Development Of Model Research Articles

Computational Legal Studies Comes of Age

Computational analysis techniques are transforming empirical legal scholarship. Two paradigms have emerged: law-as-code, which seeks to represent legal rules in a logical, executable format; and law-as-data, which leverages quantitative analysis of legal texts to reveal patterns and insights. This article surveys these approaches, emphasizing recent developments in large language models and generative artificial intelligence (AI). Law-as-code systems have enabled applications from tax preparation software to smart contracts, but realizing the vision of fully computational law has proven challenging. Law-as-data techniques like natural language processing and machine learning have charted the semantic relationship between courts and illuminated changes in judicial culture. Generative models showcase AI's explosive progress, with impressive feats like passing the U.S. bar example, but they also highlight limitations like factual inaccuracy and interpretability issues. Hybrid approaches integrating computational law, data science, and AI offer a promising research direction. As these tools spread, legal scholars can analyze more legal data than ever before, but they must remain cognizant of challenges like biased or low-quality data and linguistic/cultural limitations. Used judiciously alongside traditional methods, computational analysis has the potential to revolutionize empirical legal studies.

Systematic Review of Prognosis Models in Predicting Tooth Loss in Periodontitis.

This study reviews and appraises the methodological and reporting quality of prediction models for tooth loss in periodontitis patients, including the use of regression and machine learning models. Studies involving prediction modeling for tooth loss in periodontitis patients were screened. A search was performed in MEDLINE via PubMed, Embase, and CENTRAL up to 12 February 2022, with citation chasing. Studies exploring model development or external validation studies for models assessing tooth loss in periodontitis patients for clinical use at any time point, with all prediction horizons in English, were considered. Studies were excluded if models were not developed for use in periodontitis patients, were not developed or validated on any data set, predicted outcomes other than tooth loss, or were prognostic factor studies. The CHARMS checklist was used for data extraction, TRIPOD to assess reporting quality, and PROBAST to assess the risk of bias. In total, 4,661 records were screened, and 45 studies were included. Only 26 studies reported any kind of performance measure. The median C-statistic reported was 0.671 (range, 0.57-0.97). All studies were at a high risk of bias due to inappropriate handling of missing data (96%), inappropriate evaluation of model performance (92%), and lack of accounting for model overfitting in evaluating model performance (68%). Many models predicting tooth loss in periodontitis are available, but studies evaluating these models are at a high risk of bias. Model performance measures are likely to be overly optimistic and might not be replicated in clinical use. While this review is unable to recommend any model for clinical practice, it has collated the existing models and their model performance at external validation and their associated sample sizes, which would be helpful to identify promising models for future external validation studies.

FPM-WSI: Fourier ptychographic whole slide imaging via feature-domain backdiffraction

Fourier ptychographic microscopy (FPM) theoretically provides a solution to the trade-off between spatial resolution and field of view (FOV), and has promising prospects in digital pathology. However, block reconstruction and then stitching has become an unavoidable procedure for reconstruction of large FOV due to vignetting artifacts. This introduces digital stitching artifacts, as the existing image-domain optimization algorithms are highly sensitive to systematic errors. Such obstacles significantly impede the advancement and practical implementation of FPM, explaining why, despite a decade of development, FPM has not gained widespread recognition in the field of biomedicine. We report a feature-domain FPM (FD-FPM) based on the structure-aware forward model to realize stitching-free, full-FOV reconstruction. The loss function is uniquely formulated in the feature domain of images, which bypasses the troublesome vignetting effect and algorithmic vulnerability via feature-domain backdiffraction. Through massive simulations and experiments, we show that FD-FPM effectively eliminates vignetting artifacts for full-FOV reconstruction, and still achieves impressive reconstructions despite the presence of various systematic errors. We also found it has great potential in recovering the data with a lower spectrum overlapping rate, and in realizing digital refocusing without a prior defocus distance. With FD-FPM, we achieved full-color and high-throughput imaging (4.7 mm diameter FOV, 336 nm resolution in the blue channel) free of blocking-and-stitching procedures on a self-developed Fourier ptychographic microscopy whole slide imaging platform. The reported FD-FPM shows the value of FPM for various experimental circumstances, and offers physical insights useful for the developments of models for other computational imaging techniques. The reported platform demonstrates high-quality, high-speed imaging and low cost, and could find applications in many fields of biomedical research, as well as in clinical applications.

Abstract PO3-07-02: Radiomics Models for B-mode Breast Ultrasound and Strain Elastography to improve Breast Cancer Diagnosis (INSPiRED 005): An International, Multicenter Analysis

Abstract Background: Breast Elastography, a technique that quantifies tissue stiffness, has been evaluated to objectify and improve the performance of B-mode breast ultrasound. However, large prospective trials showed benefits in BI-RADS 4a breast masses only and a high operator dependency. Modern Artificial Intelligence techniques for automated image processing like radiomics, a technique where quantified features are extracted from images, may overcome these limitations. We aimed to develop and validate radiomics models based on B-mode and Strain Elastography (SE) images for patients with BI-RADS 3 or 4 breast masses and compare their performance to the respective human experts. Methods: This is a secondary analysis of an international, multicenter trial (NCT02638935), evaluating the performance of SE in women with BI-RADS 3 or 4 breast masses. Women were recruited at 12 institutions in 7 countries and underwent B-mode breast ultrasound as well as SE. B-mode images were saved and re-assed by three ultrasound readers ( >10 years of experience), resulting in three independent assessments and a final consensus assessment. SE was interpreted using the E-/B ratio. B-mode and strain images were manually segmented and quantitative radiomics features were extracted using pyradiomics. We used 10-fold cross-validation to build machine learning models (XGBoostTree, MARS) based on data of 11 of 12 study sites. The data of the 12th (largest) study site was used as external validation set. Performance metrics included sensitivity, specificity and area under the receiver operator characteristic curve (AUROC). Results: The study included a total of 1288 patients, 1206 with evaluable B-mode images and 1190 with evaluable Strain images. Mean age was 46.6 years (SD 16.02) and a total number of 29.0% (350 of 1206) and 28.9% (344 of 1190) breast masses were malignant in the B-mode and Strain cohort, respectively. Distribution of BI-RADS categories was 33.0%, 34.5%, 14.5%, and 18.0% for BI-RADS 3, 4a, 4b, and 4c, respectively. In the external validation set (n = 342), the B-mode radiomics model (XGBoostTree) achieved an AUROC of 0.86 (95% CI 0.82 to 0.90), with a sensitivity of 97.4% (95% CI 0.93 to 1.00, 113 of 116) and a specificity of 27.0% (95% CI 0.21 to 0.33, 61 of 226). The model showed equivalent performance compared to the three ultrasound readers (P = 0.133); see also Table 1. In the external validation set (n = 333), the Strain radiomics model (MARS) achieved an AUROC of 0.84 (95% CI 0.79 to 0.88), with a sensitivity of 100% (95% CI 47.0 to 58.0, 115 of 115) and a specificity of 25.5% (95% CI 0.22 to 0.34, 60 of 218). The model showed equivalent performance compared to the three ultrasound readers (P = 0.696) and performed significantly better compared to SE (P = 0.002); see also Table 1. Sensitivity of the strain model was descriptively higher (100% vs. 97.4%, see Table 1). Both models were well-calibrated. Conclusion: This is the largest development and validation study for radiomics models based on B-mode breast ultrasound and SE, to date. The radiomics models performed on par with human readers, with the strain radiomics model showing potential to identify initially missed carcinomas in BI-RADS 3 breast masses. Future implementation studies may evaluate the performance of these image analysis algorithms in clinical routine and their integration into the multi-modal breast cancer diagnostics process, including mammography and MRI. Table 1. Diagnostic performance metrics Citation Format: André Pfob, Tanja He, Lie Cai, Richard G. Barr, Volker Duda, Zaher Alwafai, Corinne Balleyguier, Dirk-André Clevert, Sarah Fastner, Christina Gomez, Manuela Goncalo, Ines Gruber, Markus Hahn, André Hennigs, Panagiotis Kapetas, Sheng-Chieh Lu, Juliane Nees, Ralf Ohlinger, Fabian Riedel, Matthieu Rutten, Benedikt Schaefgen, Anne Stieber, Riku Togawa, Mitsuhiro Tozaki, Sebastian Wojcinski, Cai Xu, Geraldine Rauch, Jörg Heil, Chris Sidey-Gibbons, Michael Golatta. Radiomics Models for B-mode Breast Ultrasound and Strain Elastography to improve Breast Cancer Diagnosis (INSPiRED 005): An International, Multicenter Analysis [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO3-07-02.

The Impact of Adversarial Attacks on Federated Learning: A Survey.

Federated learning (FL) has emerged as a powerful machine learning technique that enables the development of models from decentralized data sources. However, the decentralized nature of FL makes it vulnerable to adversarial attacks. In this survey, we provide a comprehensive overview of the impact of malicious attacks on FL by covering various aspects such as attack budget, visibility, and generalizability, among others. Previous surveys have primarily focused on the multiple types of attacks and defenses but failed to consider the impact of these attacks in terms of their budget, visibility, and generalizability. This survey aims to fill this gap by providing a comprehensive understanding of the attacks' effect by identifying FL attacks with low budgets, low visibility, and high impact. Additionally, we address the recent advancements in the field of adversarial defenses in FL and highlight the challenges in securing FL. The contribution of this survey is threefold: first, it provides a comprehensive and up-to-date overview of the current state of FL attacks and defenses. Second, it highlights the critical importance of considering the impact, budget, and visibility of FL attacks. Finally, we provide ten case studies and potential future directions towards improving the security and privacy of FL systems.

Design and Development of Mathematical and Thermal Load Modelling for Induction Heating Systems

Complex systems can be modelled and their performance can be anticipated with the help of current computers and software tools that are applicable in real-world situations. The IH system is capable of being modelled and put through analysis in two distinct domains. The modelling is compatible with the research and applications in their entirety, including the particular control circuits and converter systems under consideration. The use of electrical modelling makes it possible to analyse the features of the IH load and to examine the variations in the parameters in order to determine the impact of load variations. In order to gain an understanding of the load's flow and thermal distribution, thermal modelling is a crucial component of this study. For the purpose of analysing the performance of the coil in terms of flux and temperature from its comparable model, the COMSOL Multiphysics software that is utilised is utilised.

Development of Teaching Factory Learning Model in Industrial Automation in Vocational High Schools

Vocational High Schools, as schools that aim to prepare students for work in a particular field, need effectiveness in the learning process. Its effectiveness must follow the real conditions required by the industry. With the rapid devel-opment of industry, industry-based learning, often called "Teaching Factory," is one of the right role models of learning. The type of research to be carried out is development research. The purpose of this study is to determine (1) the devel-opment of the teaching factory learning model, (2) the application of the teach-ing factory learning model, (3) user response to the product, and (4) the effec-tiveness of using the developed product. The development model used in this study is the Plomp development model. The Plomp development model includes (1) the initial investigation phase, (2) the design phase, (3) the realiza-tion/construction phase, (4) the testing, evaluation, and revision phase, and (5) the field test/implementation phase. Validity by model experts about the teach-ing factory learning model gets an average score of 95.45 and is included in the very decent category. The material expert gave it a score of 93.41, and it be-longs to the very decent category. The media expert gave it a score of 95.84, which belongs to the decent category. The effectiveness test result shows a sig-nificant difference between the learning outcomes in the Industrial Automation Engineering Expertise Concentration subjects before and after applying the Teaching Factory learning model. This shows the positive influence and effectiveness of using the Teaching Factory learning model on the learning out-comes of the Industrial Automation Engineering Expertise Concentration in In-dustrial Automation Engineering Vocational School students.

Development of Electronic Student Worksheets (E-LKPD) Pendap Bengkulu Based on Discovery Learning as A Learning Media for Additive Material

This research aims to develop the electronic student worksheets (E-LKPD) Pendap Bengkulu based on Discovery Learning as a Learning Media for Additive Material in Science lesson content for class VIII SMP. This study used the 4D development model (Define, Design, Develop, and Disseminate), which was limited to the development stage with limited trials conducted at SMPN 03 Bengkulu City, with a sample of 33 students from class VIII.1. To collect data, several questionnaires were distributed to media experts, material experts, participants, and students. Data analysis was carried out using descriptive statistical analysis techniques using percentage analysis. The results of the research showed that the feasibility test of E-LKPD involving 3 material experts, 2 media experts, and 2 participants resulted in an 92.3% percentage of validation test results, and it can be said that E-LKPD Pendap Bengkulu, which was developed as a learning medium for additive material for junior high school students, was very feasible to use. Analysis of student readability of E-LKPD Pendap Bengkulu as a learning medium for additive material for junior high school students resulted in a percentage of 89.8% readability response, indicating that E-LKPD is very well applied to junior high school students in grade VIII.

Experimental solubility of FeCO3 in aqueous 3–20 wt% KCl at 25–80 °C

FeCO3 is a mineral, present in both industrial and natural systems. While the solubility of FeCO3 is determining for CO2 storage, and driving for corrosion, it is not well understood. For the development of rigorous modelling, there is a lack of adequate experimental data under relevant conditions to model carbonated systems containing iron.In this work, the solid-liquid equilibrium (SLE) of the ternary system FeCO3-KCl-H2O was measured at atmospheric pressure at temperatures from 25 to 80 °C. The FeCO3 solubility was studied in aqueous KCl solutions from 3 to 20 wt%. 73 new solubility data points for the systems FeCO3–KCl–H2O are presented together with 73 oxidation data points. Results showed that less than 5% of FeCO3 is oxidized in the electrolyte system. The equilibrium concentration of FeCO3 was obtained after 5–10 days with the fastest equilibrium kinetics obtained at the highest temperature, 80 °C. Temperature had a minor impact on the FeCO3 solubility in aqueous KCl solutions. However, the FeCO3 solubility seemed to slightly decrease with temperature. The solubility data was fitted to a first-order rate equation with molality as driving, to obtain kinetic insights. The dissolution rate decreases as a function of KCl concentrations. In some cases, it was 75 % lower than in NaCl. A rate constant of approximately 0.2 1/day was obtained for most experiments. The FeCO3 solubility and kinetic data obtained in this work will allow for a deeper understanding and better prediction of CO2 corrosion and mineralization, suitable for CO2 storage.

Predicting removal efficiency of organic pollutants by soil vapor extraction based on an optimized machine learning method

Soil vapor extraction (SVE) was a cost-effective technology for remediating volatile and semi-volatile organic contaminated soils. Many factors, including SVE parameters, soil properties, and contaminant characteristics, significantly influenced the remediation efficiency of SVE. The optimal conditions for organic contaminant removal efficiency were site-specific and varied among studies. Therefore, a generalized model was needed to predict the remediation efficiency in organic contaminated soils. This study employed machine learning to predict the removal efficiency of organic pollutants by SVE. The model's development was based on a trainset, and its predictive capabilities were evaluated using a testset. An XGBoost (XGB) model was derived from literature data (R2 = 0.9775). Time, pollutant type, and temperature were identified as the three most important features affecting SVE remediation efficiency. The accuracy (R2 = 0.9799) and universality of the model were enhanced through an optimization scheme. The developed XGB model demonstrated the ability to predict the removal efficiency of organic pollutants by considering all collected influential factors. The mechanism of multi-factor interaction on remediation efficiency was clarified. Overall, this study would contribute to evaluating the remediation potential of SVE for specific organic contaminated soils, aiding in maximizing the removal efficiency of organic pollutants under optimal conditions.

Exploiting Blockchain to Make AI Trustworthy: A Software Development Lifecycle View

Artificial intelligence (AI) is a very powerful technology and can be a potential disrupter and essential enabler. As AI expands into almost every aspect of our lives, people raise serious concerns about AI misbehaving and misuse. To address this concern, international organizations have put forward ethics guidelines for constructing trustworthy AI (TAI), including privacy, transparency, fairness, robustness, accountability, and so on. However, because of the black-box characteristics and complex models of AI systems, it is challenging to translate these guiding principles and aspirations into AI systems. Blockchain, an important decentralized technology, can provide the capabilities of transparency, traceability, immutability, and secure sharing and hence can be used to make AI trustworthy. In this paper, we survey studies on blockchain-based TAI (BTAI) from a software development lifecycle view. We classify the lifecycle of BTAI into four stages: Planning, data collection, model development, and system deployment/use. Particularly, we investigate and summarize the trustworthy issues that blockchain can achieve in the latter three stages, including (1) data transparency, privacy, and accountability; (2) model transparency, privacy, robustness, and fairness; and (3) robustness, privacy, transparency, and fairness of system deployment/use. Finally, we present essential open research issues and future work on developing BTAI systems.

Recurrent Neural Networks for Image Captioning: A Case Study with LSTM

This research investigates the viability of Long Short-Term Memory (LSTM) systems, a subtype of Recurrent Neural Networks (RNNs), for picture captioning. Leveraging the MS COCO dataset, the study compares the execution of LSTM-based RNNs with Vanilla RNN, Gated Recurrent Unit (GRU), consideration components, and transformer-based models. Experimental comes about to illustrate that the LSTM-based RNN shows competitive execution, accomplishing a BLEU-4 score of 0.72, a METEOR score of 0.68, and a CIDEr score of 2.1. The comparative investigation uncovers its prevalence over Vanilla RNN and GRU, highlighting its capability to capture long-range conditions inside successive picture information. Moreover, the study investigates the effect of consideration instruments and transformer designs, exhibiting their potential improvements in the context-aware caption era. The transformer-based show outflanks all other models, accomplishing a BLEU-4 score of 0.78, a METEOR score of 0.72, and a CIDEr score of 2.5. The findings give important bits of knowledge toward the creating scene of picture captioning strategy, which makes LSTM-based RNNs solid and productive approaches for capturing worldly groupings in visual substance. In achieving these, the study provides a framework for future developments in hybrid models and manufacturing processes that push boundaries of smart image perception and understanding

Enhancing Thai student-teacher problem-solving skills and academic achievement through a blended problem-based learning approach in online flipped classrooms

The study aimed to develop a learning model that enhances Thai student-teacher problem-solving skills (PSS) and academic achievement (AA) through a blended problem-based learning (PBL) approach in online flipped classrooms. Phase 1 consisted of the design of the Flipped PARSER (problem-attempt-research-solutions-evaluation-reflect) Model (FPM) through the study of documentation and research. Phase 2 involved using nine experts to assist with the model's development and evaluation using in-depth interviews and content analysis. Phase 3 involved the application and use of the FPM by 30 student-teachers, from which their AA, PSS, and satisfaction were evaluated against the control group of 31 participants. The results from the nine experts' input on the FPM design were significant. Instructor activities included selecting content, media, materials, and their design and development. Other components included learning activities, tools, lesson delivery, and evaluation. Also, PBL methods were again confirmed as an instrumental pedagogy in teaching PSS. When combined with online learning and flipped classrooms, the study's results were higher than those using traditional classroom methods. The study contributes to the literature by determining that online teaching models effectively teach PSS and raise AA scores.

Developing Biogas Digital Comic for Improving Renewable Energy Literacy

The use of renewable energy as a substitute for fossil energy must continue to be promoted to achieve sustainability. This study aimed to reveal the development of digital comics on the topic of biogas energy as a learning medium. The method used is research and development with the DDDE model (Decide, Design, Develop, and Evaluate). The results showed that biogas digital comics were very good to be used as learning media. This study recommends the use of comics as a medium of learning on other topics.
 Keywords: Biogas Digital Comic, Improving, Renewable Energy Literacy

Understanding vote transfers in two-round elections without resorting to declared data. The contribution of ecological inference, consolidated with factual information from a case study of the 2014 municipal elections in Montpellier

In electoral sociology, the analysis of vote transfers has traditionally depended on individual data obtained from surveys. Because such data suffer from a significant amount of declaration and memory bias, replacing them with the electoral statistics available down to polling station level may be advantageous. Recent developments in models of ecological inference allow us to use these aggregated data to establish estimates of vote transfers while minimising the risk of ecological error. Nonetheless, the reliability of ecological inference models for estimating vote transfers has thus far received little attention in the form of empirical evaluations. The purpose of the present article is to cast light on this blind spot by analysing a model for predicting electoral volatility in a two-round election, namely the municipal election held in Montpellier in 2014. What makes this approach original is its use of observed information – the proportion of non-voters in both rounds – first to compare this data with the estimates produced by the model and then to integrate it as a modelling parameter to measure its impact on estimated vote allocation. This analysis reveals that the initial model's results are relatively reliable regarding the known parameter, although they slightly overestimate its amplitude and underestimate its variability. The model that integrates information regarding the proportion of consistent non-voters yields estimates close to those obtained using the “raw” model. In terms of interpretative capacity, the value added by integrating this additional information is, therefore, slight. However, integrating the information does make it possible to establish narrower density intervals, reducing the uncertainty associated with the interpretation of the other parameters, particularly proportions associated with candidates who received few votes in the first round.

Detecting and resolving feature envy through automated machine learning and move method refactoring

Efficiently identifying and resolving code smells enhances software project quality. This paper presents a novel solution, utilizing automated machine learning (AutoML) techniques, to detect code smells and apply move method refactoring. By evaluating code metrics before and after refactoring, we assessed its impact on coupling, complexity, and cohesion. Key contributions of this research include a unique dataset for code smell classification and the development of models using AutoGluon for optimal performance. Furthermore, the study identifies the top 20 influential features in classifying feature envy, a well-known code smell, stemming from excessive reliance on external classes. We also explored how move method refactoring addresses feature envy, revealing reduced coupling and complexity, and improved cohesion, ultimately enhancing code quality. In summary, this research offers an empirical, data-driven approach, integrating AutoML and move method refactoring to optimize software project quality. Insights gained shed light on the benefits of refactoring on code quality and the significance of specific features in detecting feature envy. Future research can expand to explore additional refactoring techniques and a broader range of code metrics, advancing software engineering practices and standards.

Development of Nusselt number and friction factor models for rough rod bundles based on wall functions

The fuel assembly in the reactor undergo surface roughening due to fouling deposits and material corrosion, which significantly impact the pressure drop and convective heat transfer performance of the fuel assembly. In this study, new models were developed based on the wall velocity and temperature distribution functions. The Nusselt number and friction factor are obtained by integrating these functions over the flow cross-section. The findings demonstrate the accuracy of the method in predicting the friction factor and Nusselt number for both rough tube and smooth rod bundles. The models developed in this study can be utilized to predict the friction factor and Nusselt number in rod bundles with different relative roughness under turbulent flow states, considering square and hexagonal arrangements with P/D<1.5. The present study reveals that the roughness promotes convective heat transfer to a slightly less extent as the pressure drop.

Precise photoionisation treatment and hydrodynamic effects in atmospheric modelling of warm and hot Neptunes

Context. Observational breakthroughs in the field of exoplanets in the last decade have motivated the development of numerous theoretical models, such as those describing atmospheres and mass loss, which is believed to be one of the main drivers of planetary evolution. Aims. We outline for which types of close-in planets in the Neptune-mass range the accurate treatment of photoionisation effects is most relevant, particularly with respect to atmospheric mass loss and the parameters relevant for interpreting observations, such as temperature and ion fraction. Methods. We developed the Cloudy e Hydro Ancora INsieme (CHAIN) model, which combines our 1D hydrodynamic upper atmosphere model with the non-local thermodynamical equilibrium (NLTE) photoionisation and radiative transfer code Cloudy accounting for ionisation, dissociation, detailed atomic level populations, and chemical reactions for all chemical elements up to zinc. The hydro-dynamic code is responsible for describing the outflow, while Cloudy solves the photoionisation and heating of planetary atmospheres. We applied CHAIN to model the upper atmospheres of a range of Neptune-like planets with masses between 1 and 50 M⊕, also varying the orbital parameters. Results. For the majority of warm and hot Neptunes, we find slower and denser outflows, with lower ion fractions, compared to the predictions of the hydrodynamic model alone. Furthermore, we find significantly different temperature profiles between CHAIN and the hydrodynamic model alone, though the peak values are similar for similar atmospheric compositions. The mass-loss rates predicted by CHAIN are higher for hot strongly irradiated planets and lower for more moderate planets. All differences between the two models are strongly correlated with the amount of high-energy irradiation. Finally, we find that the hydrodynamic effects significantly impact ionisation and heating. Conclusions. The impact of the precise photoionisation treatment provided by Cloudy strongly depends on the system parameters. This suggests that some of the simplifications typically employed in hydrodynamic modelling might lead to systematic errors when studying planetary atmospheres, even at a population-wide level.

Development of a Comprehensive Dynamic Model for Giant Magnetostrictive Transducers Considering Bias Magnetic Field

Development of a Comprehensive Dynamic Model for Giant Magnetostrictive Transducers Considering Bias Magnetic Field

Considering the Secondary Use of Clinical and Educational Data to Facilitate the Development of Artificial Intelligence Models.

Medical training programs and health care systems collect ever-increasing amounts of educational and clinical data. These data are collected with the primary purpose of supporting either trainee learning or patient care. Well-established principles guide the secondary use of these data for program evaluation and quality improvement initiatives. More recently, however, these clinical and educational data are also increasingly being used to train artificial intelligence (AI) models. The implications of this relatively unique secondary use of data have not been well explored. These models can support the development of sophisticated AI products that can be commercialized. While these products have the potential to support and improve the educational system, there are challenges related to validity, patient and learner consent, and biased or discriminatory outputs. The authors consider the implications of developing AI models and products using educational and clinical data from learners, discuss the uses of these products within medical education, and outline considerations that should guide the appropriate use of data for this purpose. These issues are further explored by examining how they have been navigated in an educational collaborative.