The impact of artificial intelligence on medical article writing: A boon or a bane?

As artificial intelligence continues its ascent across numerous sectors, it presents both exciting opportunities and unique challenges for the future of academic publishing. Artificial intelligence (AI) is the way of computer systems to perform tasks that has been done by human brain before, such as learning, problem-solving, and decision-making. In the realm of medical writing, AI is being harnessed through various applications. The increasing amalgamation of AI into medical writing has ignited a fervent debate, with experts and stakeholders divided on whether it represents a valuable tool for progress or a potential threat to the integrity and quality of scientific publications. While proponents celebrate AI's potential to streamline research, enhance efficiency, and broaden access to knowledge, critics voice concerns about ethical implications, the risk of plagiarism, and the potential for deskilling among researchers. Therefore, it is pivotal to acknowledge that AI has the potential to be both a boon and a bane, and its ethical and practical implications must be carefully considered to ensure its responsible and beneficial integration into the spectrum of medical writing.

AI as your ally: The effects of AI-assisted venting on negative affect and perceived social support

In recent years, artificial intelligence (AI) chatbots have made significant strides in generating human-like conversations. With AI's expanding capabilities in mimicking human interactions, its affordability and accessibility underscore the potential of AI chatbots to facilitate negative emotional disclosure or venting. The study's primary objective is to highlight the potential benefits of AI-assisted venting by comparing its effectiveness to venting through a traditional journaling platform in reducing negative affect and increasing perceived social support. We conducted a pre-registered within-subject experiment involving 150 participants who completed both traditional venting and AI-assisted venting conditions with counterbalancing and a wash-out period of 1-week between the conditions. Results from the frequentist and Bayesian dependent samples t-test revealed that AI-assisted venting effectively reduced high and medium arousal negative affect such as anger, frustration and fear. However, participants in the AI-assisted venting condition did not experience a significant increase in perceived social support and perceived loneliness, suggesting that participants did not perceive the effective assistance from AI as social support. This study demonstrates the promising role of AI in improving individuals' emotional well-being, serving as a catalyst for a broader discussion on the evolving role of AI and its potential psychological implications.

Frontiers in pancreatic cancer on biomarkers, microenvironment, and immunotherapy

Pancreatic cancer remains one of the most challenging malignancies to treat due to its late-stage diagnosis, aggressive progression, and high resistance to existing therapies. This review examines the latest advancements in early detection, and therapeutic strategies, with a focus on emerging biomarkers, tumor microenvironment (TME) modulation, and the integration of artificial intelligence (AI) in data analysis. We highlight promising biomarkers, including microRNAs (miRNAs) and circulating tumor DNA (ctDNA), that offer enhanced sensitivity and specificity for early-stage diagnosis when combined with multi-omics panels. A detailed analysis of the TME reveals how components such as cancer-associated fibroblasts (CAFs), immune cells, and the extracellular matrix (ECM) contribute to therapy resistance by creating immunosuppressive barriers. We also discuss therapeutic interventions that target these TME components, aiming to improve drug delivery and overcome immune evasion. Furthermore, AI-driven analyses are explored for their potential to interpret complex multi-omics data, enabling personalized treatment strategies and real-time monitoring of treatment response. We conclude by identifying key areas for future research, including the clinical validation of biomarkers, regulatory frameworks for AI applications, and equitable access to innovative therapies. This comprehensive approach underscores the need for integrated, personalized strategies to improve outcomes in pancreatic cancer.

Exploring Chatbot contributions to enhancing vaccine literacy and uptake: A scoping review of the literature

BACKGROUND

The increasing integration of chatbots across various sectors marks a significant shift in digital communication, and their role in healthcare makes no exception. This scoping review aims to systematically examine the role of chatbots in the perspective of organizational vaccine literacy, particularly in enhancing vaccine literacy and facilitating the dissemination of vaccine-related information, evaluating the potential of chatbots to transform vaccination communication strategies and improve health education outcomes.

METHODS

This scoping review adhered to the Joanna Briggs Institute methodology and the PRISMA-ScR checklist. A systematic search of MEDLINE, Embase, Scopus, and PsycInfo was conducted from January 2020 to October 30, 2024, using keywords related to "chatbots" and "vaccination." Study selection involved a two-stage screening process, focusing on studies reporting the use of chatbots to improve vaccine literacy and uptake. Data were thematically analyzed and presented in a narrative format.

RESULTS

Twenty-two studies were included in the review: these studies demonstrate the effectiveness of chatbots in enhancing vaccine literacy and acceptance, mainly focusing on COVID-19 but also addressing HPV and childhood vaccinations. They highlight chatbots' role in improving the vaccine-literate environment through countering misinformation and improving communication with healthcare professionals, showcasing their potential to significantly influence public health outcomes and their adaptability to diverse populations and geographic regions.

CONCLUSIONS

These digital assistants could provide personalized and up-to-date information, improving not only knowledge but also attitudes and intentions towards vaccinations.

Electroencephalography Searchlight Decoding Reveals Person- and Place-specific Responses for Semantic Category and Familiarity

Proper names are linguistic expressions referring to unique entities, such as individual people or places. This sets them apart from other words like common nouns, which refer to generic concepts. And yet, despite both being individual entities, one's closest friend and one's favorite city are intuitively associated with very different pieces of knowledge-face, voice, social relationship, autobiographical experiences for the former, and mostly visual and spatial information for the latter. Neuroimaging research has revealed the existence of both domain-general and domain-specific brain correlates of semantic processing of individual entities; however, it remains unclear how such commonalities and similarities operate over a fine-grained temporal scale. In this work, we tackle this question using EEG and multivariate (time-resolved and searchlight) decoding analyses. We look at when and where we can accurately decode the semantic category of a proper name and whether we can find person- or place-specific effects of familiarity, which is a modality-independent dimension and therefore avoids sensorimotor differences inherent among the two categories. Semantic category can be decoded in a time window and with spatial localization typically associated with lexical semantic processing. Regarding familiarity, our results reveal that it is easier to distinguish patterns of familiarity-related evoked activity for people, as opposed to places, in both early and late time windows. Second, we discover that within the early responses, both domain-general (left posterior-lateral) and domain-specific (right fronto-temporal, only for people) neural patterns can be individuated, suggesting the existence of person-specific processes.

Can Chat-GPT assist orthopedic surgeons in evaluating the quality of rotator cuff surgery patient information videos?

BACKGROUND

Patients and healthcare professionals extensively rely on the internet for medical information. Low-quality videos can significantly impact the patient-doctor relationship, potentially affecting consultation efficiency and decision-making process. Chat Generative Pre-Trained Transformer (ChatGPT) is an artificial intelligence application with the potential to improve medical reports, provide medical information, and supplement orthopedic knowledge acquisition. This study aimed to assess the ability of ChatGPT-4 to detect deficiencies in these videos, assuming it would be successful in identifying such deficiencies.

MATERIALS AND METHODS

YouTube was searched for "rotator cuff surgery" and "rotator cuff surgery clinic" videos. A total of 90 videos were evaluated, with 40 included in the study after exclusions. Using the Google Chrome extension ''YouTube Summary with ChatGPT & Claude,'' transcripts of these videos were accessed. Two senior orthopedic surgeons and ChatGPT-4 evaluated the videos using the rotator cuff surgery YouTube score (RCSS) system and DISCERN criteria.

RESULTS

ChatGPT-4's RCSS evaluations were comparable to those of the observers in 25% of instances, and 40% for DISCERN. The interobserver agreement between human observers and ChatGPT-4 was fair (AC1: 0.575 for DISCERN and AC1: 0.516 for RCSS). Even after correcting ChatGPT-4's incorrect answers, the agreement did not change significantly. ChatGPT-4 tended to give higher scores than the observers, particularly in sections related to anatomy, surgical technique, and indications for surgery.

CONCLUSION

The use of ChatGPT-4 as an observer in evaluating rotator cuff surgery-related videos and identifying deficiencies is not currently recommended. Future studies with trained ChatGPT models may address these deficiencies and enable ChatGPT to evaluate videos at a human observer level.

The role of artificial intelligence in enhancing healthcare for people with disabilities

The integration of artificial intelligence (AI) in healthcare delivery represents a transformative opportunity to enhance the lives of people living with disabilities. AI-driven technologies, such as assistive devices, conversational agents, and rehabilitation tools, can mitigate health disparities, improve diagnostic accuracy, and facilitate effective communication with healthcare providers, fostering more equitable healthcare environments. This commentary explores these applications while addressing the ethical challenges and limitations associated with AI deployment. Specific challenges, such as algorithmic bias, privacy risks with patient data, and the complexity of designing inclusive technologies, are discussed to provide a balanced perspective. For example, biased diagnostic tools may lead to inequitable care, and privacy breaches can compromise sensitive data. Key areas of focus include personalised care through AI-powered systems, the design of inclusive AI technologies incorporating continuous feedback loops and partnerships with advocacy groups, and the development of AI-enabled robotics for physical assistance. This commentary paper emphasises the importance of addressing these limitations alongside advancing ethical AI practices and ensuring continuous user involvement to meet the diverse needs of people living with disabilities, ultimately promoting greater independence and participation in society. Consequently, while AI holds transformative potential in advancing equitable and inclusive healthcare for people with disabilities, addressing ethical challenges, overcoming limitations, and fostering user-centred design are essential to fully realise its benefits and ensure these innovations promote autonomy, accessibility, and well-being.

Evaluation of the Appropriateness and Readability of ChatGPT-4 Responses to Patient Queries on Uveitis

Purpose

To compare the utility of ChatGPT-4 as an online uveitis patient education resource with existing patient education websites.

Design

Evaluation of technology.

Participants

Not applicable.

Methods

The term "uveitis" was entered into the Google search engine, and the first 8 nonsponsored websites were selected to be enrolled in the study. Information regarding uveitis for patients was extracted from Healthline, Mayo Clinic, WebMD, National Eye Institute, Ocular Uveitis and Immunology Foundation, American Academy of Ophthalmology, Cleveland Clinic, and National Health Service websites. ChatGPT-4 was then prompted to generate responses about uveitis in both standard and simplified formats. To generate the simplified response, the following request was added to the prompt: 'Please provide a response suitable for the average American adult, at a sixth-grade comprehension level.' Three dual fellowship-trained specialists, all masked to the sources, graded the appropriateness of the contents (extracted from the existing websites) and responses (generated responses by ChatGPT-4) in terms of personal preference, comprehensiveness, and accuracy. Additionally, 5 readability indices, including Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning Fog Index, Coleman-Liau Index, and Simple Measure of Gobbledygook index were calculated using an online calculator, Readable.com, to assess the ease of comprehension of each answer.

Main Outcome Measures

Personal preference, accuracy, comprehensiveness, and readability of contents and responses about uveitis.

Results

A total of 497 contents and responses, including 71 contents from existing websites, 213 standard responses, and 213 simplified responses from ChatGPT-4 were recorded and graded. Standard ChatGPT-4 responses were preferred and perceived to be more comprehensive by dually trained (uveitis and retina) specialist ophthalmologists while maintaining similar accuracy level compared with existing websites. Moreover, simplified ChatGPT-4 responses matched almost all existing websites in terms of personal preference, accuracy, and comprehensiveness. Notably, almost all readability indices suggested that standard ChatGPT-4 responses demand a higher educational level for comprehension, whereas simplified responses required lower level of education compared with the existing websites.

Conclusions

This study shows that ChatGPT can provide patients with an avenue to access comprehensive and accurate information about uveitis, tailored to their educational level.

Financial Disclosures

The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Label refinement network from synthetic error augmentation for medical image segmentation

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: (1) a model that generates synthetic structural errors, and (2) a label appearance simulation network that produces segmentations with synthetic errors that are similar in appearance to the real initial segmentations. Using these segmentations with synthetic errors and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net, four previous label refinement methods, and a U-Net trained with a loss tailored for tubular structures. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method.

Chat Generative Pre-Trained Transformer (ChatGPT) - 3.5 Responses Require Advanced Readability for the General Population and May Not Effectively Supplement Patient-Related Information Provided by the Treating Surgeon Regarding Common Questions About Rotator Cuff Repair

PURPOSE

To investigate the accuracy of Chat Generative Pre-Trained Transformer (ChatGPT)'s responses to frequently asked questions prior to rotator cuff repair surgery.

METHODS

The 10 most common frequently asked questions related to rotator cuff repair were compiled from 4 institution websites. Questions were then input into ChatGPT-3.5 in 1 session. The provided ChatGPT-3.5 responses were analyzed by 2 orthopaedic surgeons for reliability, quality, and readability using the Journal of the American Medical Association Benchmark criteria, the DISCERN score, and the Flesch-Kincaid Grade Level.

RESULTS

The Journal of the American Medical Association Benchmark criteria score was 0, indicating the absence of reliable source material citations. The mean Flesch-Kincaid Grade Level was 13.4 (range, 11.2-15.0). The mean DISCERN score was 43.4 (range, 36-51), indicating that the quality of the responses overall was considered fair. All responses cited making final decision-making to be made with the treating physician.

CONCLUSIONS

ChatGPT-3.5 provided substandard patient-related information in alignment with recommendations from the treating surgeon regarding common questions around rotator cuff repair surgery. Additionally, the responses lacked reliable source material citations, and the readability of the responses was relatively advanced with a complex language style.

CLINICAL RELEVANCE

The findings of this study suggest that ChatGPT-3.5 may not effectively supplement patient-related information in the context of recommendations provided by the treating surgeon prior to rotator cuff repair surgery.

Quantifying the Scope of Artificial Intelligence-Assisted Writing in Orthopaedic Medical Literature: An Analysis of Prevalence and Validation of AI-Detection Software

INTRODUCTION

The popularization of generative artificial intelligence (AI), including Chat Generative Pre-trained Transformer (ChatGPT), has raised concerns for the integrity of academic literature. This study asked the following questions: (1) Has the popularization of publicly available generative AI, such as ChatGPT, increased the prevalence of AI-generated orthopaedic literature? (2) Can AI detectors accurately identify ChatGPT-generated text? (3) Are there associations between article characteristics and the likelihood that it was AI generated?

METHODS

PubMed was searched across six major orthopaedic journals to identify articles received for publication after January 1, 2023. Two hundred and forty articles were randomly selected and entered into three popular AI detectors. Twenty articles published by each journal before the release of ChatGPT were randomly selected as negative control articles. 36 positive control articles (6 per journal) were created by altering 25%, 50%, and 100% of text from negative control articles using ChatGPT and were then used to validate each detector. The mean percentage of text detected as written by AI per detector was compared between pre-ChatGPT and post-ChatGPT release articles using independent t -test. Multivariate regression analysis was conducted using percentage AI-generated text per journal, article type (ie, cohort, clinical trial, review), and month of submission.

RESULTS

One AI detector consistently and accurately identified AI-generated text in positive control articles, whereas two others showed poor sensitivity and specificity. The most accurate detector showed a modest increase in the percentage AI detected for the articles received post release of ChatGPT (+1.8%, P = 0.01). Regression analysis showed no consistent associations between likelihood of AI-generated text per journal, article type, or month of submission.

CONCLUSIONS

As this study found an early, albeit modest, effect of generative AI on the orthopaedic literature, proper oversight will play a critical role in maintaining research integrity and accuracy. AI detectors may play a critical role in regulatory efforts, although they will require further development and standardization to the interpretation of their results.

Predicting the 3-Dimensional Dose Distribution of Multilesion Lung Stereotactic Ablative Radiation Therapy With Generative Adversarial Networks

PURPOSE

Because SABR therapy is being used to treat greater numbers of lung metastases, selecting the optimal dose and fractionation to balance local failure and treatment toxicity becomes increasingly challenging. Multilesion lung SABR therapy plans include spatially diverse lesions with heterogeneous prescriptions and interacting dose distributions. In this study, we developed and evaluated a generative adversarial network (GAN) to provide real-time dosimetry predictions for these complex cases.

METHODS AND MATERIALS

A GAN was trained to predict dosimetry on a data set of patients who received SABR therapy for lung lesions at a tertiary center. Model input included the planning computed tomography scan, the organs at risk (OARs) and target structures, and an initial estimate of exponential dose fall-off. Multilesion plans were split 80/20 for training and evaluation. Models were evaluated on voxel-voxel, clinical dose-volume histogram, and conformality metrics. An out-of-sample validation and analysis of model variance were performed.

RESULTS

There were 125 multilesion plans from 102 patients with 357 lesions. Patients were treated for 2 to 7 lesions, with 19 unique dose-fractionation schemes over 1 to 3 courses of treatment. The out-of-sample validation set contained an additional 90 plans from 80 patients. The mean absolute difference and gamma pass fraction between the predicted and true dosimetry was <3 Gy and >90% for all OARs. The absolute differences in lung V20 and CV14 were 1.40% ± 0.99% and 75.8 ± 42.0 cc, respectively. The ratios of predicted to true R50%, R100%, and D2cm were 1.00 ± 0.16, 0.96 ± 0.32, and 1.01 ± 0.36, respectively. The out-of-sample validation set maintained mean absolute difference and gamma pass fraction of <3 Gy and >90%, respectively for all OARs. The median standard deviation of variance in V20 and CV14 prediction was 0.49% and 22.2 cc, respectively.

CONCLUSIONS

A GAN for predicting the 3-D dosimetry of complex multilesion lung SABR therapy is presented. Rapid dosimetry prediction can be used to assess treatment feasibility and explore dosimetric differences between varying prescriptions.

Diffusion data augmentation for enhancing Norberg hip angle estimation

The Norberg angle (NA) plays a crucial role in evaluating hip joint conformation, particularly in canines, by quantifying femoral head subluxation within the hip joint. Therefore, it is an important metric for evaluating hip joint quality and diagnosing canine hip dysplasia, the most prevalent hereditary orthopedic disorder in dogs. While contemporary tools offer automated quantification of the NA, their usage typically entails manual labeling and verification of radiographic images by professional veterinarians. To enhance efficiency and streamline this process, the study aims to develop a tool capable of predicting the NA directly from the image without the need for veterinary intervention. Due to the challenges in acquiring annotated, diverse, high-quality images, this study introduces diffusion models to expand the training dataset from 219 to 1493 images, encompassing original images. This augmentation enhances the dataset's diversity and scale, thereby improving the accuracy of Norberg angle estimation. The model predicts four key points: the center of left and right femoral heads and the edge of the left and right acetabulum, as well as the radii of the femoral heads and the Norberg angles. By evaluating 18 distinct pretrained ImageNet models, we investigate their performance pre- and post-incorporating augmented data from generated images. The results demonstrate a significant enhancement, with an average 35.3% improvement based on mean absolute percentage error when utilizing generated images from diffusion models. This study showcases the potential of diffusion modeling in advancing canine hip dysplasia diagnosis and underscores the value of incorporating augmented data to elevate model accuracy.

Comparing the Management Recommendations of Large Language Model and Colorectal Cancer Multidisciplinary Team: A Pilot Study

BACKGROUND

Management of anorectal cancers requires a multidisciplinary team approach. Recently, large language models have been suggested as potential tools for various applications in health care.

OBJECTIVE

Assess suggested management recommendations provided by a generative artificial intelligence chatbot with those of a colorectal cancer multidisciplinary team to evaluate applicability in clinical settings.

DESIGN

Comparative pilot study where management recommendations from a generative artificial intelligence chatbot for patients with anal or colorectal cancers were compared against historical consensus decisions from multidisciplinary team meetings.

SETTING

Single referral tertiary center.

PATIENTS

Fifteen patients (mean age of 66.5 years; 53.5% woman) were included; 80% were primarily diagnosed with rectal cancer, predominantly stage II and III disease (46.6%). The mean tumor height from the anal verge was 4 cm.

INTERVENTIONS

From a generative artificial intelligence chatbot, we generated management recommendations for each patient, which were subsequently compared to historical decisions from a multidisciplinary team to gauge concordance.

MAIN OUTCOME MEASURES

Primary outcomes included a degree of concordance between generative artificial intelligence chatbot recommendations and the multidisciplinary team decisions, assessed on a scale from 1 (complete disagreement) to 5 (complete agreement), and justification was evaluated by 3 experienced colorectal surgeons.

RESULTS

A generative artificial intelligence chatbot achieved a high concordance rate with multidisciplinary team decisions, with an average concordance rating of 4.08. Multidisciplinary team treatment strategies included neoadjuvant therapy for 33.3% of patients, upfront surgery for 26.6%, and further diagnostic assessment for 20%. Interrater agreement on concordance was found to be moderate (κ coefficient range, 0.333-0.577), whereas agreement on decision justification was slight (κ coefficient range, 0.047-0.094).

LIMITATIONS

Retrospective study with small sample size.

CONCLUSIONS

The findings indicate a high level of concordance between generative artificial intelligence chatbot recommendations and the decisions from a colorectal cancer multidisciplinary team, suggesting the potential of large language models to support clinical decision-making in the management of anal and colorectal cancers. See Video Abstract.

COMPARACIN ENTRE RECOMENDACIONES DE MANEJO DEL MODELO EXTENSO DE LENGUAJE Y EL EQUIPO MULTIDISCIPLINARIO DE CNCER COLORRECTAL UN ESTUDIO PILOTO

ANTECEDENTES:El manejo de los cánceres anorrectales requiere un enfoque de equipo multidisciplinario. Recientemente, se han sugerido modelos extensos de lenguaje como herramientas potenciales para diversas aplicaciones en la asistencia sanitaria.OBJETIVO:Evaluar las recomendaciones de gestión sugeridos por un chatbot de inteligencia artificial generativa con las de un equipo multidisciplinario de cáncer colorrectal para evaluar la aplicabilidad en entornos clínicos.DISEÑO:Estudio piloto comparativo entre las recomendaciones de gestión de un chatbot de inteligencia artificial generativa con pacientes de cáncer anal o colorrectal y con las decisiones consensuadas históricas de reuniones de equipos multidisciplinarios.LUGAR:Un único centro terciario de referencia.PACIENTES:Se incluyeron 15 pacientes (edad media de 66,5 años; 53,5% mujeres); el 80% fueron diagnosticados principalmente de cáncer de recto, con predominio de la enfermedad en estadio II-III (46,6%). La altura media del tumor desde el borde anal fue de 4 cm.INTERVENCIONESUtilizando de un chatbot de inteligencia artificial generativa, producimos recomendaciones de manejo para cada paciente, que posteriormente se compararon con las decisiones del equipo multidisciplinario histórico para medir la concordancia.PRINCIPALES MEDIDAS DE RESULTADO:Los resultados primarios incluyeron el grado de concordancia entre las recomendaciones de un chatbot de inteligencia artificial generativa y las decisiones del equipo multidisciplinario, evaluadas en una escala de 1 (desacuerdo total) a 5 (acuerdo total), y la justificación evaluada por tres cirujanos colorrectales experimentados.RESULTADOS:Un chatbot de inteligencia artificial generativa logró una alta tasa de concordancia con las decisiones del equipo multidisciplinario, con una calificación media de concordancia de 4,08. Las estrategias de tratamiento del equipo multidisciplinario incluyeron terapia neoadyuvante para el 33,3% de los pacientes, cirugía inicial para el 26,6% y evaluación diagnóstica adicional para el 20%. La concordancia entre los evaluadores fue moderada (rango del coeficiente kappa: 0,333 a 0,577), mientras que la concordancia en la justificación de las decisiones fue leve (rango del coeficiente kappa: 0,047 a 0,094).LIMITACIONES:Estudio retrospectivo con pequeño tamaño muestral.CONCLUSIONES:Los hallazgos indican un alto nivel de concordancia entre las recomendaciones de un chatbot de inteligencia artificial generativa y las decisiones de un equipo multidisciplinario de cáncer colorrectal, lo que sugiere el potencial de los modelos extensos de lenguaje en apoyar la toma de decisiones clínicas en el manejo del cáncer anal y colorrectal. (Traducción: Dr. Fidel Ruiz Healy).

People's judgments of humans and robots in a classic moral dilemma

How do ordinary people evaluate robots that make morally significant decisions? Previous work has found both equal and different evaluations, and different ones in either direction. In 13 studies (N = 7670), we asked people to evaluate humans and robots that make decisions in norm conflicts (variants of the classic trolley dilemma). We examined several conditions that may influence whether moral evaluations of human and robot agents are the same or different: the type of moral judgment (norms vs. blame); the structure of the dilemma (side effect vs. means-end); salience of particular information (victim, outcome); culture (Japan vs. US); and encouraged empathy. Norms for humans and robots are broadly similar, but blame judgments show a robust asymmetry under one condition: Humans are blamed less than robots specifically for inaction decisions-here, refraining from sacrificing one person for the good of many. This asymmetry may emerge because people appreciate that the human faces an impossible decision and deserves mitigated blame for inaction; when evaluating a robot, such appreciation appears to be lacking. However, our evidence for this explanation is mixed. We discuss alternative explanations and offer methodological guidance for future work into people's moral judgment of robots and humans.

Topological analysis of the three-dimensional radiodensity distribution of fish otoliths: Point sampling effects on dimensionality reduction

Otoliths are calcified structures found in the inner ears of teleost fish, pivotal in marine biology for studies on metabolism, age, growth, and the identification of fish stocks, potentially leading to sustainable management practices. An important feature of this structure is its density, as it corresponds to modifications in the crystalline form of calcium carbonate during the fish's lifetime, resulting in variations in its final shape. The internal and external 3D radiodensity of otoliths from different species was obtained utilizing micro-computed tomography, however, an appropriate methodology for describing and conducting comparative studies on these data appears to be absent in the current body of literature. Therefore, we study otolith density variations from 3D computed tomography images, employing the Ball Mapper technique of Topological Data Analysis. We focus on reducing the computational cost of this analysis by applying probabilistic sampling and assessing its effects on the density variations provided by the Ball Mapper graph. To determine the sample size, we used the topology to establish what we term "Topological Sample Validation", which provided the minimum resolution with the same density information as raw data. Sample representativeness was validated through non-parametric statistical tests on the density variable. Based on the network's structural characteristics, network properties allowed for evaluating similarity between graphs. Besides the small sample size, remarkable correlations were obtained between age and network variables. Additionally, the Ball Mapper technique proved effective as a preprocessing algorithm for tomographic images, enabling the segmentation of undesired features in the object of interest.

Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation

Computational models are not just appealing because they can simulate and predict the development of biological phenomena across multiple spatial and temporal scales, but also because they can integrate information from well-established in vitro and in vivo models and test new hypotheses in cancer biomedicine. Agent-based models and simulations are especially interesting candidates among computational modeling procedures in cancer research due to the capability to, for instance, recapitulate the dynamics of neoplasia and tumor - host interactions. Yet, the absence of methods to validate the consistency of the results across scales can hinder adoption by turning fine-tuned models into black boxes. This review compiles relevant literature that explores strategies to leverage high-fidelity simulations of multi-scale, or multi-level, cancer models with a focus on verification approached as simulation calibration. We consolidate our review with an outline of modern approaches for agent-based models' validation and provide an ambitious outlook toward rigorous and reliable calibration.

Revolutionizing the food industry: The transformative power of artificial intelligence-a review

Artificial Intelligence (AI) is revolutionizing the food industry by optimizing processes, improving food quality and safety, and fostering innovation. This review examines AI's applications in food science, including supply chain management, production, sensory science, and personalized nutrition. It discusses techniques like knowledge-based expert systems, fuzzy logic, artificial neural networks, and machine learning, highlighting their roles in predictive maintenance, quality control, product development, and waste management. The integration of AI with sophisticated sensors enhances real-time monitoring and decision-making in food safety and packaging. However, challenges such as ethical concerns, data security, transparency, and high costs persist. AI is poised to advance sustainability by optimizing resource use, enhance food security through predictive analytics of crop yields, and drive innovation in personalized nutrition and supply chain automation, ensuring tailored products and efficient delivery. This paper underscores AI's transformative potential in the food industry while addressing the obstacles to its widespread adoption.

Unraveling the complexities of colorectal cancer and its promising therapies - An updated review

Colorectal cancer (CRC) continues to be a global health concern, necessitating further research into its complex biology and innovative treatment approaches. The etiology, pathogenesis, diagnosis, and treatment of colorectal cancer are summarized in this thorough review along with recent developments. The multifactorial nature of colorectal cancer is examined, including genetic predispositions, environmental factors, and lifestyle decisions. The focus is on deciphering the complex interactions between signaling pathways such as Wnt/β-catenin, MAPK, TGF-β as well as PI3K/AKT that participate in the onset, growth, and metastasis of CRC. There is a discussion of various diagnostic modalities that span from traditional colonoscopy to sophisticated molecular techniques like liquid biopsy and radiomics, emphasizing their functions in early identification, prognostication, and treatment stratification. The potential of artificial intelligence as well as machine learning algorithms in improving accuracy as well as efficiency in colorectal cancer diagnosis and management is also explored. Regarding therapy, the review provides a thorough overview of well-known treatments like radiation, chemotherapy, and surgery as well as delves into the newly-emerging areas of targeted therapies as well as immunotherapies. Immune checkpoint inhibitors as well as other molecularly targeted treatments, such as anti-epidermal growth factor receptor (anti-EGFR) as well as anti-vascular endothelial growth factor (anti-VEGF) monoclonal antibodies, show promise in improving the prognosis of colorectal cancer patients, in particular, those suffering from metastatic disease. This review focuses on giving readers a thorough understanding of colorectal cancer by considering its complexities, the present status of treatment, and potential future paths for therapeutic interventions. Through unraveling the intricate web of this disease, we can develop a more tailored and effective approach to treating CRC.

Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review

BACKGROUND

Artificial intelligence is experiencing rapid growth, with continual innovation and advancements in the health care field.

OBJECTIVE

This study aims to evaluate the application of artificial intelligence technologies across various domains of respiratory care.

METHODS

We conducted a narrative review to examine the latest advancements in the use of artificial intelligence in the field of respiratory care. The search was independently conducted by respiratory care experts, each focusing on their respective scope of practice and area of interest.

RESULTS

This review illuminates the diverse applications of artificial intelligence, highlighting its use in areas associated with respiratory care. Artificial intelligence is harnessed across various areas in this field, including pulmonary diagnostics, respiratory care research, critical care or mechanical ventilation, pulmonary rehabilitation, telehealth, public health or health promotion, sleep clinics, home care, smoking or vaping behavior, and neonates and pediatrics. With its multifaceted utility, artificial intelligence can enhance the field of respiratory care, potentially leading to superior health outcomes for individuals under this extensive umbrella.

CONCLUSIONS

As artificial intelligence advances, elevating academic standards in the respiratory care profession becomes imperative, allowing practitioners to contribute to research and understand artificial intelligence's impact on respiratory care. The permanent integration of artificial intelligence into respiratory care creates the need for respiratory therapists to positively influence its progression. By participating in artificial intelligence development, respiratory therapists can augment their clinical capabilities, knowledge, and patient outcomes.

AI technology to support adaptive functioning in neurodevelopmental conditions in everyday environments: a systematic review

Supports for adaptive functioning in individuals with neurodevelopmental conditions (NDCs) is of umost importance to long-term outcomes. Artificial intelligence (AI)-assistive technologies has enormous potential to offer efficient, cost-effective, and personalized solutions to address these challenges, particularly in everday environments. This systematic review examines the existing evidence for using AI-assistive technologies to support adaptive functioning in people with NDCs in everyday settings. Searches across six databases yielded 15 studies meeting inclusion criteria, focusing on robotics, phones/computers and virtual reality. Studies most frequently recruited children diagnosed with autism and targeted social skills (47%), daily living skills (26%), and communication (16%). Despite promising results, studies addressing broader transdiagnostic needs across different NDC populations are needed. There is also an urgent need to improve the quality of evidence-based research practices. This review concludes that AI holds enormous potential to support adaptive functioning for people with NDCs and for personalized health support. This review underscores the need for further research studies to advance AI technologies in this field.

Four functional genotoxic marker genes (Bax, Btg2, Ccng1, and Cdkn1a) discriminate genotoxic hepatocarcinogens from non-genotoxic hepatocarcinogens and non-genotoxic non-hepatocarcinogens in rat public toxicogenomics data, Open TG-GATEs

BACKGROUND

Previously, Japanese Environmental Mutagen and Genome Society/Mammalian Mutagenicity Study Group/Toxicogenomics Study Group (JEMS/MMS toxicogenomic study group) proposed 12 genotoxic marker genes (Aen, Bax, Btg2, Ccnf, Ccng1, Cdkn1a, Gdf15, Lrp1, Mbd1, Phlda3, Plk2, and Tubb4b) to discriminate genotoxic hepatocarcinogens (GTHCs) from non-genotoxic hepatocarcinogens (NGTHCs) and non-genotoxic non-hepatocarcinogens (NGTNHCs) in mouse and rat liver using qPCR and RNA-Seq and confirmed in public rat toxicogenomics data, Open TG-GATEs, by principal component analysis (PCA). On the other hand, the U.S. Environmental Protection Agency (US EPA) suggested seven genotoxic marker genes (Bax, Btg2, Ccng1, Cgrrf1, Cdkn1a, Mgmt, and Tmem47) with Open TG-GATEs data. Four genes (Bax, Btg2, Ccng1, and Cdkn1a) were common in these two studies. In the present study, we examined the performance of these four genes in Open TG-GATEs data using PCA.

RESULTS

The study's findings are of paramount significance, as these four genes proved to be highly effective in distinguishing five typical GTHCs (2-acetylaminofluorene, aflatoxin B1, 2-nitrofluorene, N-nitrosodiethylamine and N-nitrosomorpholine) from seven typical NGTHCs (clofibrate, ethanol, fenofibrate, gemfibrozil, hexachlorobenzene, phenobarbital, and WY-14643) and 11 NGTNHCs (allyl alcohol, aspirin, caffeine, chlorpheniramine, chlorpropamide, dexamethasone, diazepam, indomethacin, phenylbutazone, theophylline, and tolbutamide) by PCA at 24 h after a single administration with 100% accuracy. These four genes also effectively distinguished two typical GTHCs (2-acetylaminofluorene and N-nitrosodiethylamine) from seven NGTHCs and ten NGTNHCs by PCA on 29 days after 28 days-repeated administrations, with a similar or even better performance compared to the previous 12 genes. Furthermore, the study's analysis revealed that the three intermediate GTHC/NGTHCs (methapyrilene, monocrotaline, and thioacetamide, which were negative in the Salmonella test but positive in the in vivo rat liver test) were located in the intermediate region between typical GTHCs and typical NGTHCs by PCA.

CONCLUSIONS

The present results unequivocally demonstrate the availability of four genotoxic marker genes ((Bax, Btg2, Ccng1, and Cdkn1a) and PCA in discriminating GTHCs from NGTHCs and NGTNHCs in Open TG-GATEs. These findings strongly support our recommendation that future rat liver in vivo toxicogenomics tests prioritize these four genotoxic marker genes, as they have proven to be highly effective in discriminating between different types of hepatocarcinogens.

Challenges in data-driven geospatial modeling for environmental research and practice

Machine learning-based geospatial applications offer unique opportunities for environmental monitoring due to domains and scales adaptability and computational efficiency. However, the specificity of environmental data introduces biases in straightforward implementations. We identify a streamlined pipeline to enhance model accuracy, addressing issues like imbalanced data, spatial autocorrelation, prediction errors, and the nuances of model generalization and uncertainty estimation. We examine tools and techniques for overcoming these obstacles and provide insights into future geospatial AI developments. A big picture of the field is completed from advances in data processing in general, including the demands of industry-related solutions relevant to outcomes of applied sciences.

Dialogues with artificial intelligence: Exploring medical students' perspectives on ChatGPT

ChatGPT has initiated a new era of inquiry into sources of information within the scientific community. Studies leveraging ChatGPT in the medical field have demonstrated notable performance in academic processes and healthcare applications. This research presents how medical students have benefited from ChatGPT during their educational journey and the challenges they encountered, as reported through their personal experiences. The methodological framework of this study adheres to the stages of qualitative research. An explanatory case study, a qualitative research method, was adopted to determine user experiences with ChatGPT. Content analysis based on student experiences with ChatGPT indicates that it may offer advantages in health education as a resource for scientific research activities. However, adverse reports were also identified, including ethical issues, lack of personal data protection, and potential misuse in scientific research. This study emphasizes the need for comprehensive steps in effectively integrating AI tools like ChatGPT into medical education as a new technology.

Deep superpixel generation and clustering for weakly supervised segmentation of brain tumors in MR images

PURPOSE

Training machine learning models to segment tumors and other anomalies in medical images is an important step for developing diagnostic tools but generally requires manually annotated ground truth segmentations, which necessitates significant time and resources. We aim to develop a pipeline that can be trained using readily accessible binary image-level classification labels, to effectively segment regions of interest without requiring ground truth annotations.

METHODS

This work proposes the use of a deep superpixel generation model and a deep superpixel clustering model trained simultaneously to output weakly supervised brain tumor segmentations. The superpixel generation model's output is selected and clustered together by the superpixel clustering model. Additionally, we train a classifier using binary image-level labels (i.e., labels indicating whether an image contains a tumor), which is used to guide the training by localizing undersegmented seeds as a loss term. The proposed simultaneous use of superpixel generation and clustering models, and the guided localization approach allow for the output weakly supervised tumor segmentations to capture contextual information that is propagated to both models during training, resulting in superpixels that specifically contour the tumors. We evaluate the performance of the pipeline using Dice coefficient and 95% Hausdorff distance (HD95) and compare the performance to state-of-the-art baselines. These baselines include the state-of-the-art weakly supervised segmentation method using both seeds and superpixels (CAM-S), and the Segment Anything Model (SAM).

RESULTS

We used 2D slices of magnetic resonance brain scans from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset and labels indicating the presence of tumors to train and evaluate the pipeline. On an external test cohort from the BraTS 2023 dataset, our method achieved a mean Dice coefficient of 0.745 and a mean HD95 of 20.8, outperforming all baselines, including CAM-S and SAM, which resulted in mean Dice coefficients of 0.646 and 0.641, and mean HD95 of 21.2 and 27.3, respectively.

CONCLUSION

The proposed combination of deep superpixel generation, deep superpixel clustering, and the incorporation of undersegmented seeds as a loss term improves weakly supervised segmentation.

Uncertainty-aware automatic TNM staging classification for [18F] Fluorodeoxyglucose PET-CT reports for lung cancer utilising transformer-based language models and multi-task learning

BACKGROUND

[18F] Fluorodeoxyglucose (FDG) PET-CT is a clinical imaging modality widely used in diagnosing and staging lung cancer. The clinical findings of PET-CT studies are contained within free text reports, which can currently only be categorised by experts manually reading them. Pre-trained transformer-based language models (PLMs) have shown success in extracting complex linguistic features from text. Accordingly, we developed a multi-task 'TNMu' classifier to classify the presence/absence of tumour, node, metastasis ('TNM') findings (as defined by The Eight Edition of TNM Staging for Lung Cancer). This is combined with an uncertainty classification task ('u') to account for studies with ambiguous TNM status.

METHODS

2498 reports were annotated by a nuclear medicine physician and split into train, validation, and test datasets. For additional evaluation an external dataset (n = 461 reports) was created, and annotated by two nuclear medicine physicians with agreement reached on all examples. We trained and evaluated eleven publicly available PLMs to determine which is most effective for PET-CT reports, and compared multi-task, single task and traditional machine learning approaches.

RESULTS

We find that a multi-task approach with GatorTron as PLM achieves the best performance, with an overall accuracy (all four tasks correct) of 84% and a Hamming loss of 0.05 on the internal test dataset, and 79% and 0.07 on the external test dataset. Performance on the individual TNM tasks approached expert performance with macro average F1 scores of 0.91, 0.95 and 0.90 respectively on external data. For uncertainty an F1 of 0.77 is achieved.

CONCLUSIONS

Our 'TNMu' classifier successfully extracts TNM staging information from internal and external PET-CT reports. We concluded that multi-task approaches result in the best performance, and better computational efficiency over single task PLM approaches. We believe these models can improve PET-CT services by assisting in auditing, creating research cohorts, and developing decision support systems. Our approach to handling uncertainty represents a novel first step but has room for further refinement.

The Algorithmic Divide: A Systematic Review on AI-Driven Racial Disparities in Healthcare

INTRODUCTION

As artificial intelligence (AI) continues to permeate various sectors, concerns about disparities arising from its deployment have surfaced. AI's effectiveness correlates not only with the algorithm's quality but also with its training data's integrity. This systematic review investigates the racial disparities perpetuated by AI systems across diverse medical domains and the implications of deploying them, particularly in healthcare.

METHODS

Six electronic databases (PubMed, Scopus, IEEE, Google Scholar, EMBASE, and Cochrane) were systematically searched on October 3, 2023. Inclusion criteria were peer-reviewed articles in English from 2013 to 2023 that examined instances of racial bias perpetuated by AI in healthcare. Studies conducted outside of healthcare settings or that addressed biases other than racial, as well as letters, opinions were excluded. The risk of bias was identified using CASP criteria for reviews and the Modified Newcastle Scale for observational studies.

RESULTS

Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 1272 articles were initially identified, from which 26 met eligibility criteria. Four articles were identified via snowballing, resulting in 30 articles in the analysis. Studies indicate a significant association between AI utilization and the exacerbation of racial disparities, especially in minority populations, including Blacks and Hispanics. Biased data, algorithm design, unfair deployment of algorithms, and historic/systemic inequities were identified as the causes. Study limitations stem from heterogeneity impeding broad comparisons and the preclusion of meta-analysis.

CONCLUSION

To address racial disparities in healthcare outcomes, enhanced ethical considerations and regulatory frameworks are needed in AI healthcare applications. Comprehensive bias detection tools and mitigation strategies, coupled with active supervision by physicians, are essential to ensure AI becomes a tool for reducing racial disparities in healthcare outcomes.

Generating 3D brain tumor regions in MRI using vector-quantization Generative Adversarial Networks

Medical image analysis has significantly benefited from advancements in deep learning, particularly in the application of Generative Adversarial Networks (GANs) for generating realistic and diverse images that can augment training datasets. The common GAN-based approach is to generate entire image volumes, rather than the region of interest (ROI). Research on deep learning-based brain tumor classification using MRI has shown that it is easier to classify the tumor ROIs compared to the entire image volumes. In this work, we present a novel framework that uses vector-quantization GAN and a transformer incorporating masked token modeling to generate high-resolution and diverse 3D brain tumor ROIs that can be used as additional data for tumor ROI classification. We apply our method to two imbalanced datasets where we augment the minority class: (1) low-grade glioma (LGG) ROIs from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019 dataset; (2) BRAF V600E Mutation genetic marker tumor ROIs from the internal pediatric LGG (pLGG) dataset. We show that the proposed method outperforms various baseline models qualitatively and quantitatively. The generated data was used to balance the data to classify brain tumor types. Our approach demonstrates superior performance, surpassing baseline models by 6.4% in the area under the ROC curve (AUC) on the BraTS 2019 dataset and 4.3% in the AUC on the internal pLGG dataset. The results indicate the generated tumor ROIs can effectively address the imbalanced data problem. Our proposed method has the potential to facilitate an accurate diagnosis of rare brain tumors using MRI scans.

Demographic factors, knowledge, attitude and perception and their association with nursing students' intention to use artificial intelligence (AI): a multicentre survey across 10 Arab countries

BACKGROUND

Artificial intelligence (AI) is becoming increasingly important in healthcare, with a significant impact on nursing practice. As future healthcare practitioners, nursing students must be prepared to incorporate AI technologies into their job. This study aimed to explore the associated factors with nursing students' intention to use AI.

METHODS

Descriptive cross-sectional multi-centre design was used. A convenience sample of 1713 university nursing students from Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Oman, Palestine, Saudi Arabia and the United Arab Emirates completed a self-reported online instrument divided into five sections covering: (1) demographic sheet, (2) knowledge, (3) attitude, (4) perception and (5) intention questionnaire.

RESULTS

Most nursing students in Arab countries have moderate levels of knowledge, attitude, perception and intention towards the use of AI. There was a significant positive association between knowledge, attitude, perception and intention towards the use of AI. A multivariate regression analysis revealed that understanding of AI technologies, self-perception as tech-savvy, age, clinical performance in previous semesters and knowledge of AI were significant and positively correlated with intention.

CONCLUSION

The findings highlight the importance of targeted educational interventions and customised strategies to support AI integration within nursing education settings across Arab countries, equipping future nurses with the necessary skills and knowledge to use AI effectively in their practice.

Predicting maternal risk level using machine learning models

BACKGROUND

Maternal morbidity and mortality remain critical health concerns globally. As a result, reducing the maternal mortality ratio (MMR) is part of goal 3 in the global sustainable development goals (SDGs), and previously, it was an important indicator in the Millennium Development Goals (MDGs). Therefore, identifying high-risk groups during pregnancy is crucial for decision-makers and medical practitioners to mitigate mortality and morbidity. However, the availability of accurate predictive models for maternal mortality and maternal health risks is challenging. Compared with traditional predictive models, machine learning algorithms have emerged as promising predictive modelling methods providing accurate predictive models.

METHODS

This work aims to explore the potential of machine learning (ML) algorithms in maternal risk level prediction using a nationwide maternal mortality dataset from Oman for the first time. A total of 402 maternal deaths from 1991 to 2023 in Oman were included in this study. We utilised principal component analysis (PCA) in the ML algorithms and compared them to the results of model performance without PCA. We employed and compared ten ML algorithms, including decision tree (DT), random forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Extreme Gradient Boosting (xgboost), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Logistic Regression (LR), Support Vector Machine (SVM) and Artificial Neural Network (ANN). Different metrics, including, accuracy, sensitivity, precision, and the F1- score, were utilised to assess Model performance.

RESULTS

The results indicated that the RF model outperformed the other methods in predicting the risk level (low or high) with an accuracy of 75.2%, precision of 85.7% and F1- score of 73% after PCA was applied.

CONCLUSIONS

We applied several machine learning models to predict maternal risk levels for the first time using real data from Oman. RF outperformed the other algorithms in this classification problem. A reliable estimate of maternal risk level would facilitate intervention plans for medical practitioners to reduce maternal death.

Digital Transformation in the Health Product Supply Chain: A Framework for Analysis

Well-functioning supply chains for medicines and other health products are vital for a health system's goals of ensuring access, quality, and efficiency. However, in several countries the performance of government-run supply chains for health products remains subpar. The widespread adoption of digital technology presents new opportunities for enhancing the performance of the health product supply chain. This paper aims to provide a practical and systematic analysis of digital initiatives within health product supply chains. It provides examples of successful digital interventions in each of the Enable, Plan, Source, and Deliver categories of the Supply Chain Operations Reference model. The examples provide clear evidence that the use of digital technology in the health supply chain can improve access and affordability; in some instances, use of digital technology can lead to faster health product adoption and alter the overall architecture of decision making. While many digital interventions in the public sector supply chain target the collection of data and its analysis and use for reporting, the long-term effectiveness of digital solutions hinges on their ability to enhance the agency of supply chain actors. A thorough and systematic inquiry about the logic model of how a particular digital solution enhances agency and improves accountability is essential at the outset. In developing roadmaps to prioritize and sequence digital solutions in health supply chains, governments should start by asking where lack of information is the primary constraint impeding supply chain performance.

AI models in clinical neonatology: a review of modeling approaches and a consensus proposal for standardized reporting of model performance

Artificial intelligence (AI) is a rapidly advancing area with growing clinical applications in healthcare. The neonatal intensive care unit (NICU) produces large amounts of multidimensional data allowing AI and machine learning (ML) new avenues to improve early diagnosis, enhance monitoring, and provide highly-targeted treatment approaches. In this article, we review recent clinical applications of AI to important neonatal problems, including sepsis, retinopathy of prematurity, bronchopulmonary dysplasia, and others. For each clinical area, we highlight a variety of ML models published in the literature and examine the future role they may play at the bedside. While the development of these models is rapidly expanding, a fundamental understanding of model selection, development, and performance evaluation is crucial for researchers and healthcare providers alike. As AI plays an increasing role in daily practice, understanding the implications of AI design and performance will enable more effective implementation. We provide a comprehensive explanation of the AI development process and recommendations for a standardized performance metric framework. Additionally, we address critical challenges, including model generalizability, ethical considerations, and the need for rigorous performance monitoring to avoid model drift. Finally, we outline future directions, emphasizing the importance of collaborative efforts and equitable access to AI innovations.

AI-Driven Drug Discovery for Rare Diseases

Rare diseases (RDs), affecting 300 million people globally, present a daunting public health challenge characterized by complexity, limited treatment options, and diagnostic hurdles. Despite legislative efforts, such as the 1983 US Orphan Drug Act, more than 90% of RDs lack effective therapies. Traditional drug discovery models, marked by lengthy development cycles and high failure rates, struggle to meet the unique demands of RDs, often yielding poor returns on investment. However, the advent of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), offers groundbreaking solutions. This review explores AI's potential to revolutionize drug discovery for RDs by overcoming these challenges. It discusses AI-driven advancements, such as drug repurposing, biomarker discovery, personalized medicine, genetics, clinical trial optimization, corporate innovations, and novel drug target identification. By synthesizing current knowledge and recent breakthroughs, this review provides crucial insights into how AI can accelerate therapeutic development for RDs, ultimately improving patient outcomes. This comprehensive analysis fills a critical gap in the literature, enhancing understanding of AI's pivotal role in transforming RD research and guiding future research and development efforts in this vital area of medicine.

How Italian radiation oncologists use ChatGPT: a survey by the young group of the Italian association of radiotherapy and clinical oncology (yAIRO)

PURPOSE

To investigate the awareness and the spread of ChatGPT and its possible role in both scientific research and clinical practice among the young radiation oncologists (RO).

MATERIAL AND METHODS

An anonymous, online survey via Google Forms (including 24 questions) was distributed among young (< 40 years old) ROs in Italy through the yAIRO network, from March 15, 2024, to 31, 2024. These ROs were officially registered with yAIRO in 2023. We particularly focused on the emerging use of ChatGPT and its future perspectives in clinical practice.

RESULTS

A total of 76 young physicians answered the survey. Seventy-three participants declared to be familiar with ChatGPT, and 71.1% of the surveyed physicians have already used ChatGPT. Thirty-one (40.8%) participants strongly agreed that AI has the potential to change the medical landscape in the future. Additionally, 79.1% of respondents agreed that AI will be mainly successful in research processes such as literature review and drafting articles/protocols. The belief in ChatGPT's potential results in direct use in daily practice in 43.4% of the cases, with mainly a fair grade of satisfaction (43.2%). A large part of participants (69.7%) believes in the implementation of ChatGPT into clinical practice, even though 53.9% fear an overall negative impact.

CONCLUSIONS

The results of the present survey clearly highlight the attitude of young Italian ROs toward the implementation of ChatGPT into clinical and academic RO practice. ChatGPT is considered a valuable and effective tool that can ease current and future workflows.

Using machine learning to improve the hard modeling of NMR time series

Modeling time series of NMR spectra is a useful method to accurately extract information such as temporal concentration profiles from complex processes, e.g. reactions. Modeling these time series by using nonlinear optimization often suffers from high runtimes. On the other hand, using deep learning solves the modeling problem quickly, especially for single spectra with separated peaks. However, the accuracy decreases significantly when peaks overlap or cross. We propose a hybrid approach combining the strengths of both methods while mitigating their drawbacks. This hybrid methods improves on a previous work (Meinhardt et al., 2022) and employs neural networks to predict initial parameters for the optimization algorithm, which only needs to fine-tune the parameters afterwards. We present results for both constructed and experimental data sets and achieve improvements in both runtime and accuracy.

A Review on Long COVID Screening: Challenges and Perspectives Focusing on Exhaled Breath Gas Sensing

Long COVID (LC) is a great global health concern, affecting individuals recovering from SARS-CoV-2 infection. The persistent and varied symptoms across multiple organs complicate diagnosis and management, and an incomplete understanding of the condition hinders advancements in therapeutics. Current diagnostic methods face challenges related to standardization and completeness. To overcome this, new technologies such as sensor-based electronic noses are being explored for LC assessment, offering a noninvasive screening approach via volatile organic compounds (VOC) sensing in exhaled breath. Although specific LC-associated VOCs have not been fully characterized, insights from COVID-19 research suggest their potential as biomarkers. Additionally, AI-driven chemometrics are promising in identifying and predicting outcomes; despite challenges, AI-driven technologies hold the potential to enhance LC evaluation, providing rapid and accurate diagnostics for improved patient care and outcomes. This review underscores the importance of emerging and sensing technologies and comprehensive diagnostic strategies to address screening and treatment challenges in the face of LC.

Estimation of foveal avascular zone area from a B-scan OCT image using machine learning algorithms

PURPOSE

The objective of this study is to estimate the area of the Foveal Avascular Zone (FAZ) from B-scan OCT images using machine learning algorithms.

METHODS

We developed machine learning models to predict the FAZ area from OCT B-scan images of eyes without retinal vascular diseases. The study involved three models: Model 1 predicted the FAZ length from B-scan images; Model 2 estimated the FAZ area from the predicted length using 1, 3, or 5 horizontal measurements; and Model 3 converted the FAZ area from pixels to mm2. The models' performance was evaluated using Mean Absolute Error (MAE), Mean Squared Error (MSE), and the Coefficient of Determination (R2). The FAZ area was subsequently estimated by sequentially applying Models 1→2→3 on a new dataset.

RESULTS

Model 1 achieved a MAE of 2.86, MSE of 17.56, and R2 of 0.87. Model 2's performance improved with the number of horizontal measurements, with the best results obtained using 5 lines (MAE: 40.36, MSE: 3129.65, R2: 0.95). Model 3 achieved a MAE of 1.52e-3, MSE of 4.0e-6, and R2 of 1.0. The accuracy of FAZ area estimation increased with the number of B-scan images used, with the correlation coefficient rising from 0.475 (1 line) to 0.596 (5 lines). Bland-Altman analysis showed improved agreement between predicted and actual FAZ areas with increasing B-scan images, evidenced by decreasing biases and narrower limits of agreement.

CONCLUSIONS

This study successfully developed machine learning models capable of predicting FAZ area from OCT B-scan images. These findings demonstrate the potential for using OCT images to predict OCTA data, particularly in populations where OCTA imaging is challenging, such as children and the elderly. Future studies could explore the developmental mechanisms of the FAZ and macula, providing new insights into retinal health across different age groups.

Municipal solid waste management in Lagos State: Expansion diffusion of awareness

This study examined the role of waste management authorities in promoting public awareness of municipal solid waste management (MSWM) through the lens of the expansion diffusion theory (EDT). EDT emphasizes the spread of new ideas and practices within a society through various communication channels and distinct individuals within each system. We employed a mixed-method approach using 116 survey responses from Lagos residents and five semi-structured in-depth interviews. Our findings reveal the need for a more structured approach to create public awareness of MSWM, considering the distinct groups of residents in Lagos and their responses to innovation and knowledge diffusion. We propose four pillars on which waste management authorities in developing countries can sustain their MSWM awareness campaigns, as well as an awareness campaign strategy flowchart. Our findings add to the expanding body of research on public awareness and participation in MSWM, emphasizing the critical role that waste management authorities can play in fostering sustainable waste management awareness and practices.

The relationship between artificial intelligence and low-skilled employment in South Africa

As artificial intelligence (AI) continues to advance, its impact on employment is a topic of concern. In South Africa, where low-skilled labor forms a significant portion of the workforce, the integration of AI technologies raises questions about the future of employment opportunities and economic stability. This manuscript explores the relationship between AI adoption, low-skilled employment dynamics, and its implications using key economic indicators such as inflation, interest rates, and foreign direct investment (FDI). Employing the Vector Error Correction Model (VECM) approach from 2012Q1 to 2021Q4, the study's findings reveal a significant negative correlation between artificial intelligence and low-skilled employment in the long run. Granger causality tests reveal directional relationships, with AI investment unidirectionally causing low-skilled employment. As a policy implication, this study recommends implementing training programs to equip workers with the necessary skills to adapt to the evolving job market influenced by technological advancements. Additionally, it suggests monitoring the implementation of AI technologies and establishing policies to mitigate labor market disruptions.

Ethical Application of Generative Artificial Intelligence in Medicine

Generative artificial intelligence(AI) may revolutionize healthcare, providing solutions that range from enhancing diagnostic accuracy to personalizing treatment plans. However, its rapid and largely unregulated integration into medicine raises ethical concerns related to data integrity, patient safety, and appropriate oversight. One of the primary ethical challenges lies in generative AI's potential to produce misleading or fabricated information, posing risks of misdiagnosis or inappropriate treatment recommendations, which underscore the necessity for robust physician oversight. Transparency also remains a critical concern as the closed-source nature of many large-language models(LLMs) prevents both patients and healthcare providers from understanding the reasoning behind AI-generated outputs, potentially eroding trust. The lack of regulatory approval for AI as a medical device, combined with concerns around the security of patient-derived data and AI-generated synthetic data, further complicates its safe integration into clinical workflows. Furthermore, synthetic datasets generated by AI, while valuable for augmenting research in areas with scarce data, complicate questions of data ownership, patient consent, and scientific validity. Additionally, generative AI's ability to streamline administrative tasks risks depersonalizing care, further distancing providers from patients. These challenges compound the deeper issues plaguing the healthcare system, including the emphasis of volume and speed - over value and expertise. The utilization of generative AI in medicine brings about mass scaling of synthetic information, thereby necessitating careful adoption to protect patient care and medical advancement. Given these considerations, generative AI applications warrant regulatory and critical scrutiny . Key starting points include establishing strict standards for data security and transparency, implementing oversight akin to Institutional Review Boards(IRBs) to govern data usage, and developing interdisciplinary guidelines that involve developers, clinicians, and ethicists. By addressing these concerns, we can better align generative AI adoption with the core foundations of humanistic healthcare - preserving patient safety, autonomy, and trust while harnessing AI's transformative potential. LEVEL OF EVIDENCE: Level V: Expert Opinion.

Healthcare workers' knowledge and attitudes regarding artificial intelligence adoption in healthcare: A cross-sectional study

Background

The convergence of healthcare and artificial intelligence (AI) introduces a transformative era in medical practice. However, the knowledge and attitudes of healthcare workers concerning the adoption of artificial intelligence in healthcare are currently unknown.

Aims

The primary objective was to investigate the knowledge and attitudes of healthcare professionals in Dhaka city, Bangladesh, regarding the adoption of AI in healthcare.

Methods

A cross-sectional research design was employed, incorporating a dual-method approach to select participants using randomness and convenience sampling techniques. Validity was ensured through a literature review, content validity, and reliability assessment (Cronbach's alpha = 0.85), and exploratory factor analysis identified robust underlying factors. Data analysis involved descriptive and inferential statistics, including Fisher's exact tests, multivariate logistic regression, and Pearson correlation analysis, conducted using STATA software, providing a comprehensive understanding of healthcare workers' AI adoption in healthcare.

Results

This study revealed that age was a significant factor, with individuals aged 18-25 and 26-35 having higher odds of good knowledge and positive attitudes (AOR 1.56, 95 % CI 1.12-2.43; AOR 1.42, 95 % CI 0.98-2.34). Physicians (AOR 1.08, 95 % CI 0.78-1.89), hospital workers (AOR 1.29, 95 % CI 0.92-2.09), and full-time employees (AOR 1.45, 95 % CI 1.12-2.34) exhibited higher odds. Attending AI conferences (AOR 1.27, 95 % CI 0.92-2.23) and learning through research articles/journals (AOR 1.31, 95 % CI 0.98-2.09) were positively associated with good knowledge and positive attitudes. This research also emphasized the strong correlations between knowledge and positive attitudes (r = 0.89, P < 0.001), as well as negative attitudes with poor knowledge (r = 0.65, P < 0.001).

Conclusions

The study highlights the critical need for targeted educational interventions to bridge the knowledge gaps among healthcare professionals regarding AI adoption. The findings reveal that younger healthcare workers, those in full-time employment, and individuals with exposure to AI through conferences or research are more likely to possess good knowledge and hold positive attitudes towards AI integration. These results suggest that policies and training programs must be tailored to address specific demographic differences, ensuring that all groups are equipped to engage with AI technologies. Moreover, the study emphasizes the importance of continuous professional development, which could foster a workforce capable of harnessing AI's potential to improve patient outcomes and healthcare efficiency.

EEG correlates of static and dynamic face perception: The role of naturalistic motion

Much of our understanding of how the brain processes dynamic faces comes from research that compares static photographs to dynamic morphs, which exhibit simplified, computer-generated motion. By comparing static, video recorded, and dynamic morphed expressions, we aim to identify the neural correlates of naturalistic facial dynamism, using time-domain and time-frequency analysis. Dynamic morphs were made from the neutral and peak frames of video recorded transitions of happy and fearful expressions, which retained expression change and removed asynchronous and non-linear features of naturalistic facial motion. We found that dynamic morphs elicited increased N400 amplitudes and lower LPP amplitudes compared to other stimulus types. Video recordings elicited higher LPP amplitudes and greater frontal delta activity compared to other stimuli. Thematic analysis of participant interviews using a large language model revealed that participants found it difficult to assess the genuineness of morphed expressions, and easier to analyse the genuineness of happy compared to fearful expressions. Our findings suggest that animating real faces with artificial motion may violate expectations (N400) and reduce the social salience (LPP) of dynamic morphs. Results also suggest that delta oscillations in the frontal region may be involved with the perception of naturalistic facial motion in happy and fearful expressions. Overall, our findings highlight the sensitivity of neural mechanisms required for face perception to subtle changes in facial motion characteristics, which has important implications for neuroimaging research using faces with simplified motion.

Unleashing the future: The revolutionary role of machine learning and artificial intelligence in drug discovery

Drug discovery is a complex and multifaceted process aimed at identifying new therapeutic compounds with the potential to treat various diseases. Traditional methods of drug discovery are often time-consuming, expensive, and characterized by low success rates. Because of this, there is an urgent need to improve the drug development process using new technologies. The integration of the current state-of-art of artificial intelligence (AI) and machine learning (ML) approaches with conventional methods will enhance the efficiency and effectiveness of pharmaceutical research. This review highlights the transformative impact of AI and ML in drug discovery, discussing current applications, challenges, and future directions in harnessing these technologies to accelerate the development of innovative therapeutics. We have discussed the latest developments in AI and ML technologies to streamline several stages of drug discovery, from target identification and validation to lead optimization and preclinical studies.

Real-world utility of ChatGPT in pre-vasectomy counselling, a safe and efficient practice: a prospective single-centre clinical study

PURPOSE

This study sought to assess if pre-vasectomy counselling with ChatGPT can safely streamline the consultation process by reducing visit times and increasing patient satisfaction.

METHODS

A single-institution randomized pilot study was conducted to evaluate the safety and efficacy of ChatGPT for pre-vasectomy counselling. All adult patients interested in undergoing a vasectomy were included. Unwillingness to provide consent or not having internet access constituted exclusion. Patients were randomized 1:1 to ChatGPT with standard in-person or in-person consultation without ChatGPT. Length of visit, number of questions asked, and a Likert scale questionnaire (on a scale of 10, with 10 being defined as great and 0 being defined as poor), were collected. Descriptive statistics and a comparative analysis were performed.

RESULTS

18 patients were included with a mean age of 35.8 ± 5.4 (n = 9) in the intervention arm and 36.9 ± 7.4 (n = 9) in the control arm. Pre-vasectomy counselling with ChatGPT was associated with a higher provider perception of patient understanding of the procedure (8.8 ± 1.0 vs. 6.7 ± 2.8; p = 0.047) and a decreased length of in-person consultation (7.7 ± 2.3 min vs. 10.6 ± 3.4 min; p = 0.05). Quality of information provided by ChatGPT, ease of use, and overall experience were rated highly at 8.3 ± 1.9, 9.1 ± 1.5, and 8.6 ± 1.7, respectively.

CONCLUSIONS

ChatGPT for pre-vasectomy counselling improved the efficiency of consultations and the provider's perception of the patient's understanding of the procedure.

Artificial Intelligence in Uveitis: Innovations in Diagnosis and Therapeutic Strategies

In the dynamic field of ophthalmology, artificial intelligence (AI) is emerging as a transformative tool in managing complex conditions like uveitis. Characterized by diverse inflammatory responses, uveitis presents significant diagnostic and therapeutic challenges. This systematic review explores the role of AI in advancing diagnostic precision, optimizing therapeutic approaches, and improving patient outcomes in uveitis care. A comprehensive search of PubMed, Scopus, Google Scholar, Web of Science, and Embase identified over 10,000 articles using primary and secondary keywords related to AI and uveitis. Rigorous screening based on predefined criteria reduced the pool to 52 high-quality studies, categorized into six themes: diagnostic support algorithms, screening algorithms, standardization of Uveitis Nomenclature (SUN), AI applications in management, systemic implications of AI, and limitations with future directions. AI technologies, including machine learning (ML) and deep learning (DL), demonstrated proficiency in anterior chamber inflammation detection, vitreous haze grading, and screening for conditions like ocular toxoplasmosis. Despite these advancements, challenges such as dataset quality, algorithmic transparency, and ethical concerns persist. Future research should focus on developing robust, multimodal AI systems and fostering collaboration among academia and industry to ensure equitable, ethical, and effective AI applications. The integration of AI heralds a new era in uveitis management, emphasizing precision medicine and enhanced care delivery.

Pharmacogenomics education in China and the United States: advancing personalized medicine

Pharmacogenomics (PGx), an integral part of functional genomics and molecular pharmacology, has evolved significantly over the past decade. Our study reveals that PGx education in China and the United States has made substantial progress, with a particular emphasis on integrating PGx into medical curricula and clinical practice, leading to improved therapeutic strategies and patient outcomes. Consequently, both China and the United States are dedicated to fostering advancements in PGx education. This paper reviews PGx education in these two countries, highlighting its importance and providing an in-depth look at the current status and challenges within universities and clinical settings. Furthermore, it offers recommendations for advancing PGx education and contemplates future trends in both nations.

Non-Generative Artificial Intelligence (AI) in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning

The use of Artificial Intelligence (AI) within pathology and healthcare has advanced extensively. We have accordingly witnessed increased adoption of various AI tools which 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 healthcare thus far fall mostly under the non-generative AI domain, which includes supervised and unsupervised machine learning (ML) techniques. This review article explores how such non-generative 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 (i.e. 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 non-generative foundation models. Unsupervised learning techniques including clustering, dimensionality reduction, and anomaly detection are also discussed for their role in uncovering novel disease subtypes or identifying outliers. Technical details related to the application of non-generative AI algorithms for analyzing whole slide images is also highlighted. The performance, explainability and reliability of non-generative 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.

Empowering cancer prevention with AI: unlocking new frontiers in prediction, diagnosis, and intervention

Artificial intelligence is rapidly changing our world at an exponential rate and its transformative power has extensively reached important sectors like healthcare. In the fight against cancer, AI proved to be a novel and powerful tool, offering new hope for prevention and early detection. In this review, we will comprehensively explore the medical applications of AI, including early cancer detection through pathological and imaging analysis, risk stratification, patient triage, and the development of personalized prevention approaches. However, despite the successful impact AI has contributed to, we will also discuss the myriad of challenges that we have faced so far toward optimal AI implementation. There are problems when it comes to the best way in which we can use AI systemically. Having the correct data that can be understood easily must remain one of the most significant concerns in all its uses including sharing information. Another challenge that exists is how to interpret AI models because they are too complicated for people to follow through examples used in their developments which may affect trust, especially among medical professionals. Other considerations like data privacy, algorithm bias, and equitable access to AI tools have also arisen. Finally, we will evaluate possible future directions for this promising field that highlight AI's capacity to transform preventative cancer care.