Bias in artificial intelligence algorithms and recommendations for mitigation

Exploring trade-offs in equitable stroke risk prediction with parity-constrained and race-free models

A recent analysis of common stroke risk prediction models showed that performance differs between Black and White subgroups, and that applying standard machine learning methods does not reduce these disparities. There have been calls in the clinical literature to correct such disparities by removing race as a predictor (i.e., race-free models). Alternatively, a variety of machine learning methods have been proposed to constrain differences in model predictions between racial groups. In this work, we compare these approaches for equitable stroke risk prediction. We begin by proposing a discrete-time, neural network-based time-to-event model that incorporates a parity constraint designed to make predictions more similar between groups. Using harmonized data from Framingham Offspring, MESA, and ARIC studies, we develop both parity-constrained and unconstrained stroke risk prediction models, then compare their performance with race-free models in a held-out test set and a secondary validation set (REGARDS). Our evaluation includes both intra-group and inter-group performance metrics for right-censored time to event outcomes. Results illustrate a fundamental trade-off in which parity-constrained models must sacrifice intra-group calibration to improve inter-group discrimination performance, while the race-free models strike a balance between the two. Consequently, the choice of model must depend on the potential benefits and harms associated with the intended clinical use. All models as well as code implementing our approach are available in a public repository. More broadly, these results provide a roadmap for development of equitable clinical risk prediction models and illustrate both merits and limitations of a race-free approach.

Patient and clinician acceptability of automated extraction of social drivers of health from clinical notes in primary care

OBJECTIVE

Artificial Intelligence (AI)-based approaches for extracting Social Drivers of Health (SDoH) from clinical notes offer healthcare systems an efficient way to identify patients' social needs, yet we know little about the acceptability of this approach to patients and clinicians. We investigated patient and clinician acceptability through interviews.

MATERIALS AND METHODS

We interviewed primary care patients experiencing social needs (n = 19) and clinicians (n = 14) about their acceptability of "SDoH autosuggest," an AI-based approach for extracting SDoH from clinical notes. We presented storyboards depicting the approach and asked participants to rate their acceptability and discuss their rationale.

RESULTS

Participants rated SDoH autosuggest moderately acceptable (mean = 3.9/5 patients; mean = 3.6/5 clinicians). Patients' ratings varied across domains, with substance use rated most and employment rated least acceptable. Both groups raised concern about information integrity, actionability, impact on clinical interactions and relationships, and privacy. In addition, patients raised concern about transparency, autonomy, and potential harm, whereas clinicians raised concern about usability.

DISCUSSION

Despite reporting moderate acceptability of the envisioned approach, patients and clinicians expressed multiple concerns about AI systems that extract SDoH. Participants emphasized the need for high-quality data, non-intrusive presentation methods, and clear communication strategies regarding sensitive social needs. Findings underscore the importance of engaging patients and clinicians to mitigate unintended consequences when integrating AI approaches into care.

CONCLUSION

Although AI approaches like SDoH autosuggest hold promise for efficiently identifying SDoH from clinical notes, they must also account for concerns of patients and clinicians to ensure these systems are acceptable and do not undermine trust.

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation strategies. The integration of Artificial Intelligence (AI) into stroke rehabilitation presents significant advancements across the continuum of care, from acute diagnosis to long-term recovery. This review explores AI's role in stroke rehabilitation, highlighting its impact on early diagnosis, motor recovery, and cognitive rehabilitation. AI-driven imaging techniques, such as deep learning applied to CT and MRI scans, improve early diagnosis and identify ischemic penumbra, enabling timely, personalized interventions. AI-assisted decision support systems optimize acute stroke treatment, including thrombolysis and endovascular therapy. In motor rehabilitation, AI-powered robotics and exoskeletons provide precise, adaptive assistance, while AI-augmented Virtual and Augmented Reality environments offer immersive, tailored recovery experiences. Brain-Computer Interfaces utilize AI for neurorehabilitation through neural signal processing, supporting motor recovery. Machine learning models predict functional recovery outcomes and dynamically adjust therapy intensities. Wearable technologies equipped with AI enable continuous monitoring and real-time feedback, facilitating home-based rehabilitation. AI-driven tele-rehabilitation platforms overcome geographic barriers by enabling remote assessment and intervention. The review also addresses the ethical, legal, and regulatory challenges associated with AI implementation, including data privacy and technical integration. Future research directions emphasize the transformative potential of AI in stroke rehabilitation, with case studies and clinical trials illustrating the practical benefits and efficacy of AI technologies in improving patient recovery.

Explanation and elaboration of MedinAI: guidelines for reporting artificial intelligence studies in medicines, pharmacotherapy, and pharmaceutical services

The increasing adoption of artificial intelligence (AI) in medicines, pharmacotherapy, and pharmaceutical services necessitates clear guidance on reporting standards. While the MedinAI Statement (Bottacin in Int J Clin Pharm, https://doi.org/10.1007/s11096-025-01905-3, 2025) provides core guidelines for reporting AI studies in these fields, detailed explanations and practical examples are crucial for optimal implementation. This companion document was developed to offer comprehensive guidance and real-world examples for each guideline item. The document elaborates on all 14 items and 78 sub-items across four domains: core, ethical considerations in medication and pharmacotherapy, medicines as products, and services related to medicines and pharmacotherapy. Through clear, actionable guidance and diverse examples, this document enhances MedinAI's utility, enabling researchers and stakeholders to improve the quality and transparency of AI research reporting across various contexts, study designs, and development stages.

Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions

Cancer remains one of the leading causes of mortality worldwide, driving the need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool in oncology, with the potential to revolutionize cancer diagnosis, treatment, and management. This paper reviews recent advancements in AI applications within cancer research, focusing on early detection through computer-aided diagnosis, personalized treatment strategies, and drug discovery. We survey AI-enhanced diagnostic applications and explore AI techniques such as deep learning, as well as the integration of AI with nanomedicine and immunotherapy for cancer care. Comparative analyses of AI-based models versus traditional diagnostic methods are presented, highlighting AI's superior potential. Additionally, we discuss the importance of integrating social determinants of health to optimize cancer care. Despite these advancements, challenges such as data quality, algorithmic biases, and clinical validation remain, limiting widespread adoption. The review concludes with a discussion of the future directions of AI in oncology, emphasizing its potential to reshape cancer care by enhancing diagnosis, personalizing treatments and targeted therapies, and ultimately improving patient outcomes.

Underestimation of lung regions on chest X-ray segmentation masks assessed by comparison with total lung volume evaluated on computed tomography

INTRODUCTION

The lung regions on chest X-ray segmentation masks created according to the current gold standard method for AI-driven applications are underestimated. This can be evaluated by comparison with computed tomography.

METHODS

This retrospective study included data from non-contrast chest low-dose CT examinations of 55 individuals without pulmonary pathology. Synthetic X-ray images were generated by projecting a 3D CT examination onto a 2D image plane. Two experienced radiologists manually created two types of lung masks: 3D lung masks from CT examinations (ground truth for further calculations) and 2D lung masks from synthetic X-ray images (according to the current gold standard method: following the contours of other anatomical structures). Overlapping and non-overlapping lung regions covered by both types of masks were analyzed. Volume of the overlapping regions was compared with total lung volume, and volume fractions of non-overlapping lung regions in relation to the total lung volume were calculated. The performance results between the two radiologists were compared.

RESULTS

Significant differences were observed between lung regions covered by CT and synthetic X-ray masks. The mean volume fractions of the lung regions not covered by synthetic X-ray masks for the right lung, the left lung, and both lungs were 22.8 %, 32.9 %, and 27.3 %, respectively, for Radiologist 1 and 22.7 %, 32.9 %, and 27.3 %, respectively, for Radiologist 2. There was excellent spatial agreement between the masks created by the two radiologists.

CONCLUSIONS

Lung X-ray masks created according to the current gold standard method significantly underestimate lung regions and do not cover substantial portions of the lungs.

IMPLICATIONS FOR PRACTICE

Standard lung masks fail to encompass the whole range of the lungs and significantly restrict the field of analysis in AI-driven applications, which may lead to false conclusions and diagnoses.

Biases in Race and Ethnicity Introduced by Filtering Electronic Health Records for "Complete Data": Observational Clinical Data Analysis

Background

Integrated clinical databases from national biobanks have advanced the capacity for disease research. Data quality and completeness filters are used when building clinical cohorts to address limitations of data missingness. However, these filters may unintentionally introduce systemic biases when they are correlated with race and ethnicity.

Objective

In this study, we examined the race and ethnicity biases introduced by applying common filters to 4 clinical records databases. Specifically, we evaluated whether these filters introduce biases that disproportionately exclude minoritized groups.

Methods

We applied 19 commonly used data filters to electronic health record datasets from 4 geographically varied locations comprising close to 12 million patients to understand how using these filters introduces sample bias along racial and ethnic groupings. These filters covered a range of information, including demographics, medication records, visit details, and observation periods. We observed the variation in sample drop-off between self-reported ethnic and racial groups for each site as we applied each filter individually.

Results

Applying the observation period filter substantially reduced data availability across all races and ethnicities in all 4 datasets. However, among those examined, the availability of data in the white group remained consistently higher compared to other racial groups after applying each filter. Conversely, the Black or African American group was the most impacted by each filter on these 3 datasets: Cedars-Sinai dataset, UK Biobank, and Columbia University dataset. Among the 4 distinct datasets, only applying the filters to the All of Us dataset resulted in minimal deviation from the baseline, with most racial and ethnic groups following a similar pattern.

Conclusions

Our findings underscore the importance of using only necessary filters, as they might disproportionally affect data availability of minoritized racial and ethnic populations. Researchers must consider these unintentional biases when performing data-driven research and explore techniques to minimize the impact of these filters, such as probabilistic methods or adjusted cohort selection methods. Additionally, we recommend disclosing sample sizes for racial and ethnic groups both before and after data filters are applied to aid the reader in understanding the generalizability of the results. Future work should focus on exploring the effects of filters on downstream analyses.

Artificial intelligence and its application in clinical microbiology

INTRODUCTION

Traditional microbiological diagnostics face challenges in pathogen identification speed and antimicrobial resistance (AMR) evaluation. Artificial intelligence (AI) offers transformative solutions, necessitating a comprehensive review of its applications, advancements, and integration challenges in clinical microbiology.

AREAS COVERED

This review examines AI-driven methodologies, including machine learning (ML), deep learning (DL), and convolutional neural networks (CNNs), for enhancing pathogen detection, AMR prediction, and diagnostic imaging. Applications in virology (e.g. COVID-19 RT-PCR optimization), parasitology (e.g. malaria detection), and bacteriology (e.g. automated colony counting) are analyzed. A literature search was conducted using PubMed, Scopus, and Web of Science (2018-2024), prioritizing peer-reviewed studies on AI's diagnostic accuracy, workflow efficiency, and clinical validation.

EXPERT OPINION

AI significantly improves diagnostic precision and operational efficiency but requires robust validation to address data heterogeneity, model interpretability, and ethical concerns. Future success hinges on interdisciplinary collaboration to develop standardized, equitable AI tools tailored for global healthcare settings. Advancing explainable AI and federated learning frameworks will be critical for bridging current implementation gaps and maximizing AI's potential in combating infectious diseases.

Bias recognition and mitigation strategies in artificial intelligence healthcare applications

Artificial intelligence (AI) is delivering value across all aspects of clinical practice. However, bias may exacerbate healthcare disparities. This review examines the origins of bias in healthcare AI, strategies for mitigation, and responsibilities of relevant stakeholders towards achieving fair and equitable use. We highlight the importance of systematically identifying bias and engaging relevant mitigation activities throughout the AI model lifecycle, from model conception through to deployment and longitudinal surveillance.

Generative AI Models in Time-Varying Biomedical Data: Scoping Review

BACKGROUND

Trajectory modeling is a long-standing challenge in the application of computational methods to health care. In the age of big data, traditional statistical and machine learning methods do not achieve satisfactory results as they often fail to capture the complex underlying distributions of multimodal health data and long-term dependencies throughout medical histories. Recent advances in generative artificial intelligence (AI) have provided powerful tools to represent complex distributions and patterns with minimal underlying assumptions, with major impact in fields such as finance and environmental sciences, prompting researchers to apply these methods for disease modeling in health care.

OBJECTIVE

While AI methods have proven powerful, their application in clinical practice remains limited due to their highly complex nature. The proliferation of AI algorithms also poses a significant challenge for nondevelopers to track and incorporate these advances into clinical research and application. In this paper, we introduce basic concepts in generative AI and discuss current algorithms and how they can be applied to health care for practitioners with little background in computer science.

METHODS

We surveyed peer-reviewed papers on generative AI models with specific applications to time-series health data. Our search included single- and multimodal generative AI models that operated over structured and unstructured data, physiological waveforms, medical imaging, and multi-omics data. We introduce current generative AI methods, review their applications, and discuss their limitations and future directions in each data modality.

RESULTS

We followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines and reviewed 155 articles on generative AI applications to time-series health care data across modalities. Furthermore, we offer a systematic framework for clinicians to easily identify suitable AI methods for their data and task at hand.

CONCLUSIONS

We reviewed and critiqued existing applications of generative AI to time-series health data with the aim of bridging the gap between computational methods and clinical application. We also identified the shortcomings of existing approaches and highlighted recent advances in generative AI that represent promising directions for health care modeling.

Introducing the Team Card: Enhancing governance for medical Artificial Intelligence (AI) systems in the age of complexity

This paper introduces the Team Card (TC) as a protocol to address harmful biases in the development of clinical artificial intelligence (AI) systems by emphasizing the often-overlooked role of researchers' positionality. While harmful bias in medical AI, particularly in Clinical Decision Support (CDS) tools, is frequently attributed to issues of data quality, this limited framing neglects how researchers' worldviews-shaped by their training, backgrounds, and experiences-can influence AI design and deployment. These unexamined subjectivities can create epistemic limitations, amplifying biases and increasing the risk of inequitable applications in clinical settings. The TC emphasizes reflexivity-critical self-reflection-as an ethical strategy to identify and address biases stemming from the subjectivity of research teams. By systematically documenting team composition, positionality, and the steps taken to monitor and address unconscious bias, TCs establish a framework for assessing how diversity within teams impacts AI development. Studies across business, science, and organizational contexts demonstrate that diversity improves outcomes, including innovation, decision-making quality, and overall performance. However, epistemic diversity-diverse ways of thinking and problem-solving-must be actively cultivated through intentional, collaborative processes to mitigate bias effectively. By embedding epistemic diversity into research practices, TCs may enhance model performance, improve fairness and offer an empirical basis for evaluating how diversity influences bias mitigation efforts over time. This represents a critical step toward developing inclusive, ethical, and effective AI systems in clinical care. A publicly available prototype presenting our TC is accessible at https://www.teamcard.io/team/demo.

Evaluating fairness of machine learning prediction of prolonged wait times in Emergency Department with Interpretable eXtreme gradient boosting

It is essential to evaluate performance and assess quality before applying artificial intelligence (AI) and machine learning (ML) models to clinical practice. This study utilized ML to predict patient wait times in the Emergency Department (ED), determine model performance accuracies, and conduct fairness evaluations to further assess ethnic disparities in using ML for wait time prediction among different patient populations in the ED. This retrospective observational study included adult patients (age ≥18 years) in the ED (n=173,856 visits) who were assigned an Emergency Severity Index (ESI) level of 3 at triage. Prolonged wait time was defined as waiting time ≥30 minutes. We employed extreme gradient boosting (XGBoost) for predicting prolonged wait times. Model performance was assessed with accuracy, recall, precision, F1 score, and false negative rate (FNR). To perform the global and local interpretation of feature importance, we utilized Shapley additive explanations (SHAP) to interpret the output from the XGBoost model. Fairness in ML models were evaluated across sensitive attributes (sex, race and ethnicity, and insurance status) at both subgroup and individual levels. We found that nearly half (48.43%, 84,195) of ED patient visits demonstrated prolonged ED wait times. XGBoost model exhibited moderate accuracy performance (AUROC=0.81). When fairness was evaluated with FNRs, unfairness existed across different sensitive attributes (male vs. female, Hispanic vs. Non-Hispanic White, and patients with insurances vs. without insurance). The predicted FNRs were lower among females, Hispanics, and patients without insurance compared to their counterparts. Therefore, XGBoost model demonstrated acceptable performance in predicting prolonged wait times in ED visits. However, disparities arise in predicting patients with different sex, race and ethnicity, and insurance status. To enhance the utility of ML model predictions in clinical practice, conducting performance assessments and fairness evaluations are crucial.

Evaluating LLM-based generative AI tools in emergency triage: A comparative study of ChatGPT Plus, Copilot Pro, and triage nurses

BACKGROUND

The number of emergency department (ED) visits has been on steady increase globally. Artificial Intelligence (AI) technologies, including Large Language Model (LLMs)-based generative AI models, have shown promise in improving triage accuracy. This study evaluates the performance of ChatGPT and Copilot in triage at a high-volume urban hospital, hypothesizing that these tools can match trained physicians' accuracy and reduce human bias amidst ED crowding challenges.

METHODS

This single-center, prospective observational study was conducted in an urban ED over one week. Adult patients were enrolled through random 24-h intervals. Exclusions included minors, trauma cases, and incomplete data. Triage nurses assessed patients while an emergency medicine (EM) physician documented clinical vignettes and assigned emergency severity index (ESI) levels. These vignettes were then introduced to ChatGPT and Copilot for comparison with the triage nurse's decision.

RESULTS

The overall triage accuracy was 65.2 % for nurses, 66.5 % for ChatGPT, and 61.8 % for Copilot, with no significant difference (p = 0.000). Moderate agreement was observed between the EM physician and ChatGPT, triage nurses, and Copilot (Cohen's Kappa = 0.537, 0.477, and 0.472, respectively). In recognizing high-acuity patients, ChatGPT and Copilot outperformed triage nurses (87.8 % and 85.7 % versus 32.7 %, respectively). Compared to ChatGPT and Copilot, nurses significantly under-triaged patients (p < 0.05). The analysis of predictive performance for ChatGPT, Copilot, and triage nurses demonstrated varying discrimination abilities across ESI levels, all of which were statistically significant (p < 0.05). ChatGPT and Copilot exhibited consistent accuracy across age, gender, and admission time, whereas triage nurses were more likely to mistriage patients under 45 years old.

CONCLUSION

ChatGPT and Copilot outperform traditional nurse triage in identifying high-acuity patients, but real-time ED capacity data is crucial to prevent overcrowding and ensure high-quality of emergency care.

The Role of Artificial Intelligence in Obesity Medicine

The rising prevalence of obesity presents significant health, economic, and social challenges, necessitating a comprehensive approach to prevention, diagnosis, treatment, and long-term management. This review highlights the transformative role of artificial intelligence in obesity medicine, showcasing how technologies such as machine learning, deep learning, natural language processing, and large language models improve obesity management. The capacity of artificial intelligence to analyze extensive datasets enables predictive analytics, personalized treatment plans, and real-time behavioral interventions. Despite its potential, integrating artificial intelligence in obesity medicine faces challenges and ethical considerations, such as data privacy, algorithmic bias, artificial intelligence hallucination, transparency, and implementation barriers.

Machine Learning Models as Early Warning Systems for Neonatal Infection

Neonatal infections pose a significant threat to the health of newborns. Associated morbidity and mortality risks underscore the urgency of prompt diagnosis and treatment with appropriate empiric antibiotics. Delay in treatment can be fatal; thus, early detection improves outcomes. However, diagnosing early is a challenge as signs and symptoms of neonatal infection are non-specific and overlap with non-infectious conditions. Machine learning (ML) offers promise in early detection, utilizing various data sources and methodologies. However, ML models require rigorous validation and consideration of various challenges, including false alarms and user acceptance requiring careful integration and ongoing evaluation for successful implementation.

Future Use of AI in Diagnostic Medicine: 2-Wave Cross-Sectional Survey Study

BACKGROUND

The rapid evolution of artificial intelligence (AI) presents transformative potential for diagnostic medicine, offering opportunities to enhance diagnostic accuracy, reduce costs, and improve patient outcomes.

OBJECTIVE

This study aimed to assess the expected future impact of AI on diagnostic medicine by comparing global researchers' expectations using 2 cross-sectional surveys.

METHODS

The surveys were conducted in September 2020 and February 2023. Each survey captured a 10-year projection horizon, gathering insights from >3700 researchers with expertise in AI and diagnostic medicine from all over the world. The survey sought to understand the perceived benefits, integration challenges, and evolving attitudes toward AI use in diagnostic settings.

RESULTS

Results indicated a strong expectation among researchers that AI will substantially influence diagnostic medicine within the next decade. Key anticipated benefits include enhanced diagnostic reliability, reduced screening costs, improved patient care, and decreased physician workload, addressing the growing demand for diagnostic services outpacing the supply of medical professionals. Specifically, x-ray diagnosis, heart rhythm interpretation, and skin malignancy detection were identified as the diagnostic tools most likely to be integrated with AI technologies due to their maturity and existing AI applications. The surveys highlighted the growing optimism regarding AI's ability to transform traditional diagnostic pathways and enhance clinical decision-making processes. Furthermore, the study identified barriers to the integration of AI in diagnostic medicine. The primary challenges cited were the difficulties of embedding AI within existing clinical workflows, ethical and regulatory concerns, and data privacy issues. Respondents emphasized uncertainties around legal responsibility and accountability for AI-supported clinical decisions, data protection challenges, and the need for robust regulatory frameworks to ensure safe AI deployment. Ethical concerns, particularly those related to algorithmic transparency and bias, were noted as increasingly critical, reflecting a heightened awareness of the potential risks associated with AI adoption in clinical settings. Differences between the 2 survey waves indicated a growing focus on ethical and regulatory issues, suggesting an evolving recognition of these challenges over time.

CONCLUSIONS

Despite these barriers, there was notable consistency in researchers' expectations across the 2 survey periods, indicating a stable and sustained outlook on AI's transformative potential in diagnostic medicine. The findings show the need for interdisciplinary collaboration among clinicians, AI developers, and regulators to address ethical and practical challenges while maximizing AI's benefits. This study offers insights into the projected trajectory of AI in diagnostic medicine, guiding stakeholders, including health care providers, policy makers, and technology developers, on navigating the opportunities and challenges of AI integration.

Evaluating and addressing demographic disparities in medical large language models: a systematic review

BACKGROUND

Large language models are increasingly evaluated for use in healthcare. However, concerns about their impact on disparities persist. This study reviews current research on demographic biases in large language models to identify prevalent bias types, assess measurement methods, and evaluate mitigation strategies.

METHODS

We conducted a systematic review, searching publications from January 2018 to July 2024 across five databases. We included peer-reviewed studies evaluating demographic biases in large language models, focusing on gender, race, ethnicity, age, and other factors. Study quality was assessed using the Joanna Briggs Institute Critical Appraisal Tools.

RESULTS

Our review included 24 studies. Of these, 22 (91.7%) identified biases. Gender bias was the most prevalent, reported in 15 of 16 studies (93.7%). Racial or ethnic biases were observed in 10 of 11 studies (90.9%). Only two studies found minimal or no bias in certain contexts. Mitigation strategies mainly included prompt engineering, with varying effectiveness. However, these findings are tempered by a potential publication bias, as studies with negative results are less frequently published.

CONCLUSION

Biases are observed in large language models across various medical domains. While bias detection is improving, effective mitigation strategies are still developing. As LLMs increasingly influence critical decisions, addressing these biases and their resultant disparities is essential for ensuring fair artificial intelligence systems. Future research should focus on a wider range of demographic factors, intersectional analyses, and non-Western cultural contexts.

Artificial Intelligence Algorithms, Bias, and Innovation: Implications for Social Work

PURPOSE

Artificial Intelligence (AI) technologies are rapidly expanding across diverse contexts. As the reach of AI continues to grow, there is a need to examine student perspectives on the increasing prevalence of AI and AI-based practice approaches in social work.

MATERIALS AND METHODS

In this qualitative study, we conducted structured interviews with 15 students in bachelors and masters social work programs. We developed an interview guide with a list of questions to ask students and no prior knowledge of AI was required by the students. The study was framed based on an interpretive phenomenological analysis approach.

RESULTS

Through thematic analysis, five key themes were developed, including 1) Risks associated with AI, 2) Ethical Concerns in AI and Technology Use, 3) Bias and Fairness in AI, 4) Applications and Possibilities of AI in Social Work, and 5) Training and Awareness of AI in Social Work.

DISCUSSION

Social workers can help disadvantaged clients by ensuring access to the various AI technologies and facilitating social welfare interventions created using these technologies. There is a need to address the gap in the existing literature about the use of AI in social work practice and education. Social work researchers can explore and conduct future studies that utilize mixed methods methodologies that can evaluate the use of AI in social work domains.

CONCLUSION

This study highlights the need to increase awareness of AI in social work education and practice settings given the potential of these technologies to aid various aspects of social work practice.

Artificial intelligence to revolutionize IBD clinical trials: a comprehensive review

Integrating artificial intelligence (AI) into clinical trials for inflammatory bowel disease (IBD) has potential to be transformative to the field. This article explores how AI-driven technologies, including machine learning (ML), natural language processing, and predictive analytics, have the potential to enhance important aspects of IBD trials-from patient recruitment and trial design to data analysis and personalized treatment strategies. As AI advances, it has potential to improve long-standing challenges in trial efficiency, accuracy, and personalization with the goal of accelerating the discovery of novel therapies and improve outcomes for people living with IBD. AI can streamline multiple trial phases, from target identification and patient recruitment to data analysis and monitoring. By integrating multi-omics data, electronic health records, and imaging repositories, AI can uncover molecular targets and personalize trial strategies, ultimately expediting drug development. However, the adoption of AI in IBD clinical trials encounters significant challenges. These include technical barriers in data integration, ethical concerns regarding patient privacy, and regulatory issues related to AI validation standards. Additionally, AI models risk producing biased outcomes if training datasets lack diversity, potentially impacting underrepresented populations in clinical trials. Addressing these limitations requires standardized data formats, interdisciplinary collaboration, and robust ethical frameworks to ensure inclusivity and accuracy. Continued partnerships among clinicians, researchers, data scientists, and regulators will be essential to establish transparent, patient-centered AI frameworks. By overcoming these obstacles, AI has the potential to enhance the efficiency, equity, and efficacy of IBD clinical trials, ultimately benefiting patient care.

Harnessing precision nutrition to individualize weight restoration in anorexia nervosa

Anorexia nervosa (AN) is a severe psychiatric disorder for which effective treatment and sustained recovery are contingent upon successful weight restoration, yet the efficacy of existing treatments is suboptimal. This narrative review considers the potential of precision nutrition for tailoring dietary interventions to individual characteristics to enhance acute and longer-term weight outcomes in AN. We review key factors that drive variation in nutritional requirements, including energy expenditure, fecal energy loss, the gut microbiota, genetic factors, and psychiatric comorbidities. Although scientific evidence supporting precision nutrition in AN is limited, preliminary findings suggest that individualized nutrition therapies, particularly those considering duration of illness and the gut microbiota, may augment weight gain. Some patients may benefit from microbiota-directed dietary plans that focus on restoring microbial diversity, keystone taxa, or functions that promote energy absorption, which could enhance weight restoration-although stronger evidence is needed to support this approach. Furthermore, accounting for psychiatric comorbidities such as depression and anxiety as well as genetic factors influencing metabolism may help refine nutrition prescriptions improving upon existing energy estimation equations, which were not developed for patients with AN. Given the reliance on large sample sizes, costly data collection, and the need for computationally intensive artificial intelligence algorithms to assimilate deep phenotypes into personalized interventions, we highlight practical considerations related to the implementation of precision nutrition approaches in clinical practice. More research is needed to identify which factors, including metabolic profiles, genetic markers, demographics, and habitual lifestyle behaviors, are most critical to target for individualizing weight restoration, and whether personalized recommendations can be practicably applied to improve and sustain patient recovery from this debilitating disorder with high relapse and mortality rates.

Addressing hidden risks: Systematic review of artificial intelligence biases across racial and ethnic groups in cardiovascular diseases

BACKGROUND

Artificial intelligence (AI)-based models are increasingly being integrated into cardiovascular medicine. Despite promising potential, racial and ethnic biases remain a key concern regarding the development and implementation of AI models in clinical settings.

OBJECTIVE

This systematic review offers an overview of the accuracy and clinical applicability of AI models for cardiovascular diagnosis and prognosis across diverse racial and ethnic groups.

METHOD

A comprehensive literature search was conducted across four medical and scientific databases: PubMed, MEDLINE via Ovid, Scopus, and the Cochrane Library, to evaluate racial and ethnic disparities in cardiovascular medicine.

RESULTS

A total of 1704 references were screened, of which 11 articles were included in the final analysis. Applications of AI-based algorithms across different race/ethnic groups were varied and involved diagnosis, prognosis, and imaging segmentation. Among the 11 studies, 9 (82%) concluded that racial/ethnic bias existed, while 2 (18%) found no differences in the outcomes of AI models across various ethnicities.

CONCLUSION

Our results suggest significant differences in how AI models perform in cardiovascular medicine across diverse racial and ethnic groups.

CLINICAL RELEVANCE STATEMENT

The increasing integration of AI into cardiovascular medicine highlights the importance of evaluating its performance across diverse populations. This systematic review underscores the critical need to address racial and ethnic disparities in AI-based models to ensure equitable healthcare delivery.

Making Pathologists Ready for the New Artificial Intelligence Era: Changes in Required Competencies

In recent years, there has been an increasing interest in developing and using artificial intelligence (AI) models in pathology. Although pathologists generally have a positive attitude toward AI, they report a lack of knowledge and skills regarding how to use it in practice. Furthermore, it remains unclear what skills pathologists would require to use AI adequately and responsibly. However, adequate training of (future) pathologists is essential for successful AI use in pathology. In this paper, we assess which entrustable professional activities (EPAs) and associated competencies pathologists should acquire in order to use AI in their daily practice. We make use of the available academic literature, including literature in radiology, another image-based discipline, which is currently more advanced in terms of AI development and implementation. Although microscopy evaluation and reporting could be transferrable to AI in the future, most of the current pathologist EPAs and competencies will likely remain relevant when using AI techniques and interpreting and communicating results for individual patient cases. In addition, new competencies related to technology evaluation and implementation will likely be necessary, along with knowing one's own strengths and limitations in human-AI interactions. Because current EPAs do not sufficiently address the need to train pathologists in developing expertise related to technology evaluation and implementation, we propose a new EPA to enable pathology training programs to make pathologists fit for the new AI era "using AI in diagnostic pathology practice" and outline its associated competencies.

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.

A scoping review and expert consensus on digital determinants of health

Objective

To map how social, commercial, political and digital determinants of health have changed or emerged during the recent digital transformation of society and to identify priority areas for policy action.

Methods

We systematically searched MEDLINE, Embase and Web of Science on 24 September 2023, to identify eligible reviews published in 2018 and later. To ensure we included the most recent literature, we supplemented our review with non-systematic searches in PubMed® and Google Scholar, along with records identified by subject matter experts. Using thematic analysis, we clustered the extracted data into five societal domains affected by digitalization. The clustering also informed a novel framework, which the authors and contributors reviewed for comprehensiveness and accuracy. Using a two-round consensus process, we rated the identified determinants into high, moderate and low urgency for policy actions.

Findings

We identified 13 804 records, of which 204 met the inclusion criteria. A total of 127 health determinants were found to have emerged or changed during the digital transformation of society (37 digital, 33 social, 33 commercial and economic and 24 political determinants). Of these, 30 determinants (23.6%) were considered particularly urgent for policy action.

Conclusion

This review offers a comprehensive overview of health determinants across digital, social, commercial and economic, and political domains, highlighting how policy decisions, individual behaviours and broader factors influence health by digitalization. The findings deepen our understanding of how health outcomes manifest within a digital ecosystem and inform strategies for addressing the complex and evolving networks of health determinants.

The bias algorithm: how AI in healthcare exacerbates ethnic and racial disparities - a scoping review

This scoping review examined racial and ethnic bias in artificial intelligence health algorithms (AIHA), the role of stakeholders in oversight, and the consequences of AIHA for health equity. Using the PRISMA-ScR guidelines, databases were searched between 2020 and 2024 using the terms racial and ethnic bias in health algorithms resulting in a final sample of 23 sources. Suggestions for how to mitigate algorithmic bias were compiled and evaluated, roles played by stakeholders were identified, and governance and stewardship plans for AIHA were examined. While AIHA represent a significant breakthrough in predictive analytics and treatment optimization, regularly outperforming humans in diagnostic precision and accuracy, they also present serious challenges to patient privacy, data security, institutional transparency, and health equity. Evidence from extant sources including those in this review showed that AIHA carry the potential to perpetuate health inequities. While the current study considered AIHA in the US, the use of AIHA carries implications for global health equity.

Exploring the potential and limitations of artificial intelligence in animal anatomy

BACKGROUND

Artificial intelligence (AI) is revolutionizing veterinary medicine, particularly in the domain of veterinary anatomy. At present, there is no existing review article in the literature that examines the prospects and challenges associated with the use of AI in animal anatomy education.

STUDY DESIGN

Narrative review.

OBJECTIVE

This review article explores the prospects and drawbacks of AI applications in veterinary anatomy. Anatomy and AI-powered diagnostic systems enhance clinical examination, diagnosis, and treatment by analyzing vast datasets, improving accuracy, and detecting subtle anomalies.

METHODS

We reviewed and analyzed recent literature on AI applications in veterinary anatomy education, emphasizing their potential, limitations, and future directions..

CONCLUSION

In veterinary anatomy education, AI integrates advanced tools like three-dimensional (3D) models, virtual reality (VR), and augmented reality (AR), offering dynamic and interactive learning experiences to students as well as the faculty of veterinary institutions across the globe. Despite these advantages, AI faces challenges such as the need for extensive, high-quality data, potential biases, and issues with algorithmic transparency. Additionally, virtual dissection and educational tools may impact hands-on learning and ethical and legal concerns regarding data privacy must be addressed. Balancing AI integration with traditional skills and addressing these challenges will maximize AI's benefits in veterinary anatomy and ensure comprehensive veterinary care.

Evaluation of the Reliability of ChatGPT to Provide Guidance on Recombinant Zoster Vaccination for Patients With Rheumatic and Musculoskeletal Diseases

INTRODUCTION

Large language models (LLMs) such as ChatGPT can potentially transform the delivery of health information. This study aims to evaluate the accuracy and completeness of ChatGPT in responding to questions on recombinant zoster vaccination (RZV) in patients with rheumatic and musculoskeletal diseases.

METHODS

A cross-sectional study was conducted using 20 prompts based on information from the Centers for Disease Control and Prevention (CDC), the Advisory Committee on Immunization Practices (ACIP), and the American College of Rheumatology (ACR). These prompts were inputted into ChatGPT 3.5. Five rheumatologists independently scored the ChatGPT responses for accuracy (Likert 1 to 5) and completeness (Likert 1 to 3) compared with validated information sources (CDC, ACIP, and ACR).

RESULTS

The overall mean accuracy of ChatGPT responses on a 5-point scale was 4.04, with 80% of responses scoring ≥4. The mean completeness score of ChatGPT response on a 3-point scale was 2.3, with 95% of responses scoring ≥2. Among the 5 raters, ChatGPT unanimously scored with high accuracy and completeness to various patient and physician questions surrounding RZV. There was one instance where it scored with low accuracy and completeness. Although not significantly different, ChatGPT demonstrated the highest accuracy and completeness in answering questions related to ACIP guidelines compared with other information sources.

CONCLUSIONS

ChatGPT exhibits promising ability to address specific queries regarding RZV for rheumatic and musculoskeletal disease patients. However, it is essential to approach ChatGPT with caution due to risk of misinformation. This study emphasizes the importance of rigorously validating LLMs as a health information source.

Harnessing Artificial Intelligence to Enhance Global Breast Cancer Care: A Scoping Review of Applications, Outcomes, and Challenges

BACKGROUND

In recent years, Artificial Intelligence (AI) has shown transformative potential in advancing breast cancer care globally. This scoping review seeks to provide a comprehensive overview of AI applications in breast cancer care, examining how they could reshape diagnosis, treatment, and management on a worldwide scale and discussing both the benefits and challenges associated with their adoption.

METHODS

In accordance with PRISMA-ScR and ensuing guidelines on scoping reviews, PubMed, Web of Science, Cochrane Library, and Embase were systematically searched from inception to end of May 2024. Keywords included "Artificial Intelligence" and "Breast Cancer". Original studies were included based on their focus on AI applications in breast cancer care and narrative synthesis was employed for data extraction and interpretation, with the findings organized into coherent themes.

RESULTS

Finally, 84 articles were included. The majority were conducted in developed countries (n = 54). The majority of publications were in the last 10 years (n = 83). The six main themes for AI applications were AI for breast cancer screening (n = 32), AI for image detection of nodal status (n = 7), AI-assisted histopathology (n = 8), AI in assessing post-neoadjuvant chemotherapy (NACT) response (n = 23), AI in breast cancer margin assessment (n = 5), and AI as a clinical decision support tool (n = 9). AI has been used as clinical decision support tools to augment treatment decisions for breast cancer and in multidisciplinary tumor board settings. Overall, AI applications demonstrated improved accuracy and efficiency; however, most articles did not report patient-centric clinical outcomes.

CONCLUSIONS

AI applications in breast cancer care show promise in enhancing diagnostic accuracy and treatment planning. However, persistent challenges in AI adoption, such as data quality, algorithm transparency, and resource disparities, must be addressed to advance the field.

Bias Mitigation in Primary Health Care Artificial Intelligence Models: Scoping Review

BACKGROUND

Artificial intelligence (AI) predictive models in primary health care have the potential to enhance population health by rapidly and accurately identifying individuals who should receive care and health services. However, these models also carry the risk of perpetuating or amplifying existing biases toward diverse groups. We identified a gap in the current understanding of strategies used to assess and mitigate bias in primary health care algorithms related to individuals' personal or protected attributes.

OBJECTIVE

This study aimed to describe the attempts, strategies, and methods used to mitigate bias in AI models within primary health care, to identify the diverse groups or protected attributes considered, and to evaluate the results of these approaches on both bias reduction and AI model performance.

METHODS

We conducted a scoping review following Joanna Briggs Institute (JBI) guidelines, searching Medline (Ovid), CINAHL (EBSCO), PsycINFO (Ovid), and Web of Science databases for studies published between January 1, 2017, and November 15, 2022. Pairs of reviewers independently screened titles and abstracts, applied selection criteria, and performed full-text screening. Discrepancies regarding study inclusion were resolved by consensus. Following reporting standards for AI in health care, we extracted data on study objectives, model features, targeted diverse groups, mitigation strategies used, and results. Using the mixed methods appraisal tool, we appraised the quality of the studies.

RESULTS

After removing 585 duplicates, we screened 1018 titles and abstracts. From the remaining 189 full-text articles, we included 17 studies. The most frequently investigated protected attributes were race (or ethnicity), examined in 12 of the 17 studies, and sex (often identified as gender), typically classified as "male versus female" in 10 of the studies. We categorized bias mitigation approaches into four clusters: (1) modifying existing AI models or datasets, (2) sourcing data from electronic health records, (3) developing tools with a "human-in-the-loop" approach, and (4) identifying ethical principles for informed decision-making. Algorithmic preprocessing methods, such as relabeling and reweighing data, along with natural language processing techniques that extract data from unstructured notes, showed the greatest potential for bias mitigation. Other methods aimed at enhancing model fairness included group recalibration and the application of the equalized odds metric. However, these approaches sometimes exacerbated prediction errors across groups or led to overall model miscalibrations.

CONCLUSIONS

The results suggest that biases toward diverse groups are more easily mitigated when data are open-sourced, multiple stakeholders are engaged, and during the algorithm's preprocessing stage. Further empirical studies that include a broader range of groups, such as Indigenous peoples in Canada, are needed to validate and expand upon these findings.

TRIAL REGISTRATION

OSF Registry osf.io/9ngz5/; https://osf.io/9ngz5/.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.2196/46684.

Harnessing artificial intelligence in sepsis care: advances in early detection, personalized treatment, and real-time monitoring

Sepsis remains a leading cause of morbidity and mortality worldwide due to its rapid progression and heterogeneous nature. This review explores the potential of Artificial Intelligence (AI) to transform sepsis management, from early detection to personalized treatment and real-time monitoring. AI, particularly through machine learning (ML) techniques such as random forest models and deep learning algorithms, has shown promise in analyzing electronic health record (EHR) data to identify patterns that enable early sepsis detection. For instance, random forest models have demonstrated high accuracy in predicting sepsis onset in intensive care unit (ICU) patients, while deep learning approaches have been applied to recognize complications such as sepsis-associated acute respiratory distress syndrome (ARDS). Personalized treatment plans developed through AI algorithms predict patient-specific responses to therapies, optimizing therapeutic efficacy and minimizing adverse effects. AI-driven continuous monitoring systems, including wearable devices, provide real-time predictions of sepsis-related complications, enabling timely interventions. Beyond these advancements, AI enhances diagnostic accuracy, predicts long-term outcomes, and supports dynamic risk assessment in clinical settings. However, ethical challenges, including data privacy concerns and algorithmic biases, must be addressed to ensure fair and effective implementation. The significance of this review lies in addressing the current limitations in sepsis management and highlighting how AI can overcome these hurdles. By leveraging AI, healthcare providers can significantly enhance diagnostic accuracy, optimize treatment protocols, and improve overall patient outcomes. Future research should focus on refining AI algorithms with diverse datasets, integrating emerging technologies, and fostering interdisciplinary collaboration to address these challenges and realize AI's transformative potential in sepsis care.

Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare

BACKGROUND AND AIM

Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress and quality of life in affected children. With the advent of artificial intelligence (AI), there's a growing interest in leveraging its capabilities to improve the diagnosis and management of pediatric epilepsy. This review aims to assess the effectiveness of AI in pediatric epilepsy detection while considering the ethical implications surrounding its implementation.

METHODOLOGY

A comprehensive systematic review was conducted across multiple databases including PubMed, EMBASE, Google Scholar, Scopus, and Medline. Search terms encompassed "pediatric epilepsy," "artificial intelligence," "machine learning," "ethical considerations," and "data security." Publications from the past decade were scrutinized for methodological rigor, with a focus on studies evaluating AI's efficacy in pediatric epilepsy detection and management.

RESULTS

AI systems have demonstrated strong potential in diagnosing and monitoring pediatric epilepsy, often matching clinical accuracy. For example, AI-driven decision support achieved 93.4% accuracy in diagnosis, closely aligning with expert assessments. Specific methods, like EEG-based AI for detecting interictal discharges, showed high specificity (93.33%-96.67%) and sensitivity (76.67%-93.33%), while neuroimaging approaches using rs-fMRI and DTI reached up to 97.5% accuracy in identifying microstructural abnormalities. Deep learning models, such as CNN-LSTM, have also enhanced seizure detection from video by capturing subtle movement and expression cues. Non-EEG sensor-based methods effectively identified nocturnal seizures, offering promising support for pediatric care. However, ethical considerations around privacy, data security, and model bias remain crucial for responsible AI integration.

CONCLUSION

While AI holds immense potential to enhance pediatric epilepsy management, ethical considerations surrounding transparency, fairness, and data security must be rigorously addressed. Collaborative efforts among stakeholders are imperative to navigate these ethical challenges effectively, ensuring responsible AI integration and optimizing patient outcomes in pediatric epilepsy care.

Disability Ethics and Education in the Age of Artificial Intelligence: Identifying Ability Bias in ChatGPT and Gemini

OBJECTIVE

To identify and quantify ability bias in generative artificial intelligence large language model chatbots, specifically OpenAI's ChatGPT and Google's Gemini.

DESIGN

Observational study of language usage in generative artificial intelligence models.

SETTING

Investigation-only browser profile restricted to ChatGPT and Gemini.

PARTICIPANTS

Each chatbot generated 60 descriptions of people prompted without specified functional status, 30 descriptions of people with a disability, 30 descriptions of patients with a disability, and 30 descriptions of athletes with a disability (N=300).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURES

Generated descriptions produced by the models were parsed into words that were linguistically analyzed into favorable qualities or limiting qualities.

RESULTS

Both large language models significantly underestimated disability in a population of people, and linguistic analysis showed that descriptions of people, patients, and athletes with a disability were generated as having significantly fewer favorable qualities and significantly more limitations than people without a disability in both ChatGPT and Gemini.

CONCLUSIONS

Generative artificial intelligence chatbots demonstrate quantifiable ability bias and often exclude people with disabilities in their responses. Ethical use of these generative large language model chatbots in medical systems should recognize this limitation, and further consideration should be taken in developing equitable artificial intelligence technologies.

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review

Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of large language models, has played a significant role in the detection, risk assessment, and prevention of suicide. The purpose of this review was to explore the use of LLM tools in various aspects of suicide prevention. PubMed, Embase, Web of Science, Scopus, APA PsycNet, Cochrane Library, and IEEE Xplore-for studies published were systematically searched for articles published between January 1, 2018, until April 2024. The 29 reviewed studies utilized LLMs such as GPT, Llama, and BERT. We categorized the studies into three main tasks: detecting suicidal ideation or behaviors, assessing the risk of suicidal ideation, and preventing suicide by predicting attempts. Most of the studies demonstrated that these models are highly efficient, often outperforming mental health professionals in early detection and prediction capabilities. Large language models demonstrate significant potential for identifying and detecting suicidal behaviors and for saving lives. Nevertheless, ethical problems still need to be examined and cooperation with skilled professionals is essential.

Race to the Moon or the Bottom? Applications, Performance, and Ethical Considerations of Artificial Intelligence in Prosthodontics and Implant Dentistry

Objectives: This review aims to explore the applications of artificial intelligence (AI) in prosthodontics and implant dentistry, focusing on its performance outcomes and associated ethical concerns. Materials and Methods: Following the PRISMA guidelines, a search was conducted across databases such as PubMed, Medline, Web of Science, and Scopus. Studies published between January 2022 and May 2024, in English, were considered. The Population (P) included patients or extracted teeth with AI applications in prosthodontics and implant dentistry; the Intervention (I) was AI-based tools; the Comparison (C) was traditional methods, and the Outcome (O) involved AI performance outcomes and ethical considerations. The Newcastle-Ottawa Scale was used to assess the quality and risk of bias in the studies. Results: Out of 3420 initially identified articles, 18 met the inclusion criteria for AI applications in prosthodontics and implant dentistry. The review highlighted AI's significant role in improving diagnostic accuracy, treatment planning, and prosthesis design. AI models demonstrated high accuracy in classifying dental implants and predicting implant outcomes, although limitations were noted in data diversity and model generalizability. Regarding ethical issues, five studies identified concerns such as data privacy, system bias, and the potential replacement of human roles by AI. While patients generally viewed AI positively, dental professionals expressed hesitancy due to a lack of familiarity and regulatory guidelines, highlighting the need for better education and ethical frameworks. Conclusions: AI has the potential to revolutionize prosthodontics and implant dentistry by enhancing treatment accuracy and efficiency. However, there is a pressing need to address ethical issues through comprehensive training and the development of regulatory frameworks. Future research should focus on broadening AI applications and addressing the identified ethical concerns.

Embedded Ethics in Practice: A Toolbox for Integrating the Analysis of Ethical and Social Issues into Healthcare AI Research

Integrating artificial intelligence (AI) into critical domains such as healthcare holds immense promise. Nevertheless, significant challenges must be addressed to avoid harm, promote the well-being of individuals and societies, and ensure ethically sound and socially just technology development. Innovative approaches like Embedded Ethics, which refers to integrating ethics and social science into technology development based on interdisciplinary collaboration, are emerging to address issues of bias, transparency, misrepresentation, and more. This paper aims to develop this approach further to enable future projects to effectively deploy it. Based on the practical experience of using ethics and social science methodology in interdisciplinary AI-related healthcare consortia, this paper presents several methods that have proven helpful for embedding ethical and social science analysis and inquiry. They include (1) stakeholder analyses, (2) literature reviews, (3) ethnographic approaches, (4) peer-to-peer interviews, (5) focus groups, (6) interviews with affected groups and external stakeholders, (7) bias analyses, (8) workshops, and (9) interdisciplinary results dissemination. We believe that applying Embedded Ethics offers a pathway to stimulate reflexivity, proactively anticipate social and ethical concerns, and foster interdisciplinary inquiry into such concerns at every stage of technology development. This approach can help shape responsible, inclusive, and ethically aware technology innovation in healthcare and beyond.

Artificial intelligence in radiation therapy treatment planning: A discrete choice experiment

INTRODUCTION

The application of artificial intelligence (AI) in radiation therapy holds promise for addressing challenges, such as healthcare staff shortages, increased efficiency and treatment planning variations. Increased AI adoption has the potential to standardise treatment protocols, enhance quality, improve patient outcomes, and reduce costs. However, drawbacks include impacts on employment and algorithmic biases, making it crucial to navigate trade-offs. A discrete choice experiment (DCE) was undertaken to examine the AI-related characteristics radiation oncology professionals think are most important for adoption in radiation therapy treatment planning.

METHODS

Radiation oncology professionals completed an online discrete choice experiment to express their preferences about AI systems for radiation therapy planning which were described by five attributes, each with 2-4 levels: accuracy, automation, exploratory ability, compatibility with other systems and impact on workload. The survey also included questions about attitudes to AI. Choices were modelled using mixed logit regression.

RESULTS

The survey was completed by 82 respondents. The results showed they preferred AI systems that offer the largest time saving, and that provide explanations of the AI reasoning (both in-depth and basic). They also favoured systems that provide improved contouring precision compared with manual systems. Respondents emphasised the importance of AI systems being cost-effective, while also recognising AI's impact on professional roles, responsibilities, and service delivery.

CONCLUSIONS

This study provides important information about radiation oncology professionals' priorities for AI in treatment planning. The findings from this study can be used to inform future research on economic evaluations and management perspectives of AI-driven technologies in radiation therapy.

Evaluating the Accuracy of ChatGPT in the Japanese Board-Certified Physiatrist Examination

Background Generative artificial intelligence (AI), such as Chat Generative Pre-trained Transformer (ChatGPT), has shown potential in various medical applications, including answering licensing examination questions. However, its performance in rehabilitation medicine remains underexplored. This study aimed to evaluate the accuracy of ChatGPT4o in answering questions from the Japanese Board-Certified Physiatrist Examination and assess its potential as an educational and clinical support tool. Methods This study assessed the performance of ChatGPT4o on questions from the 2021-2023 Japanese Board-Certified Physiatrist Examinations. Questions were categorized into text- and image-based types and correct response rates were calculated. Errors were classified into informational, logical, or statistical. Results ChatGPT4o achieved correct response rates of 79.1% in 2021, 80.0% in 2022, and 86.3% in 2023, with an overall accuracy of 81.8%. The AI performed better on text-based (83.0%) than on image-based (70.0%) questions. Most errors (92.8%) were related to information. Conclusions ChatGPT4o demonstrated high accuracy in the Japanese Board-Certified Physiatrist Examination, particularly for text-based questions, demonstrating its potential as an educational tool. However, limitations in image interpretation and specialized topics indicate the need for further improvements for clinical application.

Vaccine development using artificial intelligence and machine learning: A review

The COVID-19 pandemic has underscored the critical importance of effective vaccines, yet their development is a challenging and demanding process. It requires identifying antigens that elicit protective immunity, selecting adjuvants that enhance immunogenicity, and designing delivery systems that ensure optimal efficacy. Artificial intelligence (AI) can facilitate this process by using machine learning methods to analyze large and diverse datasets, suggest novel vaccine candidates, and refine their design and predict their performance. This review explores how AI can be applied to various aspects of vaccine development, such as predicting immune response from protein sequences, discovering adjuvants, optimizing vaccine doses, modeling vaccine supply chains, and predicting protein structures. We also address the challenges and ethical issues that emerge from the use of AI in vaccine development, such as data privacy, algorithmic bias, and health data sensitivity. We contend that AI has immense potential to accelerate vaccine development and respond to future pandemics, but it also requires careful attention to the quality and validity of the data and methods used.

Artificial intelligence in nanotechnology for treatment of diseases

Nano-based drug delivery systems (DDSs) have demonstrated the ability to address challenges posed by therapeutic agents, enhancing drug efficiency and reducing side effects. Various nanoparticles (NPs) are utilised as DDSs with unique characteristics, leading to diverse applications across different diseases. However, the complexity, cost and time-consuming nature of laboratory processes, the large volume of data, and the challenges in data analysis have prompted the integration of artificial intelligence (AI) tools. AI has been employed in designing, characterising and manufacturing drug delivery nanosystems, as well as in predicting treatment efficiency. AI's potential to personalise drug delivery based on individual patient factors, optimise formulation design and predict drug properties has been highlighted. By leveraging AI and large datasets, developing safe and effective DDSs can be accelerated, ultimately improving patient outcomes and advancing pharmaceutical sciences. This review article investigates the role of AI in the development of nano-DDSs, with a focus on their therapeutic applications. The use of AI in DDSs has the potential to revolutionise treatment optimisation and improve patient care.

Artificial Intelligence in Human Reproduction

The use of artificial intelligence (AI) in human reproduction is a rapidly evolving field with both exciting possibilities and ethical considerations. This technology has the potential to improve success rates and reduce the emotional and financial burden of infertility. However, it also raises ethical and privacy concerns. This paper presents an overview of the current and potential applications of AI in human reproduction. It explores the use of AI in various aspects of reproductive medicine, including fertility tracking, assisted reproductive technologies, management of pregnancy complications, and laboratory automation. In addition, we discuss the need for robust ethical frameworks and regulations to ensure the responsible and equitable use of AI in reproductive medicine.

Addressing ethical issues in healthcare artificial intelligence using a lifecycle-informed process

Objectives

Artificial intelligence (AI) proceeds through an iterative and evaluative process of development, use, and refinement which may be characterized as a lifecycle. Within this context, stakeholders can vary in their interests and perceptions of the ethical issues associated with this rapidly evolving technology in ways that can fail to identify and avert adverse outcomes. Identifying issues throughout the AI lifecycle in a systematic manner can facilitate better-informed ethical deliberation.

Materials and Methods

We analyzed existing lifecycles from within the current literature for ethical issues of AI in healthcare to identify themes, which we relied upon to create a lifecycle that consolidates these themes into a more comprehensive lifecycle. We then considered the potential benefits and harms of AI through this lifecycle to identify ethical questions that can arise at each step and to identify where conflicts and errors could arise in ethical analysis. We illustrated the approach in 3 case studies that highlight how different ethical dilemmas arise at different points in the lifecycle.

Results Discussion and Conclusion

Through case studies, we show how a systematic lifecycle-informed approach to the ethical analysis of AI enables mapping of the effects of AI onto different steps to guide deliberations on benefits and harms. The lifecycle-informed approach has broad applicability to different stakeholders and can facilitate communication on ethical issues for patients, healthcare professionals, research participants, and other stakeholders.

Approaches to Reach Trustworthy Patient Education: A Narrative Review

BACKGROUND

Patient education is a cornerstone of modern healthcare. Health literacy improves health-related quality of life and health outcomes of patients, enhanced by effective patient education. Inadequate competency of patient education in healthcare providers triggered this review to summarize common approaches and recent advancements.

METHODS

This narrative review summarizes common approaches and recent advancements in patient education with their relations to health literacy, their strengths, limitations, and practical issues.

RESULTS

This review highlighted the multifaceted approaches to patient education, emphasizing the importance of tailoring methods to meet the diverse needs of patients. By integrating various strategies, including intrapersonal, interpersonal, and societal/community-level interventions, healthcare providers can create a more comprehensive educational experience that addresses the complexities of patient needs, meanwhile improving the health literacy of patients. With the rise of digital media and artificial intelligence, there is an increasing need for innovative educational resources that can effectively reach and engage patients. Ongoing research and collaboration among healthcare professionals and policymakers will be essential to refine educational strategies and adapt to emerging challenges. It is essential to remain vigilant about potential conflicts of interest that may compromise the integrity of educational content.

CONCLUSION

Effective patient education empowers individuals and their contributions to a healthier society by fostering informed decision-making and encouraging proactive health management.

Artificial Intelligence-Driven Computational Approaches in the Development of Anticancer Drugs

The integration of AI has revolutionized cancer drug development, transforming the landscape of drug discovery through sophisticated computational techniques. AI-powered models and algorithms have enhanced computer-aided drug design (CADD), offering unprecedented precision in identifying potential anticancer compounds. Traditionally, cancer drug design has been a complex, resource-intensive process, but AI introduces new opportunities to accelerate discovery, reduce costs, and optimize efficiency. This manuscript delves into the transformative applications of AI-driven methodologies in predicting and developing anticancer drugs, critically evaluating their potential to reshape the future of cancer therapeutics while addressing their challenges and limitations.

Navigating ethical considerations in the use of artificial intelligence for patient care: A systematic review

AIM

To explore the ethical considerations and challenges faced by nursing professionals in integrating artificial intelligence (AI) into patient care.

BACKGROUND

AI's integration into nursing practice enhances clinical decision-making and operational efficiency but raises ethical concerns regarding privacy, accountability, informed consent, and the preservation of human-centered care.

METHODS

A systematic review was conducted, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Thirteen studies were selected from databases including PubMed, Embase, IEEE Xplore, PsycINFO, and CINAHL. Thematic analysis identified key ethical themes related to AI use in nursing.

RESULTS

The review highlighted critical ethical challenges, such as data privacy and security, accountability for AI-driven decisions, transparency in AI decision-making, and maintaining the human touch in care. The findings underscore the importance of stakeholder engagement, continuous education for nurses, and robust governance frameworks to guide ethical AI implementation in nursing.

DISCUSSION

The results align with existing literature on AI's ethical complexities in healthcare. Addressing these challenges requires strengthening nursing competencies in AI, advocating for patient-centered AI design, and ensuring that AI integration upholds ethical standards.

CONCLUSION

Although AI offers significant benefits for nursing practice, it also introduces ethical challenges that must be carefully managed. Enhancing nursing education, promoting stakeholder engagement, and developing comprehensive policies are essential for ethically integrating AI into nursing.

IMPLICATIONS FOR NURSING

AI can improve clinical decision-making and efficiency, but nurses must actively preserve humanistic care aspects through ongoing education and involvement in AI governance.

IMPLICATIONS FOR HEALTH POLICY

Establish ethical frameworks and data protection policies tailored to AI in nursing. Support continuous professional development and allocate resources for the ethical integration of AI in healthcare.

Prospects for AI clinical summarization to reduce the burden of patient chart review

Effective summarization of unstructured patient data in electronic health records (EHRs) is crucial for accurate diagnosis and efficient patient care, yet clinicians often struggle with information overload and time constraints. This review dives into recent literature and case studies on both the significant impacts and outstanding issues of patient chart review on communications, diagnostics, and management. It also discusses recent efforts to integrate artificial intelligence (AI) into clinical summarization tasks, and its transformative impact on the clinician's potential, including but not limited to reductions of administrative burden and improved patient-centered care. Furthermore, it takes into account the numerous ethical challenges associated with integrating AI into clinical workflow, including biases, data privacy, and cybersecurity.

AI's pivotal impact on redefining stakeholder roles and their interactions in medical education and health care

Artificial Intelligence (AI) has the potential to revolutionize medical training, diagnostics, treatment planning, and healthcare delivery while also bringing challenges such as data privacy, the risk of technological overreliance, and the preservation of critical thinking. This manuscript explores the impact of AI and Machine Learning (ML) on healthcare interactions, focusing on faculty, students, clinicians, and patients. AI and ML's early inclusion in the medical curriculum will support student-centered learning; however, all stakeholders will require specialized training to bridge the gap between medical practice and technological innovation. This underscores the importance of education in the ethical and responsible use of AI and emphasizing collaboration to maximize its benefits. This manuscript calls for a re-evaluation of interpersonal relationships within healthcare to improve the overall quality of care and safeguard the welfare of all stakeholders by leveraging AI's strengths and managing its risks.

Raising awareness of potential biases in medical machine learning: Experience from a Datathon

Objective

To challenge clinicians and informaticians to learn about potential sources of bias in medical machine learning models through investigation of data and predictions from an open-source severity of illness score.

Methods

Over a two-day period (total elapsed time approximately 28 hours), we conducted a datathon that challenged interdisciplinary teams to investigate potential sources of bias in the Global Open Source Severity of Illness Score. Teams were invited to develop hypotheses, to use tools of their choosing to identify potential sources of bias, and to provide a final report.

Results

Five teams participated, three of which included both informaticians and clinicians. Most (4/5) used Python for analyses, the remaining team used R. Common analysis themes included relationship of the GOSSIS-1 prediction score with demographics and care related variables; relationships between demographics and outcomes; calibration and factors related to the context of care; and the impact of missingness. Representativeness of the population, differences in calibration and model performance among groups, and differences in performance across hospital settings were identified as possible sources of bias.

Discussion

Datathons are a promising approach for challenging developers and users to explore questions relating to unrecognized biases in medical machine learning algorithms.

Role of Artificial Intelligence and Machine Learning in Facial Aesthetic Surgery: A Systematic Review

Objective: To analyze the quality of artificial intelligence (AI) and machine learning (ML) tools developed for facial aesthetic surgery. Data Sources: Medline, Embase, CINAHL, Central, Scopus, and Web of Science databases were searched in February 2024. Study Selection: All original research in adults undergoing facial aesthetic surgery was included. Pilot reports, case reports, case series (n < 5), conference proceedings, letters (except research letters and brief reports), and editorials were excluded. Main Outcomes and Measures: Facial aesthetic surgery procedures employing AI and ML tools to measure improvements in diagnostic accuracy, predictive outcomes, precision patient counseling, and the scope of facial aesthetic surgery procedures where these tools have been implemented. Results: Out of 494 initial studies, 66 were included in the qualitative analysis. Of these, 42 (63.6%) were of "good" quality, 20 (30.3%) were of "fair" quality, and 4 (6.1%) were of "poor" quality. Conclusion: AI improves diagnostic accuracy, predictive capabilities, patient counseling, and facial aesthetic surgery treatment planning.