History of artificial intelligence in medicine

The potential of artificial intelligence to transform medicine

PURPOSE OF REVIEW

Increased incorporation of artificial intelligence in medicine has raised questions regarding how it can enhance efficiency in concert with providing accurate medical information without violating patient privacy. Pediatricians should understand the impact of AI in terms of both their daily practice and the changing landscape of the medical field.

RECENT FINDINGS

Computer vision modeling and large language models have been designed for diagnostic and predictive health outcomes purposes; yet many still lack external validity and reliability. Artificial intelligence can also increase efficiency in electronic health record documentation. Despite potential benefits, legal and ethical concerns are raised with patient data that is stored and used by artificial intelligence models. More research is recommended before artificial intelligence is fully implemented into medical practice.

SUMMARY

Utilizing artificial intelligence in medical practice and medical education as supplemental tools, rather than in replacement of traditional methods, may result in more efficient medical practice and enhanced methods of studying. Yet, there needs to be a balance such that overreliance does not result in automatic trusting of potentially misinformation. Increased oversight and regulation of artificial intelligence in medicine is crucial to ensure legal and ethical approaches that protect patient privacy.

Patients' Perceptions of Artificial Intelligence Acceptance, Challenges, and Use in Medical Care: Qualitative Study

BACKGROUND

Artificial intelligence (AI) is increasingly used in medical care, particularly in the areas of image recognition and processing. While its practical use in other areas is still limited, an understanding of patients' needs is essential for the practical and sustainable implementation of AI, which could further acceptance of new innovations.

OBJECTIVE

The objective of this study was to explore patients' perceptions toward acceptance, challenges of implementation, and potential applications of AI in medical care.

METHODS

The study used a qualitative research design. To capture a broad range of patient perspectives, we conducted semistructured focus groups (FGs). As a stimulus for the FGs and as an introduction to the topic, we presented a video defining AI and showing 3 potential AI applications in health care. Participants were recruited from different locations in the regions of Halle (Saale) and Erlangen, Germany; all but one group were from outpatient settings. We analyzed the data using a content analysis approach.

RESULTS

A total of 35 patients (13 female and 22 male; age: range 23-92, median 50 years) participated in 6 focus groups. They highlighted that AI acceptance in medical care could be improved through user-friendly applications, clear instructions, feedback mechanisms, and a patient-centered approach. Perceived key barriers included data protection concerns, lack of human oversight, and profit-driven motives. Perceived challenges and requirements for AI implementation involved compatibility, training of end users, environmental sustainability, and adherence to quality standards. Potential AI application areas identified were diagnostics, image and data processing, and administrative tasks, though participants stressed that AI should remain a support tool, not an autonomous system. Psychology was an area where its use was opposed due to the need for human interaction.

CONCLUSIONS

Patients were generally open to the use of AI in medical care as a support tool rather than as an independent decision-making system. Acceptance and successful use of AI in medical care could be achieved if it is easy to use, adapted to individual characteristics of the users, and accessible to everyone, with the primary aim of enhancing patient well-being. AI in health care requires a regulatory framework, quality standards, and monitoring to ensure socially fair and environmentally sustainable development. However, the successful implementation of AI in medical practice depends on overcoming the mentioned challenges and addressing user needs.

Combining meta reinforcement learning with neural plasticity mechanisms for improved AI performance

This research explores the potential of combining Meta Reinforcement Learning (MRL) with Spike-Timing-Dependent Plasticity (STDP) to enhance the performance and adaptability of AI agents in Atari game settings. Our methodology leverages MRL to swiftly adjust agent strategies across a range of games, while STDP fine-tunes synaptic weights based on neuronal spike timings, which in turn improves learning efficiency and decision-making under changing conditions. A series of experiments were conducted with standard Atari games to compare the hybrid MRL-STDP model against baseline models using traditional reinforcement learning techniques like Q-learning and Deep Q-Networks. Various performance metrics, including learning speed, adaptability, and cross-game generalization, were evaluated. The results show that the MRL-STDP approach significantly accelerates the agent's ability to reach competitive performance levels, with a 40% boost in learning efficiency and a 35% increase in adaptability over conventional models.

Emerging Technology and the Future of Perioperative Care: Perspectives and Recommendations From the 2023 Stoelting Conference of the Anesthesia Patient Safety Foundation

Anesthesiology has a longstanding commitment to patient safety, characterized by innovative research, quality improvement, multidisciplinary collaboration, and engineering-based approaches to care systems. The field has been instrumental in advancing technological developments across the perioperative continuum, contributing to the ongoing mission of harm reduction and risk mitigation. However, modern challenges in health care, including increasingly complex patient conditions, workforce shortages, burnout, and the overwhelming volume of health data generated, have created a more urgent and multifaceted landscape for patient safety efforts. Furthermore, with the expanding perioperative continuum, from prehabilitation to postoperative acute care at home, anesthesiology teams must now adapt to a broader role in patient care. To continue enhancing patient safety, anesthesiology must integrate emerging technologies into clinical workflows, scaling their presence and effectiveness. The 2023 Anesthesia Patient Safety Foundation Stoelting Conference highlighted the necessity for anesthesiology to embrace these innovations while recognizing the challenges they pose. Three key technological domains were emphasized: wearables and the Internet of Medical Things; big data and artificial intelligence; and clinical decision support systems coupled with advanced alarm systems. These technologies offer opportunities to improve patient safety but require careful integration into clinical practice. This report explores the potential of these technologies to reshape anesthesiology and perioperative care while focusing on their application across 4 key phases: the preanesthesia phase at home; the intraoperative phase within health systems; postanesthesia recovery; and recovery at home. By leveraging these technologies, anesthesiology can enhance decision-making, improve outcomes, and continue advancing the mission of patient safety in a rapidly evolving health care landscape.

Artificial Intelligence in the Prevention and Detection of Cardiovascular Disease

For more than 60 years, artificial intelligence (AI) has served as a mainstay in augmenting and assisting the lives of individuals across a wide array of interests and professional fields. Functioning to create deep computer simulations, analyze data, solve problems, and synthesize human behavior/emotion, AI has recently become a topic of popular interest in many fields of medicine. Despite decades of usage, modern AI-and its newer branch of machine learning (ML)-have yet to find a fully established and regulated niche in medicine. Understanding the clinical implications that AI and ML might be able to play in cardiovascular medicine, studies have sought to understand and compare how this technology compares with human rationality and diagnostics. Utilizing AI and ML in an array of cardiovascular medical techniques, analyses, and predictive measurements seems to have produced accurate results while also saving healthcare providers time and enabling them to expand their reach to further populations. Although current research and literature might hypothesize AI's potential clinical applications, it is nearly impossible to fully understand the breadth and scope that this new technology can play in the future. In this article, we attempt to analyze a few of the potential applications of AI and ML for the detection, prevention, and treatment of cardiovascular disease. Additionally, we discuss how AI might make cardiovascular care more equitable and highlight a few precautions for utilizing this technology.

Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differentiating between benign and malignant lesions, which is crucial for treatment decisions. AI improves diagnostic accuracy through high sensitivity and specificity, while CNN algorithms enhance image analysis and reduce variability. These advancements aim to match the accuracy of pathologists in cancer detection. In addition, AI aids in identifying diagnostic markers, as early detection is essential. This article reviews the applications of machine learning and deep learning in the diagnosis of hepatic and pancreaticobiliary diseases. Although the use of AI in these specialized areas of gastroenterology is primarily confined to experimental trials, current models demonstrate significant potential for enhancing the detection, evaluation, and treatment planning of hepatic and pancreaticobiliary conditions.

RelAI: an automated approach to judge pointwise ML prediction reliability

OBJECTIVES

AI/ML advancements have been significant, yet their deployment in clinical practice faces logistical, regulatory, and trust-related challenges. To promote trust and informed use of ML predictions in real-world scenarios, reliable assessment of individual predictions is essential. We propose RelAI, a tool for pointwise reliability assessment of ML predictions that can support the identification of prediction errors during deployment.

MATERIALS AND METHODS

RelAI utilizes Autoencoders (AEs) to detect distributional shifts (Density principle) and a proxy model to encode local performance (Local Fit principle). We validated RelAI on a synthetic dataset and a real-world scenario involving Multiple Sclerosis (MS) patient outcomes.

RESULTS

On a synthetic dataset, RelAI effectively identified unreliable predictions, outperforming alternative approaches. In the MS case study, reliable predictions exhibited higher accuracy and were associated with specific demographic features, such as sex, residence, and eye symptoms.

DISCUSSION AND CONCLUSION

RelAI can support ML deployment in clinical settings by providing pointwise reliability assessments, ensuring regulatory compliance, and fostering user trust. Its model-agnostic nature and its compatibility with Python-based ML pipelines enhance its potential for widespread adoption.

Diagnostic accuracy of ChatGPT-4 in orthopedic oncology: a comparative study with residents

BACKGROUND

Artificial intelligence (AI) is increasingly being explored for its potential role in medical diagnostics. ChatGPT-4, a large language model (LLM) with image analysis capabilities, may assist in histopathological interpretation, but its accuracy in musculoskeletal oncology remains untested. This study evaluates ChatGPT-4's diagnostic accuracy in identifying musculoskeletal tumors from histology slides compared to orthopedic surgery residents.

METHODS

A comparative study was conducted using 24 histology slides randomly selected from an orthopedic oncology registry. Five teams of orthopedic surgery residents (PGY-1 to PGY-5) participated in a diagnostic competition, providing their best diagnosis for each slide. ChatGPT-4 was tested separately using identical histology images and clinical vignettes, with two independent attempts. Statistical analyses, including one-way ANOVA and independent t-tests were performed to compare diagnostic accuracy.

RESULTS

Orthopedic residents significantly outperformed ChatGPT-4 in diagnosing musculoskeletal tumors. The mean diagnostic accuracy among resident teams was 55%, while ChatGPT-4 achieved 25% on its first attempt and 33% on its second attempt. One-way ANOVA revealed a significant difference in accuracy across groups (F = 8.51, p = 0.033). Independent t-tests confirmed that residents performed significantly better than ChatGPT-4 (t = 5.80, p = 0.0004 for first attempt; t = 4.25, p = 0.0028 for second attempt). Both residents and ChatGPT-4 struggled with specific cases, particularly soft tissue sarcomas.

CONCLUSIONS

ChatGPT-4 demonstrated limited accuracy in interpreting histopathological slides compared to orthopedic residents. While AI holds promise for medical diagnostics, its current capabilities in musculoskeletal oncology remain insufficient for independent clinical use. These findings should be viewed as exploratory rather than confirmatory, and further research with larger, more diverse datasets is needed to assess AI's role in histopathology. Future studies should investigate AI-assisted workflows, refine prompt engineering, and explore AI models specifically trained for histopathological diagnosis.

Are we sleepwalking into a fully automated medical imaging service?

INTRODUCTION

Artificial intelligence (AI) is already embedded in medical imaging services, but now that the National Institute for Health and Care Excellence (NICE) has released position statements looking favourably on AI use in healthcare, its use will embed even further.

DISCUSSION

AI has brought many positives to medical imaging services and is far superior at making calculations using vast amounts of data. It can therefore help improve the speed and accuracy of diagnosis and treatment plans for many patients, but at what cost to the radiography profession? Surveys have shown that the majority of the workforce welcome AI, but admit that they don't fully understand the principles behind it. AI developers are keen to improve patient output, and many are unconcerned about the possible negative effects on staff morale and expertise. As computers remove the autonomy and competency that radiographers have previously held with pride, staff may find that they become de-skilled and de-motivated, and it may eventually subsume the traditional role of the radiographer altogether. The profession needs to be aware of these potential impacts and prepare accordingly.

CONCLUSION

Higher education plays an important role in preparing radiographers of the future for the changing landscape of medical imaging and should include more engineering and data science modules in the curriculum to prevent radiographers from becoming irrelevant.

Role of artificial intelligence in advancing immunology

Artificial intelligence (AI) has revolutionized various biomedical fields, particularly immunology, by enhancing vaccine development, immunotherapies, and allergy treatments. AI helps identify potential vaccine candidates and predict how the body reacts to different antigens based on a vast number of genomic sequences and protein structures. AI can help cancer patients by analyzing their data and offering personalized immunotherapies. AI has also advanced the field of allergy by identifying potential allergens and predicting allergic reactions based on patient genetic and environmental factors. AI could also help diagnose multiple immunological diseases, including autoimmune diseases and immunodeficiencies, by analyzing patient history and laboratory results. AI has deepened our understanding of the human genome by providing numerous amounts of data from DNA sequences previously believed to be nonfunctional. Through machine learning and deep learning, many laborious research tasks, such as screening for DNA mutations, can be efficiently performed in a short amount of time. AI and machine learning are significantly advancing biomedical science in significant areas, including research and industry. This review discusses the latest AI-based tools that can be utilized in the field of immunology. AI tools significantly advance the field of medical research and healthcare by enabling new scientific discoveries and facilitating rapid clinical diagnosis.

Artificial intelligence to predict cancer risk, are we there yet? A comprehensive review across cancer types

Cancer remains the second leading cause of death worldwide, representing a substantial challenge to global health. Although traditional risk prediction models have played a crucial role in epidemiology of several cancer types, they have limitations especially in the ability to process complex and multidimensional data. In contrast, artificial intelligence (AI) approaches represent a promising solution to overcome this limitation. AI techniques have the potential to identify complex patterns and relationships in data that traditional methods might overlook, making them especially useful for handling large and heterogeneous datasets analysed in cancer research. This review first examines the current state of the art of AI techniques, highlighting their differences and suitability for various data types. Then, offers a comprehensive analysis of the literature, focusing on the application of AI approaches in nineteen cancer types (bladder cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastric cancer, gynaecological cancers, head and neck cancer, haematological cancers, kidney cancer, liver cancer, lung cancer, melanoma, ovarian cancer, pancreatic cancer, prostate cancer, thyroid cancer and overall cancer), evaluating the models, metrics, and exposure variables used. Finally, the review discusses the application of AI in the clinical practice, along with an assessment of its potential limitations and future directions.

Accuracy of artificial intelligence in prediction of osteoporotic fractures in comparison with dual-energy X-ray absorptiometry and the Fracture Risk Assessment Tool: A systematic review

BACKGROUND

Osteoporotic fractures, whether due to postmenopausal or senile causes, impose a significant financial burden on developing countries and diminish quality of life. Recent advancements in artificial intelligence (AI) algorithms have demonstrated immense potential in predicting osteoporotic fractures.

AIM

To assess and compare the efficacy of AI models against dual-energy X-ray absorptiometry (DXA) and the Fracture Risk Assessment Tool (FRAX) in predicting fragility fractures.

METHODS

We conducted a literature search in English using electronic databases, including PubMed, Web of Science, and Scopus, for studies published until May 2024. The keywords employed were fragility fractures, osteoporosis, AI, deep learning, machine learning, and convolutional neural network. The inclusion criteria for selecting publications were based on studies involving patients with proximal femur and vertebral column fractures due to osteoporosis, utilizing AI algorithms, and analyzing the site of fracture and accuracy for predicting fracture risk using SPSS version 29 (Chicago, IL, United States).

RESULTS

We identified 156 publications for analysis. After applying our inclusion criteria, 24489 patients were analyzed from 13 studies. The mean area under the receiver operating characteristic curve was 0.925 ± 0.69. The mean sensitivity was 68.3% ± 15.3%, specificity was 85.5% ± 13.4%, and positive predictive value was 86.5% ± 6.3%. DXA showed a sensitivity of 37.0% and 74.0%, while FRAX demonstrated a sensitivity of 45.7% and 84.7%. The P value for sensitivity between DXA and AI was < 0.0001, while for FRAX it was < 0.0001 and 0.2.

CONCLUSION

This review found that AI is a valuable tool to analyze and identify patients who will suffer from fragility fractures before they occur, demonstrating superiority over DXA and FRAX. Further studies are necessary to be conducted across various centers with diverse population groups, larger datasets, and a longer duration of follow-up to enhance the predictive performance of the AI models before their universal application.

Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma

Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.

Global Research Trends, Hotspots, Impacts, and Emergence of Artificial Intelligence and Machine Learning in Health and Medicine: A 25-Year Bibliometric Analysis

Background/Objectives: The increasing application of artificial intelligence (AI) and machine learning (ML) in health and medicine has attracted a great deal of research interest in recent decades. This study aims to provide a global and historical picture of research concerning AI and ML in health and medicine. Methods: We used the Scopus database for searching and extracted articles published between 2000 and 2024. Then, we generated information about productivity, citations, collaboration, most impactful research topics, emerging research topics, and author keywords using Microsoft Excel 365 and VOSviewer software (version 1.6.20). Results: We retrieved a total of 22,113 research articles, with a notable surge in research activity in recent years. Core journals were Scientific Reports and IEEE Access, and core institutions included Harvard Medical School and the Ministry of Education of the People's Republic of China, while core countries comprised the United States, China, India, the United Kingdom, and Saudi Arabia. Citation trends indicated substantial growth and recognition of AI's and ML impact on health and medicine. Frequent author keywords identified key research hotspots, including specific diseases like Alzheimer's disease, Parkinson's diseases, COVID-19, and diabetes. The author keyword analysis identified "deep learning", "convolutional neural network", and "classification" as dominant research themes. Conclusions: AI's transformative potential in AI and ML in health and medicine holds promise for improving global health outcomes.

Comparison of an AI-driven planning tool and manual radiographic measurements in total knee arthroplasty

Background

Accurate preoperative planning in total knee arthroplasty (TKA) is essential. Traditional manual radiographic planning can be time-consuming and potentially prone to inaccuracies. This study investigates the performance of an AI-based radiographic planning tool in comparison with manual measurements in patients undergoing total knee arthroplasty, using a retrospective observational design to assess reliability and efficiency.

Methods

We retrospectively compared the Autoplan tool integrated within the mediCAD software (mediCAD Hectec GmbH, Altdorf, Germany), routinely implemented in our institutional workflow, to manual measurements performed by two orthopedic specialists on pre- and postoperative radiographs of 100 patients who underwent elective TKA. The following parameters were measured: leg length, mechanical axis deviation (MAD), mechanical lateral proximal femoral angle (mLPFA), anatomical mechanical angle (AMA), mechanical lateral distal femoral angle (mLDFA), joint line convergence angle (JLCA), mechanical medial proximal tibial angle (mMPTA), and mechanical tibiofemoral angle (mTFA).Intraclass correlation coefficients (ICCs) were calculated to assess measurement reliability, and the time required for each method was recorded.

Results

The Autoplan tool demonstrated high reliability (ICC > 0.90) compared with manual measurements for linear parameters (e.g., leg length and MAD). However, the angular measurements of mLPFA, JLCA, and AMA exhibited poor reliability (ICC < 0.50) among all raters. The Autoplan tool significantly reduced the time required for measurements compared to manual measurements, with a mean time saving of 44.3 seconds per case (95 % CI: 43.5-45.1 seconds, p < 0.001).

Conclusion

AI-assisted tools like the Autoplan tool in mediCAD offer substantial time savings and demonstrate reliable measurements for certain linear parameters in preoperative TKA planning. However, the observed low reliability in some measurements, even amongst experienced human raters, suggests inherent challenges in the radiographic assessment of angular parameters. Further development is needed to improve the accuracy of automated angular measurements, and to address the inherent variability in their assessment.

The Use of Artificial Intelligence for Skin Cancer Detection in Asia-A Systematic Review

Background: Artificial intelligence (AI) developed for skin cancer recognition has been shown to have comparable or superior performance to dermatologists. However, it is uncertain if current AI models trained predominantly with lighter Fitzpatrick skin types can be effectively adapted for Asian populations. Objectives: A systematic review was performed to summarize the existing use of artificial intelligence for skin cancer detection in Asian populations. Methods: Systematic search was conducted on PubMed and EMBASE for articles published regarding the use of artificial intelligence for skin cancer detection amongst Asian populations. Information regarding study characteristics, AI model characteristics, and outcomes was collected. Conclusions: Current studies show optimistic results in utilizing AI for skin cancer detection in Asia. However, the comparison of image recognition abilities might not be a true representation of the diagnostic abilities of AI versus dermatologists in the real-world setting. To ensure appropriate implementation, maximize the potential of AI, and improve the transferability of AI models across various Asian genotypes and skin cancers, it is crucial to focus on prospective, real-world-based practice, as well as the expansion and diversification of existing Asian databases used for training and validation.

AIAIAI: AI insights on amassing influence in AI-related publications - an AI-assisted retrospective analysis into AI-related publication

OBJECTIVES

This study analyses the trend of artificial intelligence (AI)-related publications in the medical field over the past decade and demonstrates the potential of AI in automating data analysis. We hypothesise exponential growth in AI-related publications, with continuous growth in the foreseeable future.

METHODS

Retrospective, AI-assisted analysis was conducted using the OpenAI application programming interface for data collection and evaluation. Publications from the top 50 medical journals (Web of Science, Journal Citation Report, 2022) covering 2014 to June 2024. A total of 315 209 papers were initially retrieved with 212 620 remaining after filtering. The outcomes were the total number and percentage of AI-related publications per year, with future trends prediction using statistical models.

RESULTS

AI-related publications increased from approximately 500 in 2014 to over 1000 in 2022, with the percentage rising from 2.5% to over 6% in 2024. The analysis identified cardiology and oncology as leading in AI adoption. Predictive models forecast that AI-related publications could reach 10% by 2030 with long-term projections suggesting potential dominance of AI presence by the mid-22nd century.

DISCUSSION

The study highlights the significant growth and integration of AI in medical research, with cardiology and oncology at the forefront. AI-assisted data analysis proves efficient and scalable but requires human oversight to maintain credibility.

CONCLUSIONS

The trajectory of AI-related publications indicates substantial growth and future integration across medical disciplines. Ongoing evaluation of AI's reliability and applicability in medical research remains essential.

Applications of Artificial Intelligence in Dental Medicine: A Critical Review

INTRODUCTION

Artificial intelligence (AI), including its subfields of machine learning and deep learning, is a branch of computer science and engineering focused on creating machines capable of tasks requiring human-like intelligence, such as visual perception, decision-making, and natural language processing. AI applications have become increasingly prevalent in dental medicine, generating high expectations as well as raising ethical and practical concerns.

METHODS

This critical review evaluates the current applications of AI in dentistry, identifying key perspectives, challenges, and limitations in ongoing AI research.

RESULTS

AI models have been applied across various dental specialties, supporting diagnosis, treatment planning, and decision-making, while also reducing the burden of repetitive tasks and optimizing clinical workflows. However, ethical complexities and methodological limitations, such as inconsistent data quality, bias risk, lack of transparency, and limited clinical validation, undermine the quality of AI studies and hinder the effective integration of AI into routine dental practice.

CONCLUSIONS

To improve AI research, studies must adhere to standardized methodological and ethical guidelines, particularly in data collection, while ensuring transparency, privacy, and accountability. Developing a comprehensive framework for producing robust, reproducible AI research and clinically validated technologies will facilitate the seamless integration of AI into clinical practice, benefiting both clinicians and patients by improving dental care.

Past, Present, and Future: A History Lesson in Artificial Intelligence

Over the past 5 decades, artificial intelligence (AI) has evolved rapidly. Moving from basic models to advanced machine learning and deep learning systems, the impact of AI on various fields, including medicine, has been profound. In gastroenterology, AI-driven computer-aided detection and computer-aided diagnosis systems have revolutionized endoscopy, imaging, and pathology detection. The future promises further advancements in diagnostic precision, personalized treatment, and clinical research. However, challenges such as transparency, liability, and ethical concerns must be addressed. By fostering collaboration, robust governance and development of quality metrics, AI can be leveraged to enhance patient care and advance scientific knowledge.

Artificial Intelligence in Medical Care - Patients' Perceptions on Caregiving Relationships and Ethics: A Qualitative Study

INTRODUCTION

Artificial intelligence (AI) offers several opportunities to enhance medical care, but practical application is limited. Consideration of patient needs is essential for the successful implementation of AI-based systems. Few studies have explored patients' perceptions, especially in Germany, resulting in insufficient exploration of perspectives of outpatients, older patients and patients with chronic diseases. We aimed to explore how patients perceive AI in medical care, focusing on relationships to physicians and ethical aspects.

METHODS

We conducted a qualitative study with six semi-structured focus groups from June 2022 to March 2023. We analysed data using a content analysis approach by systemising the textual material via a coding system. Participants were mostly recruited from outpatient settings in the regions of Halle and Erlangen, Germany. They were enrolled primarily through convenience sampling supplemented by purposive sampling.

RESULTS

Patients (N = 35; 13 females, 22 males) with a median age of 50 years participated. Participants were mixed in socioeconomic status and affinity for new technology. Most had chronic diseases. Perceived main advantages of AI were its efficient and flawless functioning, its ability to process and provide large data volume, and increased patient safety. Major perceived disadvantages were impersonality, potential data security issues, and fear of errors based on medical staff relying too much on AI. A dominant theme was that human interaction, personal conversation, and understanding of emotions cannot be replaced by AI. Participants emphasised the need to involve everyone in the informing process about AI. Most considered physicians as responsible for decisions resulting from AI applications. Transparency of data use and data protection were other important points.

CONCLUSIONS

Patients could generally imagine AI as support in medical care if its usage is focused on patient well-being and the human relationship is maintained. Including patients' needs in the development of AI and adequate communication about AI systems are essential for successful implementation in practice.

PATIENT OR PUBLIC CONTRIBUTION

Patients' perceptions as participants in this study were crucial. Further, patients assessed the presentation and comprehensibility of the research material during a pretest, and recommended adaptations were implemented. After each FG, space was provided for requesting modifications and discussion.

The Role of Artificial Intelligence for Advanced Endoscopy

Artificial intelligence (AI) application in gastroenterology has grown in the last decade and continues to evolve very rapidly. Early promising results have opened the door to explore its potential application to advanced endoscopy (AE). The aim of this review is to discuss the current state of the art and future directions of AI in AE. Current evidence suggests that AI-assisted endoscopic ultrasound models can be used in clinical practice to distinguish between benign and malignant pancreatic diseases with excellent results. AI-assisted endoscopic retrograde cholangiopancreatography models could also be useful in identifying the papilla, predicting difficult cannulation, and differentiating between benign and malignant strictures.

Applications, challenges and future directions of artificial intelligence in cardio-oncology

BACKGROUND

The management of cardiotoxicity related to cancer therapies has emerged as a significant clinical challenge, prompting the rapid growth of cardio-oncology. As cancer treatments become more complex, there is an increasing need to enhance diagnostic and therapeutic strategies for managing their cardiovascular side effects.

OBJECTIVE

This review investigates the potential of artificial intelligence (AI) to revolutionize cardio-oncology by integrating diverse data sources to address the challenges of cardiotoxicity management.

METHODS

We explore applications of AI in cardio-oncology, focusing on its ability to leverage multiple data sources, including electronic health records, electrocardiograms, imaging modalities, wearable sensors, and circulating serum biomarkers.

RESULTS

AI has demonstrated significant potential in improving risk stratification and longitudinal monitoring of cardiotoxicity. By optimizing the use of electrocardiograms, non-invasive imaging, and circulating biomarkers, AI facilitates earlier detection, better prediction of outcomes, and more personalized therapeutic interventions. These advancements are poised to enhance patient outcomes and streamline clinical decision-making.

CONCLUSIONS

AI represents a transformative opportunity in cardio-oncology by advancing diagnostic and therapeutic capabilities. However, successful implementation requires addressing practical challenges such as data integration, model interpretability, and clinician training. Continued collaboration between clinicians and AI developers will be essential to fully integrate AI into routine clinical workflows.

Predicting changes of incisor and facial profile following orthodontic treatment: a machine learning approach

BACKGROUND

Facial aesthetics is one of major motivations for seeking orthodontic treatment. However, even for experienced professionals, the impact and extent of incisor and soft tissue changes remain largely empirical. With the application of interdisciplinary approach, we aim to predict the changes of incisor and profile, while identifying significant predictors.

METHODS

A three-layer back-propagation artificial neural network model (BP-ANN) was constructed to predict incisor and profile changes of 346 patients, they were randomly divided into training, validation and testing cohort in the ratio of 7:1.5:1.5. The input data comprised of 28 predictors (model measurements, cephalometric analysis and other relevant information). Changes of U1-SN, LI-MP, Z angle and facial convex angle were set as continuous outcomes, mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R²) were used as evaluation index. Change trends of Z angle and facial convex angle were set as categorical outcomes, accuracy, precision, recall, and F1 score were used as evaluation index. Furthermore, we utilized SHapley Additive exPlanations (SHAP) method to identify significant predictors in each model.

RESULTS

MSE/MAE/R2 values for U1-SN were 0.0042/0.055/0.84, U1-SN, MP-SN and ANB were identified as the top three influential predictors. MSE/MAE/R2 values for L1-MP were 0.0062/0.063/0.84, L1-MP, ANB and extraction pattern were identified as the top three influential predictors. MSE/MAE/R2 values for Z angle were 0.0027/0.043/0.80, Z angle, MP-SN and LL to E-plane were considered as the top three influential indicators. MSE/MAE/R2 values for facial convex angle were 0.0042/0.050/0.73, LL to E-plane, UL to E-plane and Z angle were considered as the top three influential indicators. Accuracy/precision/recall/F1 Score of the change trend of Z angle were 0.89/1.0/0.80/0.89, Z angle, Lip incompetence and LL to E-plane made the largest contributions. Accuracy/precision/recall/F1 Score of the change trend of facial convex angel were 0.93/0.87/0.93/0.86, key contributors were LL to E-plane, UL to E-plane and Z angle.

CONCLUSION

BP-ANN could be a promising method for objectively predicting incisor and profile changes prior to orthodontic treatment. Such model combined with key influential predictors could provide valuable reference for decision-making process and personalized aesthetic predictions.

The integration of artificial intelligence in assisted reproduction: a comprehensive review

Artificial Intelligence (AI) has emerged as a transformative force in healthcare, with its integration into assisted reproduction technologies representing a notable milestone. The utilization of AI in assisted reproduction is rooted in the persistent challenge of optimizing outcomes. Despite years of progress, success rates in assisted reproductive techniques remain a concern. The current landscape of AI applications demonstrates significant potential to revolutionize various facets of assisted reproduction, including stimulation protocol optimization, embryo formation prediction, oocyte and sperm selection, and live birth prediction from embryos. AI's capacity for precise image-based analysis, leveraging convolutional neural networks, stands out as a promising avenue. Personalized treatment plans and enhanced diagnostic accuracy are central themes explored in this review. AI-driven healthcare products demonstrate the potential for real-time, adaptive health programs, fostering improved communication between patients and healthcare teams. Continuous learning systems to address challenges associated with biased training data and the time required for accurate decision-making capabilities to develop is imperative. Challenges and ethical considerations in AI-assisted conception as evident when taking into consideration issues such as the lack of legislation regulating AI in healthcare, a fact that emphasizes the need for transparency and equity in the development and implementation of AI technologies. The regulatory framework, both in the UK and globally, is making efforts to balance innovation with patient safety. This paper delves into the revolutionary impact of Artificial Intelligence (AI) in the realm of assisted reproduction technologies (ART). As AI continues to evolve, its application in the field of reproductive medicine holds great promise for improving success rates, personalized treatments, and overall efficiency. This comprehensive review explores the current state of AI in assisted reproduction, its potential benefits, challenges, and ethical considerations.

Evaluation of Awareness, Perception and Opinions Toward Artificial Intelligence Among Pharmacy Students

Background: Artificial intelligence (AI) helps to develop personalized medication therapy and regimens. It improves the patient care system. A cross-sectional study used and included pharmacy students, using validated survey questions. Objective: This study aimed to evaluate awareness, perception and opinion toward AI among pharmacy students. Design: This is a cross-sectional study (survey-based). Methods: A cross-sectional survey distribution among students in different levels of the college of pharmacy at National University (NU). The questions were classified to measure the variation of demographics, awareness, perceptions and opinions toward Artificial Intelligence (AI). Results: The results showed that more than 50% of pharmacy students are familiar with the uses of AI and know it's important in scientific research, 46.4% have a basic understanding of AI technologies. However more than 75% don't know the applications of AI used in pharmacy practice, 50.6 % don't know AI can support therapeutic diagnosis and 57 % don't know its importance in pharmacy education. A high perception was shown toward AI in facilitating pharmacy access to information (84.2%) and patients' access to the service (80.8%). In addition, 92% suggested that AI training is needed and 86.1 % recommended using AI in scientific research. The conclusion of this study identified the needs for awareness toward AI, and the important role of AI for education in pharmacy and health communities.

Leveraging Generative Artificial Intelligence to Improve Motivation and Retrieval in Higher Education Learners

Unlabelled

Generative artificial intelligence (GenAI) presents novel approaches to enhance motivation, curriculum structure and development, and learning and retrieval processes for both learners and instructors. Though a focus for this emerging technology is academic misconduct, we sought to leverage GenAI in curriculum structure to facilitate educational outcomes. For instructors, GenAI offers new opportunities in course design and management while reducing time requirements to evaluate outcomes and personalizing learner feedback. These include innovative instructional designs such as flipped classrooms and gamification, enriching teaching methodologies with focused and interactive approaches, and team-based exercise development among others. For learners, GenAI offers unprecedented self-directed learning opportunities, improved cognitive engagement, and effective retrieval practices, leading to enhanced autonomy, motivation, and knowledge retention. Though empowering, this evolving landscape has integration challenges and ethical considerations, including accuracy, technological evolution, loss of learner's voice, and socioeconomic disparities. Our experience demonstrates that the responsible application of GenAI's in educational settings will revolutionize learning practices, making education more accessible and tailored, producing positive motivational outcomes for both learners and instructors. Thus, we argue that leveraging GenAI in educational settings will improve outcomes with implications extending from primary through higher and continuing education paradigms.

Mini review on skin biopsy: traditional and modern techniques

The incidence of skin cancer continues to rise due to increased sun exposure and tanning habits, requiring early detection and treatment for favorable outcomes. Skin biopsy is an important diagnostic tool in dermatology and pathology, as it provides a valuable understanding of various skin diseases. Proper handling of skin biopsy specimens is vital to ensure accurate histopathological assessment. Still, the use of light microscopy and immunofluorescence provides a comprehensive approach to evaluating skin biopsy specimens, with each contributing unique information to aid in accurate diagnosis and management. This review highlights the evolution of skin biopsy practices, from traditional techniques to advanced methods incorporating artificial intelligence (AI) and convolutional neural networks. AI technologies enhance diagnostic accuracy and efficiency, aiding in the rapid analysis of skin lesions and biopsies. Despite challenges such as the need for extensively annotated datasets and ethical considerations, AI shows promise in dermatological diagnostics. The future of skin biopsy lies in minimally invasive techniques, liquid biopsies, and integrated pharmacogenomics for personalized medicine.

Evolution of AI enabled healthcare systems using textual data with a pretrained BERT deep learning model

In the rapidly evolving field of healthcare, Artificial Intelligence (AI) is increasingly driving the promotion of the transformation of traditional healthcare and improving medical diagnostic decisions. The overall goal is to uncover emerging trends and potential future paths of AI in healthcare by applying text mining to collect scientific papers and patent information. This study, using advanced text mining and multiple deep learning algorithms, utilized the Web of Science for scientific papers (1587) and the Derwent innovations index for patents (1314) from 2018 to 2022 to study future trends of emerging AI in healthcare. A novel self-supervised text mining approach, leveraging bidirectional encoder representations from transformers (BERT), is introduced to explore AI trends in healthcare. The findings point out the market trends of the Internet of Things, data security and image processing. This study not only reveals current research hotspots and technological trends in AI for healthcare but also proposes an advanced research method. Moreover, by analysing patent data, this study provides an empirical basis for exploring the commercialisation of AI technology, indicating the potential transformation directions for future healthcare services. Early technology trend analysis relied heavily on expert judgment. This study is the first to introduce a deep learning self-supervised model to the field of AI in healthcare, effectively improving the accuracy and efficiency of the analysis. These findings provide valuable guidance for researchers, policymakers and industry professionals, enabling more informed decisions.

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model

PURPOSE

Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individualized models through automatic machine learning (autoML) to predict the efficacy of immunotherapy in patients with inoperable advanced NSCLC.

METHODS

A total of 63 eligible participants were included and randomized into training and validation groups. Radiomics features were extracted from the volumes of interest of the tumor circled in the preprocessed computed tomography (CT) images. Golden feature, clinical, radiomics, and fusion models were generated using a combination of various algorithms through autoML. The models were evaluated using a multi-class receiver operating characteristic curve.

RESULTS

In total, 1,219 radiomics features were extracted from regions of interest. The ensemble algorithm demonstrated superior performance in model construction. In the training cohort, the fusion model exhibited the highest accuracy at 0.84, with an area under the curve (AUC) of 0.89-0.98. In the validation cohort, the radiomics model had the highest accuracy at 0.89, with an AUC of 0.98-1.00; its prediction performance in the partial response subgroup outperformed that in both the clinical and radiomics models. Patients with low rad scores achieved improved progression-free survival (PFS); (median PFS 16.2 vs. 13.4, P = 0.009).

CONCLUSION

autoML accurately and robustly predicted the short-term outcomes of patients with inoperable NSCLC treated with immune checkpoint inhibitor immunotherapy by constructing CT-based radiomics models, confirming it as a powerful tool to assist in the individualized management of patients with advanced NSCLC.

CLINICAL SIGNIFICANCE

This article highlights that autoML promotes the accuracy and efficiency of feature selection and model construction. The radiomics model generated by autoML predicted the efficacy of immunotherapy in patients with advanced NSCLC effectively. This may provide a rapid and non-invasive method for making personalized clinical decisions.

Nongenerative Artificial Intelligence in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning

The use of artificial intelligence (AI) within pathology and health care has advanced extensively. We have accordingly witnessed an increased adoption of various AI tools that are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within health care thus far fall mostly under the nongenerative AI domain, which includes supervised and unsupervised machine learning (ML) techniques. This review article explores how such nongenerative AI methods, rooted in traditional rules-based systems, enhance diagnostic accuracy, efficiency, and consistency within medicine. Key concepts and the application of supervised learning models (ie, classification and regression) such as decision trees, support vector machines, linear and logistic regression, K-nearest neighbor, and neural networks are explained along with the newer landscape of neural network-based nongenerative foundation models. Unsupervised learning techniques, including clustering, dimensionality reduction, and anomaly detection, are also discussed for their roles in uncovering novel disease subtypes or identifying outliers. Technical details related to the application of nongenerative AI algorithms for analyzing whole slide images are also highlighted. The performance, explainability, and reliability of nongenerative AI models essential for clinical decision-making is also reviewed, as well as challenges related to data quality, model interpretability, and risk of data drift. An understanding of which AI-ML models to employ and which shortcomings need to be addressed is imperative to safely and efficiently leverage, integrate, and monitor these traditional AI tools in clinical practice and research.

Current perspectives on the use of artificial intelligence in critical patient safety

Intensive Care Units (ICUs) have undergone enhancements in patient safety, and artificial intelligence (AI) emerges as a disruptive technology offering novel opportunities. While the published evidence is limited and presents methodological issues, certain areas show promise, such as decision support systems, detection of adverse events, and prescription error identification. The application of AI in safety may pursue predictive or diagnostic objectives. Implementing AI-based systems necessitates procedures to ensure secure assistance, addressing challenges including trust in such systems, biases, data quality, scalability, and ethical and confidentiality considerations. The development and application of AI demand thorough testing, encompassing retrospective data assessments, real-time validation with prospective cohorts, and efficacy demonstration in clinical trials. Algorithmic transparency and explainability are essential, with active involvement of clinical professionals being crucial in the implementation process.

Applications of artificial intelligence in current pharmacy practice: A scoping review

BACKGROUND

Artificial intelligence (AI), a branch of computer science, has been of growing research interest since its introduction to healthcare disciplines in the 1970s. Research has demonstrated that the application of such technologies has allowed for greater task accuracy and efficiency in medical disciplines such as diagnostics, treatment protocols and clinical decision-making. Application in pharmacy practice is reportedly narrower in scope; with greater emphasis placed on stock management and day-to-day function optimisation than enhancing patient outcomes. Despite this, new studies are underway to explore how AI technologies may be utilised in areas such as pharmacist interventions, medication adherence, and personalised medicine. Objective/s: The aim of this study was to identify current use of AI in measuring performance outcomes in pharmacy practice.

METHODS

A scoping review was conducted in accordance with PRISMA Extension for Scoping Reviews (PRISMA-ScR). A comprehensive literature search was conducted in MEDLINE, Embase, IPA (International Pharmaceutical Abstracts), and Web of Science databases for articles published between January 1, 2018 to September 11, 2023, relevant to the aim. The final search strategy included the following terms: ("artificial intelligence") AND ("pharmacy" OR "pharmacist" OR "pharmaceutical service" OR "pharmacy service"). Reference lists of identified review articles were also screened.

RESULTS

The literature search identified 560 studies, of which seven met the inclusion criteria. These studies described the use of AI in pharmacy practice. All seven studies utilised models derived from machine learning AI techniques. AI identification of prescriptions requiring pharmacist intervention was the most frequent (n = 4), followed by screening services (n = 2), and patient-facing mobile applications (n = 1). These results indicated a workflow- and productivity-focused application of AI within current pharmacy practice, with minimal intention for direct patient health outcome improvement. Despite this, the review also revealed AI's potential in data collation and analytics to aid in pharmacist contribution towards the healthcare team and improvement of health outcomes.

CONCLUSIONS

This scoping review has identified, from the literature available, three main areas of focus, (1) identification and classification of atypical or inappropriate medication orders, (2) improving efficiency of mass screening services, and (3) improving adherence and quality use of medicines. It also identified gaps in AI's current utility within the profession and its potential for day-to-day practice, as our understanding of general AI techniques continues to advance.

Agents for Change: Artificial Intelligent Workflows for Quantitative Clinical Pharmacology and Translational Sciences

Artificial intelligence (AI) is making a significant impact across various industries, including healthcare, where it is driving innovation and increasing efficiency. In the fields of Quantitative Clinical Pharmacology (QCP) and Translational Sciences (TS), AI offers the potential to transform traditional practices through the use of agentic workflows-systems with different levels of autonomy where specialized AI agents work together to perform complex tasks, while keeping "human in the loop." These workflows can simplify processes, such as data collection, analysis, modeling, and simulation, leading to greater efficiency and consistency. This review explores how these AI-powered agentic workflows can help in addressing some of the current challenges in QCP and TS by streamlining pharmacokinetic and pharmacodynamic analyses, optimizing clinical trial designs, and advancing precision medicine. By integrating domain-specific tools while maintaining data privacy and regulatory standards, well-designed agentic workflows empower scientists to automate routine tasks and make more informed decisions. Herein, we showcase practical examples of AI agents in existing platforms that support QCP and biomedical research and offer recommendations for overcoming potential challenges involved in implementing these innovative workflows. Looking ahead, fostering collaborative efforts, embracing open-source initiatives, and establishing robust regulatory frameworks will be key to unlocking the full potential of agentic workflows in advancing QCP and TS. These efforts hold the promise of speeding up research outcomes and improving the efficiency of drug development and patient care.

The Ever-Evolving Regulatory Landscape Concerning Development and Clinical Application of Machine Intelligence: Practical Consequences for Spine Artificial Intelligence Research

This paper analyzes the regulatory frameworks for artificial intelligence/machine learning AI/ML-enabled medical devices in the European Union (EU), the United States (US), and the Republic of Korea, with a focus on applications in spine surgery. The aim is to provide guidance for developers and researchers navigating regulatory pathways. A review of current literature, regulatory documents, and legislative frameworks was conducted. Key differences in regulatory bodies, risk classification, submission requirements, and approval pathways for AI/ML medical devices were examined in the EU, US, and Republic of Korea. The EU AI Act (2024) establishes a risk-based framework, requiring regulatory review based on device risk, with high-risk devices subject to stricter oversight. The US applies a more flexible approach, allowing multiple submission pathways and incorporating a focus on continuous learning. The Republic of Korea emphasizes possibilities of streamlined approval and with growing use of real-world data to support validation. Developers must ensure regulatory alignment early in the development process, focusing on key aspects like dataset quality, transparency, and continuous monitoring. Across all regions, the need for technical documentation, quality management systems, and bias mitigation are essential for approval. Developers are encouraged to adopt adaptable strategies to comply with evolving regulatory standards, ensuring models remain transparent, fair, and reliable. The EU's comprehensive AI Act enforces stricter oversight, while the US and Korea offer more flexible pathways. Developers of spine surgery AI/ML devices must tailor development strategies to align with regional regulations, emphasizing transparent development, quality assurance, and postmarket monitoring to ensure approval success.

Toward Safe and Ethical Implementation of Health Care Artificial Intelligence: Insights From an Academic Medical Center

Claims abound that advances in artificial intelligence (AI) will permeate virtually every aspect of medicine and transform clinical practice. Simultaneously, concerns about the safety and equity of health care AI have prompted ethical and regulatory scrutiny from multiple oversight bodies. Positioned at the intersection of these perspectives, academic medical centers (AMCs) are charged with navigating the safe and responsible implementation of health care AI. Decisions about the use of AI at AMCs are complicated by uncertainties regarding the risks posed by these technologies and a lack of consensus on best practices for managing these risks. In this article, we highlight several potential harms that may arise in the adoption of health care AI, with a focus on risks to patients, clinicians, and medical practice. In addition, we describe several strategies that AMCs might adopt now to address concerns about the safety and ethical uses of health care AI. Our analysis aims to support AMCs as they seek to balance AI innovation with proactive oversight.

Machine learning or traditional statistical methods for predictive modelling in perioperative medicine: A narrative review

Prediction of outcomes in perioperative medicine is key to decision-making and various prediction models have been created to help quantify and communicate those risks to both patients and clinicians. Increasingly, machine learning (ML) is being favoured over more traditional techniques to improve prediction of outcomes, however, the studies are of varying quality. It is also not known whether any increase in predictive performance using ML algorithms transpires into a clinically meaningful benefit. This coupled with the difficulty in interrogating ML algorithms is a potential cause of concern within the medical community. In this review, we provide a concise appraisal of studies which develop perioperative predictive ML models and compare predictive performance to traditional statistical models. The search strategy, title and abstract screening, and full-text reviews produced 37 studies for data extraction. Initially designed as a systematic review but due to the heterogeneity of the population and outcomes, was written in the narrative. Perioperative ML and traditional predictive models continue to be developed and published across a range of populations. This review highlights several studies which show that ML can enhance perioperative prediction models, although this is not universal, and performance for both methods remain context dependent. By focusing on relevant patient-centred outcomes, model interpretability, external validation, and maintaining high standards of reporting and methodological transparency, researchers can develop ML models alongside traditional methods to enhance clinical decision-making and improve patient care.

Machine Learning Approach to Investigating Macrophage Polarization on Various Titanium Surface Characteristics

Objective: Current laboratory studies on the effect of biomaterial properties on immune reactions are incomplete and based on a single or a few combination features of the biomaterial design. This study utilizes intelligent prediction models to explore the key features of titanium implant materials in macrophage polarization. Impact Statement: This pilot study provided some insights into the great potential of machine learning in exploring bone immunomodulatory biomaterials. Introduction: Titanium materials are commonly utilized as bone replacement materials to treat missing teeth and bone defects. The immune response caused by implant materials after implantation in the body has a double-edged sword effect on osseointegration. Macrophage polarization has been extensively explored to understand early material-mediated immunomodulation. However, understanding of implant material surface properties and immunoregulations remains limited due to current experimental settings, which are based on trial-by-trial approaches. Artificial intelligence, with its capacity to analyze large datasets, can help explore complex material-cell interactions. Methods: In this study, the effect of titanium surface properties on macrophage polarization was analyzed using intelligent prediction models, including random forest, extreme gradient boosting, and multilayer perceptron. Additionally, data extracted from the newly published literature were further input into the trained models to validate their performance. Results: The analysis identified "cell seeding density", "contact angle", and "roughness" as the most important features regulating interleukin 10 and tumor necrosis factor α secretion. Additionally, the predicted interleukin 10 levels closely matched the experimental results from newly published literature, while the tumor necrosis factor α predictions exhibited consistent trends. Conclusion: The polarization response of macrophages seeded on titanium materials is influenced by multiple factors, and artificial intelligence can assist in extracting the key features of implant materials for immunoregulation.

Predictive Factors of Length of Stay in Intensive Care Unit after Coronary Artery Bypass Graft Surgery based on Machine Learning Methods

Introduction

Coronary artery bypass grafting (CABG) surgery requires an extended length of stay (LOS) in the intensive care unit (ICU). This study aimed to predict the factors affecting LOS in the ICU after CABG surgery using machine learning methods.

Methods

In this study, after extracting factors affecting the LOS of patients in the ICU after CABG surgery from the literature and confirming these factors by experts, the medical records of 605 patients at Farshchian Specialized Heart Hospital were reviewed between April 20 and August 9, 2024. Four machine learning models were trained and tested to predict the most desired factors, and finally, the performance of the models was evaluated based on the relevant criteria.

Results

The most important predictors of the LOS of CABG patients in the ICU were the length of intubation, body mass index (BMI), age, duration of surgery, and the number of postoperative transfusions of packed cells. The Random Forest model also performed best in predicting the effective factors (Mean square Error = 1.64, Mean absolute error = 0.93, and R2 = 0.28).

Conclusion

The insights gained from the mashine learning model highlight the significance of demographic and clinical variables in predicting LOS in ICU. By understanding these predictors, healthcare professionals can better identify patients at higher risk for prolonged ICU stays.

Progressive role of artificial intelligence in treatment decision-making in the field of medical oncology

This article explores the role of artificial intelligence (AI) in medical oncology, emphasizing its impact on treatment decision-making for adult and pediatric cancer care. AI applications, including advanced imaging, drug discovery, and clinical decision support systems, enhance precision, personalization, and efficiency. Pediatric oncology benefits from improved diagnostics, risk stratification, and targeted therapies, despite unique challenges. AI-driven personalized medicine optimizes treatment strategies, improving patient outcomes and reducing costs. Ethical considerations, such as data privacy, algorithmic bias, and explainability, remain critical for responsible AI integration. Future advancements, including explainable AI and quantum computing, promise to redefine cancer care through data-driven insights.

Artificial Intelligence and Machine Learning in Preeclampsia

Preeclampsia is a multisystem hypertensive disorder that manifests itself after 20 weeks of pregnancy, along with proteinuria. The pathophysiology of preeclampsia is incompletely understood. Artificial intelligence, especially machine learning with its capability to identify patterns in complex data, has the potential to revolutionize preeclampsia research. These data-driven techniques can improve early diagnosis, personalize risk assessment, uncover the disease's molecular basis, optimize treatments, and enable remote monitoring. This brief review discusses the recent applications of artificial intelligence and machine learning in preeclampsia management and research, including the improvements these approaches have brought, along with their challenges and limitations.

Artificial intelligence and forensic mental health in Africa: a narrative review

This narrative review examines the integration of Artificial Intelligence (AI) tools into forensic psychiatry in Africa, highlighting possible opportunities and challenges. Specifically, AI may have the potential to augment screening in prisons, risk assessment/management, and forensic-psychiatric treatment, alongside offering benefits for training and research purposes. These use-cases may be particularly advantageous in contexts of forensic practice in Africa, where there remains a need for capacity building and service improvements in jurisdictions affected by distinctive sociolegal and socioeconomic challenges. However, AI can also entail ethical risks associated with misinformation, privacy concerns, and an overreliance on automated systems that need to be considered within implementation and policy planning. Equally, the political and regulatory backdrop surrounding AI in countries in Africa needs to be carefully scrutinised (and, where necessary, strengthened). Accordingly, this review calls for rigorous feasibility studies and the development of training programmes to ensure the effective application of AI in enhancing forensic-psychiatric services in Africa.

Evaluation of Advanced Artificial Intelligence Algorithms' Diagnostic Efficacy in Acute Ischemic Stroke: A Comparative Analysis of ChatGPT-4o and Claude 3.5 Sonnet Models

Background/Objectives: Acute ischemic stroke (AIS) is a leading cause of mortality and disability worldwide, with early and accurate diagnosis being critical for timely intervention and improved patient outcomes. This retrospective study aimed to assess the diagnostic performance of two advanced artificial intelligence (AI) models, Chat Generative Pre-trained Transformer (ChatGPT-4o) and Claude 3.5 Sonnet, in identifying AIS from diffusion-weighted imaging (DWI). Methods: The DWI images of a total of 110 cases (AIS group: n = 55, healthy controls: n = 55) were provided to the AI models via standardized prompts. The models' responses were compared to radiologists' gold-standard evaluations, and performance metrics such as sensitivity, specificity, and diagnostic accuracy were calculated. Results: Both models exhibited a high sensitivity for AIS detection (ChatGPT-4o: 100%, Claude 3.5 Sonnet: 94.5%). However, ChatGPT-4o demonstrated a significantly lower specificity (3.6%) compared to Claude 3.5 Sonnet (74.5%). The agreement with radiologists was poor for ChatGPT-4o (κ = 0.036; %95 CI: -0.013, 0.085) but good for Claude 3.5 Sonnet (κ = 0.691; %95 CI: 0.558, 0.824). In terms of the AIS hemispheric localization accuracy, Claude 3.5 Sonnet (67.2%) outperformed ChatGPT-4o (32.7%). Similarly, for specific AIS localization, Claude 3.5 Sonnet (30.9%) showed greater accuracy than ChatGPT-4o (7.3%), with these differences being statistically significant (p < 0.05). Conclusions: This study highlights the superior diagnostic performance of Claude 3.5 Sonnet compared to ChatGPT-4o in identifying AIS from DWI. Despite its advantages, both models demonstrated notable limitations in accuracy, emphasizing the need for further development before achieving full clinical applicability. These findings underline the potential of AI tools in radiological diagnostics while acknowledging their current limitations.

Japanese waka translation supported by internet of things and artificial intelligence technology

With the advancement of internet of things (IoT) and artificial intelligence (AI) technology, access to large-scale bilingual parallel data has become more efficient, thereby accelerating the development and application of machine translation. Given the increasing cultural exchanges between China and Japan, many scholars have begun to study the Chinese translation of Japanese waka poetry. Based on this, the study first explores the structure of waka and the current state of its Chinese translations, analyzing existing translation disputes and introducing a data collection method for waka using IoT. Then, an optimized neural machine translation model is proposed, which integrates a Bidirectional Long Short-Term Memory (Bi-LSTM) network, vertical Tree-LSTM, and an attention mechanism into the Transformer framework. Experimental results demonstrate that the three optimized models-Transformer + Bi-LSTM, Transformer + Tree-LSTM, and Transformer + Tree-LSTM + Attention-outperform the baseline Transformer model on both public and waka datasets. The BLEU scores of the models on the public dataset were 23.71, 23.95, and 24.12, respectively. Notably, on the waka dataset, the Transformer + Tree-LSTM + Attention model achieved the highest BLEU score of 20.65, demonstrating a significant advantage in capturing waka's unique features and contextual information. This study offers new methods to enhance the quality of Chinese-Japanese translation, promoting cultural exchange and understanding.

Total-Body PET/CT: A Role of Artificial Intelligence?

The purpose of this paper is to provide an overview of the cutting-edge applications of artificial intelligence (AI) technology in total-body positron emission tomography/computed tomography (PET/CT) scanning technology and its profound impact on the field of medical imaging. The introduction of total-body PET/CT scanners marked a major breakthrough in medical imaging, as their superior sensitivity and ultralong axial fields of view allowed for high-quality PET images of the entire body to be obtained in a single scan, greatly enhancing the efficiency and accuracy of diagnoses. However, this advancement is accompanied by the challenges of increasing data volumes and data complexity levels, which pose severe challenges for traditional image processing and analysis methods. Given the excellent ability of AI technology to process massive and high-dimensional data, the combination of AI technology and ultrasensitive PET/CT can be considered a complementary match, opening a new path for rapidly improving the efficiency of the PET-based medical diagnosis process. Recently, AI technology has demonstrated extraordinary potential in several key areas related to total-body PET/CT, including radiation dose reductions, dynamic parametric imaging refinements, quantitative analysis accuracy improvements, and significant image quality enhancements. The accelerated adoption of AI in clinical practice is of particular interest and is directly driven by the rapid progress made by AI technologies in terms of interpretability; i.e., the decision-making processes of algorithms and models have become more transparent and understandable. In the future, we believe that AI technology will fundamentally reshape the use of PET/CT, not only playing a more critical role in clinical diagnoses but also facilitating the customization and implementation of personalized healthcare solutions, providing patients with safer, more accurate, and more efficient healthcare experiences.

Overview of basic design recommendations for user-centered explanation interfaces for AI-based clinical decision support systems: A scoping review

Objective

The application of artificial intelligence (AI)-based clinical decision support systems (CDSS) in the healthcare domain is still limited. End-users' difficulty understanding how the outputs of opaque black AI models are generated contributes to this. It is still unknown which explanations are best presented to end users and how to design the interfaces they are presented in (explanation user interface, XUI). This article aims to provide an overview of recommendations for the user-centered design of XUIs from the scientific literature.

Methods

A scoping review was conducted to identify recommendations for the design of XUIs. Articles published between 2017 and 2022 in English or German, presenting original research or literature reviews, focusing on XUIs for end users or domain experts, which are intended for presentation in graphical user interfaces and from which recommendations could be extracted were included in the review. Articles were retrieved from Scopus, Web of Science, IEEE Explore, PubMed, ACM Digital Library, and PsychInfo. A mind map was created to organize and summarize the identified recommendations.

Results

From the 47 included articles, 240 recommendations for the user-centered design were extracted. The organization in a mind map resulted in 64 summarized recommendations.

Conclusion

This review provides a synopsis of basic recommendations for the user-centered design of XUIs, focusing on the healthcare domain. During the analysis of the articles, it became clear that no specific and directly implementable design recommendations for AI-based CDSS can be given, but only basic recommendations for raising awareness about the user-centered design of XUIs.

Controversies in Artificial Intelligence in Neurosurgery

Artificial intelligence (AI) has evolved from science fiction to a technology infiltrating everyday life. In neurosurgery, clinicians and researchers are exploring ways to implement this powerful tool to improve the safety and efficiency of the perioperative process. Current applications include preoperative diagnosis, intraoperative detection and recommendations, and technical skills assessment and feedback. Although the potential benefits are evident, AI integration into neurosurgical workflows requires discussions around ethical regulations, cybersecurity, privacy concerns, and data and algorithm ownership.

Reader's digest version of scientific writing: comparative evaluation of summarization capacity between large language models and medical students in analyzing scientific writing in sleep medicine

Introduction

As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably.

Methods

A collection of three AI-generated and human-generated summaries of sleep medicine research article abstracts were shared with 19 study participants (medical students) attending a sleep medicine conference. Participants were blind as to which summary was human or LLM generated. After reading both human and AI-generated research summaries participants completed a 1-5 Likert scale survey on the readability of the extracted writings. Participants also answered article-specific multiple-choice questions evaluating their comprehension of the summaries, as a representation of the quality of content retained by the AI-generated summaries.

Results

An independent sample t-test between the AI-generated and human-generated summaries comprehension by study participants revealed no significant difference between the Likert readability ratings (p = 0.702). A chi-squared test of proportions revealed no significant association (χ 2 = 1.485, p = 0.223), and a McNemar test revealed no significant association between summary type and the proportion of correct responses to the comprehension multiple choice questions (p = 0.289).

Discussion

Some limitations in this study were a small number of participants and user bias. Participants attended at a sleep conference and study summaries were all from sleep medicine journals. Lastly the summaries did not include graphs, numbers, and pictures, and thus were limited in material extraction. While the present analysis did not demonstrate a significant difference among the readability and content quality between the AI and human-generated summaries, limitations in the present study indicate that more research is needed to objectively measure, and further define strengths and weaknesses of AI models in condensing medical literature into efficient and accurate summaries.

Artificial Intelligence and ChatGPT in Medical Education: A Cross-Sectional Questionnaire on students' Competence

Artificial intelligence is rapidly transforming the field of health science and medical education, but less is known about the students´ competencies related to knowledge, skills and attitudes towards the application of AI tools like ChatGPT. Therefore, a unicentric questionnaire-based cross-sectional study was applied to students in the medical field (n = 207). The data revealed that while most students were familiar with ChatGPT (66.7%), other AI tools were significantly less known or utilised for study purposes. Students approached AI tools rather informally, often preferring to use them as a simple search engine. More than half of the students admitted that they were not sufficiently informed about the underlying technology of AI. They applied ChatGPT in a self-directed manner but expressed considerable uncertainty regarding effective prompt engineering and ChatGPT's legal implications. Overall, the majority of respondents showed interest in and positivity towards the introduction of AI. However, they did not feel adequately prepared to handle AI confidently, leading many to express interest in further training. This training should be directly related to students' professional roles, e.g. as a physician. The three most favoured AI-topics for voluntary learning formats were AI in their studies (62.5%), AI in general (58.0%), and the use of AI in scientific writing (57.0%). Notable subgroup differences related to the students" gender or self-assessed study performance were observed and should be considered in future research.