Better understanding fall risk: AI-based computer vision for contextual gait assessment.

Contemporary research to better understand free-living fall risk assessment in Parkinson's disease (PD) often relies on the use of wearable inertial-based measurement units (IMUs) to quantify useful temporal and spatial gait characteristics (e.g., step time, step length). Although use of IMUs is useful to understand some intrinsic PD fall-risk factors, their use alone is limited as they do not provide information on extrinsic factors (e.g., obstacles). Here, we update on the use of ergonomic wearable video-based eye-tracking glasses coupled with AI-based computer vision methodologies to provide information efficiently and ethically in free-living home-based environments to better understand IMU-based data in a small group of people with PD. The use of video and AI within PD research can be seen as an evolutionary step to improve methods to understand fall risk more comprehensively.

Trametinib Sensitivity is Defined by a Myeloid Differentiation Profile in Acute Myeloid Leukemia.

BACKGROUND AND OBJECTIVE: Acute myelogenous leukemia (AML) is a common blood cancer marked by heterogeneity in disease and diverse genetic abnormalities. Additional therapies are needed as the 5-year survival remains below 30%. Trametinib is a mitogen-activated extracellular signal-regulated kinase (MEK) inhibitor that is widely used in solid tumors and also in tumors with activating RAS mutations. A subset of patients with AML carry activating RAS mutations; however, a small-scale clinical trial with trametinib showed little efficacy. Here, we sought to identify transcriptomic determinants of trametinib sensitivity in AML. METHODS: We tested the activity of trametinib against a panel of tumor cells from patients with AML ex vivo and compared this with RNA sequencing (RNA-Seq) data from untreated blasts from the same patient samples. We then used a correlation analysis between gene expression and trametinib sensitivity to identify potential biomarkers predictive of drug response. RESULTS: We found that a subset of AML tumor cells were sensitive to trametinib ex vivo, only a fraction of which (3/10) carried RAS mutations. On the basis of our RNA-Seq analysis we found that markers of trametinib sensitivity are associated with a myeloid differentiation profile that includes high expression of CD14 and CLEC7A (Dectin-1), similar to the gene expression profile of monocytes. Further characterization confirmed that trametinib-sensitive samples display features of monocytic differentiation with high CD14 surface expression and were enriched for the M4 subtypes of the FAB classification. CONCLUSIONS: Our study identifies additional molecular markers that can be used with molecular features including RAS status to identify patients with AML that may benefit from trametinib treatment.

Assessing the Limitations of Relief-Based Algorithms in Detecting Higher-Order Interactions.

Background: The investigation of epistasis becomes increasingly complex as more loci are considered due to the exponential expansion of possible interactions. Consequently, selecting key features that influence epistatic interactions is crucial for effective downstream analyses. Recognizing this challenge, this study investigates the efficiency of Relief-Based Algorithms (RBAs) in detecting higher-order epistatic interactions, which may be critical for understanding the genetic architecture of complex traits. RBAs are uniquely non-exhaustive, eliminating the need to construct features for every possible interaction and thus improving computational tractability. Motivated by previous research indicating that some RBAs rank predictive features involved in higher-order epistasis as highly negative, we explore the utility of absolute value ranking of RBA feature weights as an alternative method to capture complex interactions. We evaluate ReliefF, MultiSURF, and MultiSURFstar on simulated genetic datasets that model various patterns of genotype-phenotype associations, including 2-way to 5-way genetic interactions, and compare their performance to two control methods: a random shuffle and mutual information. Results: Our findings indicate that while RBAs effectively identify lower-order (2 to 3-way) interactions, their capability to detect higher-order interactions is significantly limited, primarily by large feature count but also by signal noise. Specifically, we observe that RBAs are successful in detecting fully penetrant 4-way XOR interactions using an absolute value ranking approach, but this is restricted to datasets with a minimal number of total features. Conclusions: These results highlight the inherent limitations of current RBAs and underscore the need for enhanced detection capabilities for the investigation of epistasis, particularly in datasets with large feature counts and complex higher-order interactions.

Expanded Combined Loading Injury Criterion for the Human Lumbar Spine Under Dynamic Compression.

Contemporary injury tolerance of the lumbar spine for under-body blast references axial compression and bending moments in a limited range. Since injuries often occur in a wider range of flexion and extension with increased moment contribution, this study expands a previously proposed combined loading injury criterion for the lumbar spine. Fifteen cadaveric lumbar spine failure tests with greater magnitudes of eccentric loading were incorporated into an existing injury criterion to augment its applicability and a combined loading injury risk model was proposed by means of survival analysis. A loglogistic distribution was the most representative of injury risk, resulting in optimized critical values of Fr,crit = 6011 N, and My,crit = 904 Nm for the proposed combined loading metric. The 50% probability of injury resulted in a combined loading metric value of 1, with 0.59 and 1.7 corresponding to 5 and 95% injury risk, respectively. The inclusion of eccentric loaded specimens resulted in an increased contribution of the bending moment relative to the previously investigated flexion/extension range (previous My,crit = 1155 Nm), with the contribution of the resultant sagittal force reduced by nearly 200 N (previous Fr,crit = 5824 N). The new critical values reflect an expanded flexion/extension range of applicability of the previously proposed combined loading injury criterion for the human lumbar spine during dynamic compression.

The evolution of computational research in a data-centric world.

Computational data-centric research techniques play a prevalent and multi-disciplinary role in life science research. In the past, scientists in wet labs generated the data, and computational researchers focused on creating tools for the analysis of those data. Computational researchers are now becoming more independent and taking leadership roles within biomedical projects, leveraging the increased availability of public data. We are now able to generate vast amounts of data, and the challenge has shifted from data generation to data analysis. Here we discuss the pitfalls, challenges, and opportunities facing the field of data-centric research in biology. We discuss the evolving perception of computational data-driven research and its rise as an independent domain in biomedical research while also addressing the significant collaborative opportunities that arise from integrating computational research with experimental and translational biology. Additionally, we discuss the future of data-centric research and its applications across various areas of the biomedical field.

Competence over confidence: uncovering lower self-efficacy for women residents during central venous catheterization training.

BACKGROUND: While women make up over 50% of students enrolled in medical school, disparities in self-efficacy of medical skills between men and women have been observed throughout medical education. This difference is significant because low self-efficacy can impact learning, achievement, and performance, and thus create gender-confidence gaps. Simulation-based training (SBT) employs assessments of self-efficacy, however, the Dunning-Kruger effect in self-assessment posits that trainees often struggle to recognize their skill level. Additionally, the impact of gender on self-efficacy during SBT has not been as widely studied. The objective of this study was to identify if the gender-confidence gap and the Dunning-Kruger effect exist in SBT for central venous catheterization (CVC) on the dynamic haptic robotic trainer (DHRT) utilizing comparisons of self-efficacy and performance. METHODS: 173 surgical residents (Nwomen=61, Nmen=112) underwent training on the DHRT system over two years. Before and after using the DHRT, residents completed a 14-item Central Line Self-Efficacy survey (CLSE). During training on the DHRT, CVC performance metrics of the number of insertion attempts, backwall puncture, and successful venipuncture were also collected. The pre- and post-CLSE, DHRT performance and their relationship were compared between men and women. RESULTS: General estimating equation results indicated that women residents were significantly more likely to report lower self-efficacy for 9 of the 14 CLSE items (p < .0035). Mann-Whitney U and Fisher's exact tests showed there were no performance differences between men and women for successfully accessing the vein on the DHRT. Regression models relating performance and self-efficacy found no correlation for either gender. CONCLUSIONS: These results indicate that despite receiving the same SBT and performing at the same level, the gender-confidence gap exists in CVC SBT, and the Dunning-Kruger effect may also be evident.

Using Video Technology and AI within Parkinson's Disease Free-Living Fall Risk Assessment.

Falls are a major concern for people with Parkinson's disease (PwPD), but accurately assessing real-world fall risk beyond the clinic is challenging. Contemporary technologies could enable the capture of objective and high-resolution data to better inform fall risk through measurement of everyday factors (e.g., obstacles) that contribute to falls. Wearable inertial measurement units (IMUs) capture objective high-resolution walking/gait data in all environments but are limited by not providing absolute clarity on contextual information (i.e., obstacles) that could greatly influence how gait is interpreted. Video-based data could compliment IMU-based data for a comprehensive free-living fall risk assessment. The objective of this study was twofold. First, pilot work was conducted to propose a novel artificial intelligence (AI) algorithm for use with wearable video-based eye-tracking glasses to compliment IMU gait data in order to better inform free-living fall risk in PwPD. The suggested approach (based on a fine-tuned You Only Look Once version 8 (YOLOv8) object detection algorithm) can accurately detect and contextualize objects (mAP50 = 0.81) in the environment while also providing insights into where the PwPD is looking, which could better inform fall risk. Second, we investigated the perceptions of PwPD via a focus group discussion regarding the adoption of video technologies and AI during their everyday lives to better inform their own fall risk. This second aspect of the study is important as, traditionally, there may be clinical and patient apprehension due to ethical and privacy concerns on the use of wearable cameras to capture real-world video. Thematic content analysis was used to analyse transcripts and develop core themes and categories. Here, PwPD agreed on ergonomically designed wearable video-based glasses as an optimal mode of video data capture, ensuring discreteness and negating any public stigma on the use of research-style equipment. PwPD also emphasized the need for control in AI-assisted data processing to uphold privacy, which could overcome concerns with the adoption of video to better inform IMU-based gait and free-living fall risk. Contemporary technologies (wearable video glasses and AI) can provide a holistic approach to fall risk that PwPD recognise as helpful and safe to use.

Hybrid III Manikin Lumbar Spine Loading Under Vertical Impact.

INTRODUCTION: Clinical investigations have attributed lumbar spine injuries in combat to the vertical vector. Injury prevention strategies include the determination of spine biomechanics under this vector and developing/evaluating physical devices for use in live fire and evaluation-type tests to enhance Warfighter safety. While biological models have replicated theater injuries in the laboratory, matched-pair tests with physical devices are needed for standardized tests. The objective of this investigation is to determine the responses of the widely used Hybrid III lumbar spine under the vertical impact-loading vector. MATERIALS AND METHODS: Our custom vertical accelerator device was used in the study. The manikin spinal column was mounted between the inferior and superior six-axis load cells, and the impact was delivered to the inferior end. The first group of tests consisted of matched-pair repeatability tests, second group consisted of adding matched-pair tests to this first group to determine the response characteristics, and the third group consisted of repeating the earlier two groups by changing the effective torso mass from 12 to 16 kg. Peak axial, shear, and resultant forces at the two ends of the spine were obtained. RESULTS: The first group of 12 repeatability tests showed that the mean difference in the axial force between two tests at the same velocity across the entire range of inputs was <3% at both ends. In the second group, at the inferior end, the axial and shear forces ranged from 4.9-25.2 kN to 0.7-3.0 kN. Shear forces accounted for a mean of 11 ± 6% and 12 ± 4% of axial forces at the two ends. In the third group of tests with increased torso mass, repeatability tests showed that the mean difference in the axial force between the two tests at the same velocity across the entire range of inputs was <2% at both ends. At the inferior end, the axial and shear forces ranged from 5.7-28.7 kN to 0.6-3.4 kN. Shear forces accounted for a mean of 11 ± 8% and 9 ± 3% of axial forces across all tests at the inferior and superior ends. Other data including plots of axial and shear forces at the superior and inferior ends across tested velocities of the spine are given in the paper. CONCLUSIONS: The Hybrid III lumbar spine when subjected to vertical impact simulating underbody blast levels showed that the impact is transmitted via the axial loading mechanism. This finding paralleled the results of axial force predominance over shear forces and axial loading injuries to human spines. Axial forces increased with increasing velocity suggesting the possibility of developing injury assessment risk curves, i.e., the manikin spine does not saturate, and its response is not a step function. It is possible to associate probability values for different force magnitudes. A similar conclusion was found to be true for both magnitudes of added effective torso mass at the superior end of the manikin spinal column. Additional matched-pair tests are needed to develop injury criteria for the Hybrid III male and female lumbar spines.

Medications that Regulate Gastrointestinal Transit Influence Inpatient Blood Glucose.

Objective: A multitude of factors affect a hospitalized individual's blood glucose (BG), making BG difficult to predict and manage. Beyond medications well established to alter BG, such as beta-blockers, there are likely many medications with undiscovered effects on BG variability. Identification of these medications and the strength and timing of these relationships has potential to improve glycemic management and patient safety. Materials and Methods: EHR data from 103,871 inpatient encounters over 8 years within a large, urban health system was used to extract over 500 medications, laboratory measurements, and clinical predictors of BG. Feature selection was performed using an optimized Lasso model with repeated 5-fold cross-validation on the 80% training set, followed by a linear mixed regression model to evaluate statistical significance. Significant medication predictors were then evaluated for novelty against a comprehensive adverse drug event database. Results: We found 29 statistically significant features associated with BG; 24 were medications including 10 medications not previously documented to alter BG. The remaining five factors were Black/African American race, history of type 2 diabetes mellitus, prior BG (mean and last) and creatinine. Discussion: The unexpected medications, including several agents involved in gastrointestinal motility, found to affect BG were supported by available studies. This study may bring to light medications to use with caution in individuals with hyper- or hypoglycemia. Further investigation of these potential candidates is needed to enhance clinical utility of these findings. Conclusion: This study uniquely identifies medications involved in gastrointestinal transit to be predictors of BG that may not well established and recognized in clinical practice.

Evaluating the effects of comprehensive simulation on central venous catheterization training: a comparative observational study.

BACKGROUND: Simulation-based training (SBT) is vital to complex medical procedures such as ultrasound guided central venous catheterization (US-IJCVC), where the experience level of the physician impacts the likelihood of incurring complications. The Dynamic Haptic Robotic Trainer (DHRT) was developed to train residents in CVC as an improvement over manikin trainers, however, the DHRT and manikin trainer both only provide training on one specific portion of CVC, needle insertion. As such, CVC SBT would benefit from more comprehensive training. An extended version of the DHRT was created, the DHRT + , to provide hands-on training and automated feedback on additional steps of CVC. The DHRT + includes a full CVC medical kit, a false vein channel, and a personalized, reactive interface. When used together, the DHRT and DHRT + systems provide comprehensive training on needle insertion and catheter placement for CVC. This study evaluates the impact of the DHRT + on resident self-efficacy and CVC skill gains as compared to training on the DHRT alone. METHODS: Forty-seven medical residents completed training on the DHRT and 59 residents received comprehensive training on the DHRT and the DHRT + . Each resident filled out a central line self-efficacy (CLSE) survey before and after undergoing training on the simulators. After simulation training, each resident did one full CVC on a manikin while being observed by an expert rater and graded on a US-IJCVC checklist. RESULTS: For two items on the US-IJCVC checklist, "verbalizing consent" and "aspirating blood through the catheter", the DHRT + group performed significantly better than the DHRT only group. Both training groups showed significant improvements in self-efficacy from before to after training. However, type of training received was a significant predictor for CLSE items "using the proper equipment in the proper order", and "securing the catheter with suture and applying dressing" with the comprehensive training group that received additional training on the DHRT + showing higher post training self-efficacy. CONCLUSIONS: The integration of comprehensive training into SBT has the potential to improve US-IJCVC education for both learning gains and self-efficacy.

Advancing LGBTQ+ inclusion in STEM education and AI research.

The authors emphasize diversity, equity, and inclusion in STEM education and artificial intelligence (AI) research, focusing on LGBTQ+ representation. They discuss the challenges faced by queer scientists, educational resources, the implementation of National AI Campus, and the notion of intersectionality. The authors hope to ensure supportive and respectful engagement across all communities.

Identifying diversity of patient anatomy through automated image analysis of clinical ultrasounds.

PURPOSE: Central venous catheterization (CVC) carries inherent risks which can be mitigated through the use of appropriate ultrasound-guidance during needle insertion. This study aims to comprehensively understand patient anatomy as it is visualized during CVC by employing a semi-automated image analysis method to track the internal jugular vein and carotid artery throughout recorded ultrasound videos. METHODS: The ultrasound visualization of 50 CVC procedures were recorded at Penn State Health Milton S. Hershey Medical Center. The developed algorithm was used to detect the vessel edges, calculating metrics such as area, position, and eccentricity. RESULTS: Results show typical anatomical variations of the vein and artery, with the artery being more circular and posterior to the vein in most cases. Notably, two cases revealed atypical artery positions, emphasizing the algorithm's precision in detecting anomalies. Additionally, dynamic vessel properties were analyzed, with the vein compressing on average to 13.4% of its original size and the artery expanding by 13.2%. CONCLUSION: This study provides valuable insights which can be used to increase the accuracy of training simulations, thus enhancing medical education and procedural expertise. Furthermore, the novel approach of employing automated data analysis techniques to clinical recordings showcases the potential for continual assessment of patient anatomy, which could be useful in future advancements.

KRAGEN: a knowledge graph-enhanced RAG framework for biomedical problem solving using large language models.

MOTIVATION: Answering and solving complex problems using a large language model (LLM) given a certain domain such as biomedicine is a challenging task that requires both factual consistency and logic, and LLMs often suffer from some major limitations, such as hallucinating false or irrelevant information, or being influenced by noisy data. These issues can compromise the trustworthiness, accuracy, and compliance of LLM-generated text and insights. RESULTS: Knowledge Retrieval Augmented Generation ENgine (KRAGEN) is a new tool that combines knowledge graphs, Retrieval Augmented Generation (RAG), and advanced prompting techniques to solve complex problems with natural language. KRAGEN converts knowledge graphs into a vector database and uses RAG to retrieve relevant facts from it. KRAGEN uses advanced prompting techniques: namely graph-of-thoughts (GoT), to dynamically break down a complex problem into smaller subproblems, and proceeds to solve each subproblem by using the relevant knowledge through the RAG framework, which limits the hallucinations, and finally, consolidates the subproblems and provides a solution. KRAGEN's graph visualization allows the user to interact with and evaluate the quality of the solution's GoT structure and logic. AVAILABILITY AND IMPLEMENTATION: KRAGEN is deployed by running its custom Docker containers. KRAGEN is available as open-source from GitHub at: https://github.com/EpistasisLab/KRAGEN.

Comparison of the Trapping Efficacy of Locally Modified Gravid Aedes Trap and Autocidal Gravid Ovitrap for the Monitoring and Surveillance of Aedes aegypti Mosquitoes in Tanzania.

The study assessed the trapping efficacy of locally modified (1) Gravid Aedes Trap (GAT) lined with insecticide-treated net (ITN) as a killing agent and (2) Autocidal Gravid Ovitrap (AGO) with sticky board in the semi-field system (SFS) and field setting. Fully balanced Latin square experiments were conducted to compare GAT lined with ITN vs. AGO, both with either yeast or grass infusion. Biogent-Sentinel (BGS) with BG-Lure and no CO2 was used as a standard trap for Aedes mosquitoes. In the SFS, GAT outperformed AGO in collecting both nulliparous (65% vs. 49%, OR = 2.22, [95% CI: 1.89-2.60], p < 0.001) and gravid mosquitoes (73% vs. 64%, OR = 1.67, [95% CI: 1.41-1.97], p < 0.001). Similar differences were observed in the field. Yeast and grass infusion did not significantly differ in trapping gravid mosquitoes (OR = 0.91, [95% CI: 0.77-1.07], p = 0.250). The use of ITN improved mosquito recapture from 11% to 70% in the SFS. The same trend was observed in the field. Yeast was chosen for further evaluation in the optimized GAT due to its convenience and bifenthrin net for its resistance management properties. Mosquito density was collected when using 4× GATs relative to BGS-captured gravid mosquitoes 64 vs. 58 (IRR = 0.82, [95% CI: 0.35-1.95], p = 0.658) and showed no density dependence. Deployment of multiple yeast-baited GAT lined with bifenthrin net is cost-effective (single GAT < $8) compared to other traps such as BGS ($160).

Enhancing fall risk assessment: instrumenting vision with deep learning during walks.

BACKGROUND: Falls are common in a range of clinical cohorts, where routine risk assessment often comprises subjective visual observation only. Typically, observational assessment involves evaluation of an individual's gait during scripted walking protocols within a lab to identify deficits that potentially increase fall risk, but subtle deficits may not be (readily) observable. Therefore, objective approaches (e.g., inertial measurement units, IMUs) are useful for quantifying high resolution gait characteristics, enabling more informed fall risk assessment by capturing subtle deficits. However, IMU-based gait instrumentation alone is limited, failing to consider participant behaviour and details within the environment (e.g., obstacles). Video-based eye-tracking glasses may provide additional insight to fall risk, clarifying how people traverse environments based on head and eye movements. Recording head and eye movements can provide insights into how the allocation of visual attention to environmental stimuli influences successful navigation around obstacles. Yet, manual review of video data to evaluate head and eye movements is time-consuming and subjective. An automated approach is needed but none currently exists. This paper proposes a deep learning-based object detection algorithm (VARFA) to instrument vision and video data during walks, complementing instrumented gait. METHOD: The approach automatically labels video data captured in a gait lab to assess visual attention and details of the environment. The proposed algorithm uses a YoloV8 model trained on with a novel lab-based dataset. RESULTS: VARFA achieved excellent evaluation metrics (0.93 mAP50), identifying, and localizing static objects (e.g., obstacles in the walking path) with an average accuracy of 93%. Similarly, a U-NET based track/path segmentation model achieved good metrics (IoU 0.82), suggesting that the predicted tracks (i.e., walking paths) align closely with the actual track, with an overlap of 82%. Notably, both models achieved these metrics while processing at real-time speeds, demonstrating efficiency and effectiveness for pragmatic applications. CONCLUSION: The instrumented approach improves the efficiency and accuracy of fall risk assessment by evaluating the visual allocation of attention (i.e., information about when and where a person is attending) during navigation, improving the breadth of instrumentation in this area. Use of VARFA to instrument vision could be used to better inform fall risk assessment by providing behaviour and context data to complement instrumented e.g., IMU data during gait tasks. That may have notable (e.g., personalized) rehabilitation implications across a wide range of clinical cohorts where poor gait and increased fall risk are common.

Radiomics-Based Analysis of Intestinal Ultrasound Images for Inflammatory Bowel Disease: A Feasibility Study.

Background: The increasing adoption of intestinal ultrasound (IUS) for monitoring inflammatory bowel diseases (IBD) by IBD providers has uncovered new challenges regarding standardized image interpretation and limitations as a research tool. Artificial intelligence approaches can help address these challenges. We aim to determine the feasibility of radiomic analysis of IUS images and to determine if a radiomics-based classification model can accurately differentiate between normal and abnormal IUS images. We will also compare the radiomic-based model's performance to a convolutional neural network (CNN)-based classification model to understand which method is more effective for extracting meaningful information from IUS images. Methods: Retrospectively analyzing IUS images obtained during routine outpatient visits, we developed and tested radiomic-based and CNN-based models to distinguish between normal and abnormal images, with abnormal images defined as bowel wall thickness > 3 mm or bowel hyperemia with modified Limberg score ≥ 1 (both are surrogate markers for inflammation). Model performances were measured by area under the receiver operator curve (AUC). Results: For this feasibility study, 125 images (33% abnormal) were analyzed. A radiomic-based model using XG boost yielded the best classifier model with average test AUC 0.98%, 93.8% sensitivity, 93.8% specificity, and 93.7% accuracy. The CNN-based classification model yielded an average testing AUC of 0.75. Conclusions: Radiomic analysis of IUS images is feasible, and a radiomic-based classification model could accurately differentiate abnormal from normal images. Our findings establish methods to facilitate future radiomic-based IUS studies that can help standardize image interpretation and expand IUS research capabilities.

The neuron as a direct data-driven controller.

In the quest to model neuronal function amid gaps in physiological data, a promising strategy is to develop a normative theory that interprets neuronal physiology as optimizing a computational objective. This study extends current normative models, which primarily optimize prediction, by conceptualizing neurons as optimal feedback controllers. We posit that neurons, especially those beyond early sensory areas, steer their environment toward a specific desired state through their output. This environment comprises both synaptically interlinked neurons and external motor sensory feedback loops, enabling neurons to evaluate the effectiveness of their control via synaptic feedback. To model neurons as biologically feasible controllers which implicitly identify loop dynamics, infer latent states, and optimize control we utilize the contemporary direct data-driven control (DD-DC) framework. Our DD-DC neuron model explains various neurophysiological phenomena: the shift from potentiation to depression in spike-timing-dependent plasticity with its asymmetry, the duration and adaptive nature of feedforward and feedback neuronal filters, the imprecision in spike generation under constant stimulation, and the characteristic operational variability and noise in the brain. Our model presents a significant departure from the traditional, feedforward, instant-response McCulloch-Pitts-Rosenblatt neuron, offering a modern, biologically informed fundamental unit for constructing neural networks.

Using GPT-4 to write a scientific review article: a pilot evaluation study.

GPT-4, as the most advanced version of OpenAI's large language models, has attracted widespread attention, rapidly becoming an indispensable AI tool across various areas. This includes its exploration by scientists for diverse applications. Our study focused on assessing GPT-4's capabilities in generating text, tables, and diagrams for biomedical review papers. We also assessed the consistency in text generation by GPT-4, along with potential plagiarism issues when employing this model for the composition of scientific review papers. Based on the results, we suggest the development of enhanced functionalities in ChatGPT, aiming to meet the needs of the scientific community more effectively. This includes enhancements in uploaded document processing for reference materials, a deeper grasp of intricate biomedical concepts, more precise and efficient information distillation for table generation, and a further refined model specifically tailored for scientific diagram creation.

PFERM: A Fair Empirical Risk Minimization Approach with Prior Knowledge.

Fairness is crucial in machine learning to prevent bias based on sensitive attributes in classifier predictions. However, the pursuit of strict fairness often sacrifices accuracy, particularly when significant prevalence disparities exist among groups, making classifiers less practical. For example, Alzheimer's disease (AD) is more prevalent in women than men, making equal treatment inequitable for females. Accounting for prevalence ratios among groups is essential for fair decision-making. In this paper, we introduce prior knowledge for fairness, which incorporates prevalence ratio information into the fairness constraint within the Empirical Risk Minimization (ERM) framework. We develop the Prior-knowledge-guided Fair ERM (PFERM) framework, aiming to minimize expected risk within a specified function class while adhering to a prior-knowledge-guided fairness constraint. This approach strikes a flexible balance between accuracy and fairness. Empirical results confirm its effectiveness in preserving fairness without compromising accuracy.