRA alone, a significant statistical difference (p < 0.05) was found for correct block view, correct structure identification and decrease in mean injection time, needle track adjustments and bone encounters in favour of having AI assistance. Assessment of operator feedback revealed that expert and nonexpert operator feedback was overall positive.

Conclusion

AI appears promising to enhance UGRA as well as to positively influence operator training. AI application of UGRA may improve the identification of anatomical structures and provide guidance for needle placement, reducing the risk of complications and improving patient outcomes.

Level of Evidence

Level IV.

Orthopaedic surgeons display a positive outlook towards artificial intelligence: A survey among members of the AGA Society for Arthroscopy and Joint Surgery

Purpose

The purpose of this study was to evaluate the perspective of orthopaedic surgeons on the impact of artificial intelligence (AI) and to evaluate the influence of experience, workplace setting and familiarity with digital solutions on views on AI.

Methods

Orthopaedic surgeons of the AGA Society for Arthroscopy and Joint Surgery were invited to participate in an online, cross-sectional survey designed to gather information on professional background, subjective AI knowledge, opinion on the future impact of AI, openness towards different applications of AI, and perceived advantages and disadvantages of AI. Subgroup analyses were performed to examine the influence of experience, workplace setting and openness towards digital solutions on perspectives towards AI.

Results

Overall, 360 orthopaedic surgeons participated. The majority indicated average (43.6%) or rudimentary (38.1%) AI knowledge. Most (54.5%) expected AI to substantially influence orthopaedics within 5-10 years, predominantly as a complementary tool (91.1%). Preoperative planning (83.8%) was identified as the most likely clinical use case. A lack of consensus was observed regarding acceptable error levels. Time savings in preoperative planning (62.5%) and improved documentation (81%) were identified as notable advantages while declining skills of the next generation (64.5%) were rated as the most substantial drawback. There were significant differences in subjective AI knowledge depending on participants' experience (p = 0.021) and familiarity with digital solutions (p < 0.001), acceptable error levels depending on workplace setting (p = 0.004), and prediction of AI impact depending on familiarity with digital solutions (p < 0.001).

Conclusion

The majority of orthopaedic surgeons in this survey anticipated a notable positive impact of AI on their field, primarily as an assistive technology. A lack of consensus on acceptable error levels of AI and concerns about declining skills among future surgeons were observed.

Level of Evidence

Level IV, cross-sectional study.

Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students

Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.

The Role of Artificial Intelligence in Healthcare: Enhancing Coronary Computed Tomography Angiography for Coronary Artery Disease Management

This review aims to explore the potential of artificial intelligence (AI) in coronary CT angiography (CCTA), a key tool for diagnosing coronary artery disease (CAD). Because CAD is still a major cause of death worldwide, effective and accurate diagnostic methods are required to identify and manage the condition. CCTA certainly is a noninvasive alternative for diagnosing CAD, but it requires a large amount of data as input. We intend to discuss the idea of incorporating AI into CCTA, which enhances its diagnostic accuracy and operational efficiency. Using such AI technologies as machine learning (ML) and deep learning (DL) tools, CCTA images are automated to perfection and the analysis is significantly refined. It enables the characterization of a plaque, assesses the severity of the stenosis, and makes more accurate risk stratifications than traditional methods, with pinpoint accuracy. Automating routine tasks through AI-driven CCTA will reduce the radiologists' workload considerably, which is a standard benefit of such technologies. More importantly, it would enable radiologists to allocate more time and expertise to complex cases, thereby improving overall patient care. However, the field of AI in CCTA is not without its challenges, which include data protection, algorithm transparency, as well as criteria for standardization encoding. Despite such obstacles, it appears that the integration of AI technology into CCTA in the future holds great promise for keeping CAD itself in check, thereby aiding the fight against this disease and begetting better clinical outcomes and more optimized modes of healthcare. Future research on AI algorithms for CCTA, making ethical use of AI, and thereby overcoming the technical and clinical barriers to widespread adoption of this new tool, will hopefully pave the way for profound AI-driven transformations in healthcare.

Application of ChatGPT for Orthopedic Surgeries and Patient Care

Artificial intelligence (AI) has rapidly transformed various aspects of life, and the launch of the chatbot "ChatGPT" by OpenAI in November 2022 has garnered significant attention and user appreciation. ChatGPT utilizes natural language processing based on a "generative pre-trained transfer" (GPT) model, specifically the transformer architecture, to generate human-like responses to a wide range of questions and topics. Equipped with approximately 57 billion words and 175 billion parameters from online data, ChatGPT has potential applications in medicine and orthopedics. One of its key strengths is its personalized, easy-to-understand, and adaptive response, which allows it to learn continuously through user interaction. This article discusses how AI, especially ChatGPT, presents numerous opportunities in orthopedics, ranging from preoperative planning and surgical techniques to patient education and medical support. Although ChatGPT's user-friendly responses and adaptive capabilities are laudable, its limitations, including biased responses and ethical concerns, necessitate its cautious and responsible use. Surgeons and healthcare providers should leverage the strengths of the ChatGPT while recognizing its current limitations and verifying critical information through independent research and expert opinions. As AI technology continues to evolve, ChatGPT may become a valuable tool in orthopedic education and patient care, leading to improved outcomes and efficiency in healthcare delivery. The integration of AI into orthopedics offers substantial benefits but requires careful consideration and continuous improvement.

Application of Artificial Intelligence in Shoulder Pathology

Artificial intelligence (AI) refers to the science and engineering of creating intelligent machines for imitating and expanding human intelligence. Given the ongoing evolution of the multidisciplinary integration trend in modern medicine, numerous studies have investigated the power of AI to address orthopedic-specific problems. One particular area of investigation focuses on shoulder pathology, which is a range of disorders or abnormalities of the shoulder joint, causing pain, inflammation, stiffness, weakness, and reduced range of motion. There has not yet been a comprehensive review of the recent advancements in this field. Therefore, the purpose of this review is to evaluate current AI applications in shoulder pathology. This review mainly summarizes several crucial stages of the clinical practice, including predictive models and prognosis, diagnosis, treatment, and physical therapy. In addition, the challenges and future development of AI technology are also discussed.

Bibliometric analysis of hotspots and trends of global myopia research

AIM

To gain insights into the global research hotspots and trends of myopia.

METHODS

Articles were downloaded from January 1, 2013 to December 31, 2022 from the Science Core Database website and were mainly statistically analyzed by bibliometrics software.

RESULTS

A total of 444 institutions in 87 countries published 4124 articles. Between 2013 and 2022, China had the highest number of publications (n=1865) and the highest H-index (61). Sun Yat-sen University had the highest number of publications (n=229) and the highest H-index (33). Ophthalmology is the main category in related journals. Citations from 2020 to 2022 highlight keywords of options and reference, child health (pediatrics), myopic traction mechanism, public health, and machine learning, which represent research frontiers.

CONCLUSION

Myopia has become a hot research field. China and Chinese institutions have the strongest academic influence in the field from 2013 to 2022. The main driver of myopic research is still medical or ophthalmologists. This study highlights the importance of public health in addressing the global rise in myopia, especially its impact on children's health. At present, a unified theoretical system is still needed. Accurate surgical and therapeutic solutions must be proposed for people with different characteristics to manage and intervene refractive errors. In addition, the benefits of artificial intelligence (AI) models are also reflected in disease monitoring and prediction.

Performance of ChatGPT in Diagnosis of Corneal Eye Diseases

PURPOSE

The aim of this study was to assess the capabilities of ChatGPT-4.0 and ChatGPT-3.5 for diagnosing corneal eye diseases based on case reports and compare with human experts.

METHODS

We randomly selected 20 cases of corneal diseases including corneal infections, dystrophies, and degenerations from a publicly accessible online database from the University of Iowa. We then input the text of each case description into ChatGPT-4.0 and ChatGPT-3.5 and asked for a provisional diagnosis. We finally evaluated the responses based on the correct diagnoses, compared them with the diagnoses made by 3 corneal specialists (human experts), and evaluated interobserver agreements.

RESULTS

The provisional diagnosis accuracy based on ChatGPT-4.0 was 85% (17 correct of 20 cases), whereas the accuracy of ChatGPT-3.5 was 60% (12 correct cases of 20). The accuracy of 3 corneal specialists compared with ChatGPT-4.0 and ChatGPT-3.5 was 100% (20 cases, P = 0.23, P = 0.0033), 90% (18 cases, P = 0.99, P = 0.6), and 90% (18 cases, P = 0.99, P = 0.6), respectively. The interobserver agreement between ChatGPT-4.0 and ChatGPT-3.5 was 65% (13 cases), whereas the interobserver agreement between ChatGPT-4.0 and 3 corneal specialists was 85% (17 cases), 80% (16 cases), and 75% (15 cases), respectively. However, the interobserver agreement between ChatGPT-3.5 and each of 3 corneal specialists was 60% (12 cases).

CONCLUSIONS

The accuracy of ChatGPT-4.0 in diagnosing patients with various corneal conditions was markedly improved than ChatGPT-3.5 and promising for potential clinical integration. A balanced approach that combines artificial intelligence-generated insights with clinical expertise holds a key role for unveiling its full potential in eye care.

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study

BACKGROUND

Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8.

METHODS

We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open.

RESULTS

Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time."

CONCLUSION

We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

An Artificial Intelligence-Supported Medicinal Chemistry Project: An Example for Incorporating Artificial Intelligence Within the Pharmacy Curriculum

OBJECTIVE

This study aims to integrate and use AI to teach core concepts in a medicinal chemistry course and to increase the familiarity of pharmacy students with AI in pharmacy practice and drug development. Artificial intelligence (AI) is a multidisciplinary science that aims to build software tools that mimic human intelligence. AI is revolutionizing pharmaceutical research and patient care. Hence, it is important to include AI in pharmacy education to prepare a competent workforce of pharmacists with skills in this area.

METHODS

AI principles were introduced in a required medicinal chemistry course for first-year pharmacy students. An AI software, KNIME, was used to examine structure-activity relationships for 5 drugs. Students completed a data sheet that required comprehension of molecular structures and drug-protein interactions. These data were then used to make predictions for molecules with novel substituents using AI. The familiarity of students with AI was surveyed before and after this activity.

RESULTS

There was an increase in the number of students indicating familiarity with use of AI in pharmacy (before vs after: 25.3% vs 74.5%). The introduction of AI stimulated interest in the course content (> 60% of students indicated increased interest in medicinal chemistry) without compromising the learning outcomes. Almost 70% of students agreed that more AI should be taught in the PharmD curriculum.

CONCLUSION

This is a successful and transferable example of integrating AI in pharmacy education without changing the main learning objectives of a course. This approach is likely to stimulate student interest in AI applications in pharmacy.

Establishment of a prognostic model for gastric cancer patients who underwent radical gastrectomy using machine learning: a two-center study

Objective

Gastric cancer is a prevalent gastrointestinal malignancy worldwide. In this study, a prognostic model was developed for gastric cancer patients who underwent radical gastrectomy using machine learning, employing advanced computational techniques to investigate postoperative mortality risk factors in such patients.

Methods

Data of 295 patients with gastric cancer who underwent radical gastrectomy at the Department of General Surgery of Affiliated Hospital of Xuzhou Medical University (Xuzhou, China) between March 2016 and November 2019 were retrospectively analyzed as the training group. Additionally, 109 patients who underwent radical gastrectomy at the Department of General Surgery Affiliated to Jining First People's Hospital (Jining, China) were included for external validation. Four machine learning models, including logistic regression (LR), decision tree (DT), random forest (RF), and gradient boosting machine (GBM), were utilized. Model performance was assessed by comparing the area under the curve (AUC) for each model. An LR-based nomogram model was constructed to assess patients' clinical prognosis.

Results

Lasso regression identified eight associated factors: age, sex, maximum tumor diameter, nerve or vascular invasion, TNM stage, gastrectomy type, lymphocyte count, and carcinoembryonic antigen (CEA) level. The performance of these models was evaluated using the AUC. In the training group, the AUC values were 0.795, 0.759, 0.873, and 0.853 for LR, DT, RF, and GBM, respectively. In the validation group, the AUC values were 0.734, 0.708, 0.746, and 0.707 for LR, DT, RF, and GBM, respectively. The nomogram model, constructed based on LR, demonstrated excellent clinical prognostic evaluation capabilities.

Conclusion

Machine learning algorithms are robust performance assessment tools for evaluating the prognosis of gastric cancer patients who have undergone radical gastrectomy. The LR-based nomogram model can aid clinicians in making more reliable clinical decisions.