Transparency In Research Research Articles

Structuring data analysis projects in the Open Science era with Kerblam!

Background Structuring data analysis projects, that is, defining the layout of files and folders needed to analyze data using existing tools and novel code, largely follows personal preferences. Open Science calls for more accessible, transparent and understandable research. We believe that Open Science principles can be applied to the way data analysis projects are structured. Methods We examine the structure of several data analysis project templates by analyzing project template repositories present in GitHub. Through visualization of the resulting consensus structure, we draw observations regarding how the ecosystem of project structures is shaped, and what salient characteristics it has. Results Project templates show little overlap, but many distinct practices can be highlighted. We take them into account with the wider Open Science philosophy to draw a few fundamental Design Principles to guide researchers when designing a project space. We present Kerblam!, a project management tool that can work with such a project structure to expedite data handling, execute workflow managers, and share the resulting workflow and analysis outputs with others. Conclusions We hope that, by following these principles and using Kerblam!, the landscape of data analysis projects can become more transparent, understandable, and ultimately useful to the wider community.

Standardized Outcomes for Randomized Controlled Trials Targeting Early Interventions in Patients With Moderate-to-Severe Traumatic Brain Injury: Protocol for the Development of a Core Outcome Set.

: With more than 60 million new cases around the world each year, traumatic brain injury (TBI) causes substantial mortality and morbidity. Managing TBI is a major human, social, and economic concern. In the last 20 years, there has been an increase in clinical trials in neurocritical care, leading mostly to negative results. The evaluation of neurological outcomes, predominantly as primary outcomes, using clinical scales (Glasgow Outcome Scale) has limitations that could explain these results. Moreover, patient-centered outcomes are seldom reported despite their recognized clinical relevance. : The aim of this project is to establish a core outcome set (COS) for patients with moderate-to-severe TBI in randomized control trials in neurocritical care research. This study will follow five distinct steps: (1) systematic review to identify outcomes that have been reported in trials; (2) semistructured interviews with patients and their families to identify their priorities after TBI and explore potential patient-centered outcomes; (3) health care stakeholder focus groups with clinicians, researchers, and policy makers to describe potential outcomes; (4) an eDelphi survey with stakeholder groups to make a list of previously identified core outcomes; and (5) a consensus workshop to establish a COS for moderate-to-severe TBI clinical trials. : The systematic review was published in August 2024. Regarding Step 2, 30 semistructured interviews of patients and relatives were performed from July 2021 to December 2023, and analyses were completed in October 2024. Step 3 is currently under development, and Step 4 is planned for the end of 2025. Step 5 is expected to occur during fall/winter 2026. Conclusions: Establishing a COS, to be consistently measured and reported in TBI trials in neurocritical care will ensure rigorous reporting, avoid bias, and improve the integrity, transparency, and usability of clinical research. The French context of the study is the main limitation, but we are seeking international collaboration on the project. The results of each step of the project will be disseminated through abstracts, publications, and patient associations. Establishing a COS, to be consistently measured and reported in TBI trials in neurocritical care will ensure rigorous reporting, avoid bias, and improve the integrity, transparency, and usability of clinical research. The French context of the study is the main limitation, but we are seeking international collaboration on the project. The results of each step of the project will be disseminated through abstracts, publications, and patient associations. DERR1-10.2196/54525.

Enforcing absolute transparency in research: Absolute transparency as an eventual and an essential prerequisite of contemporary twentyfirst century research

Enforcing absolute transparency in research: Absolute transparency as an eventual and an essential prerequisite of contemporary twentyfirst century research

A novel framework for increasing research transparency: Exploring the connection between diversity and innovation.

A split sample/dual method research protocol is demonstrated to increase transparency while reducing the probability of false discovery. We apply the protocol to examine whether diversity in ownership teams increases or decreases the likelihood of a firm reporting a novel innovation using data from the 2018 United States Census Bureau's Annual Business Survey. Transparency is increased in three ways: 1) all specification testing and identifying potentially productive models is done in an exploratory subsample that 2) preserves the validity of hypothesis test statistics from de novo estimation in the holdout confirmatory sample with 3) all findings publicly documented in an earlier registered report and in this journal publication. Bayesian estimation procedures that leverage information from the exploratory stage included in the confirmatory stage estimation replace traditional frequentist null hypothesis significance testing. In addition to increasing statistical power by using information from the full sample, Bayesian methods directly estimate a probability distribution for the magnitude of an effect, allowing much richer inference. Estimated magnitudes of diversity along academic discipline, race, ethnicity, and foreign-born status dimensions are positively associated with innovation. A maximally diverse ownership team on these dimensions would be roughly six times more likely to report new-to-market innovation than a homophilic team.

Improving Reproducibility in AI Research: Four Mechanisms Adopted by JAIR

Background: Lately, the reproducibility of scientific results has become an increasing worry in the scientific community. Several studies show that artificial intelligence research is not spared from reproducibility issues. Objectives: As a pioneer in open and transparent research published on the Internet, the Journal of Artificial Intelligence Research (JAIR) seeks to promote good research practices and close the feedback loop between the original researchers and those reproducing their research. Methods: Four different mechanisms will be adopted immediately by JAIR. These are: 1) reproducibility checklists, 2) structured abstracts, 3) reproducibility badges and 4) reproducibility reports. Results: All authors submitting articles to JAIR fill out a reproducibility checklist and are encouraged to use structured abstracts. Articles that fulfill certain criteria will receive reproducibility badges, and reproducibility reports can be submitted by anyone for any article published in JAIR. Conclusions: We believe that adopting the four mechanisms outlined in this paper will improve the reproducibility of research published in JAIR and thus make a contribution to addressing the broader reproducibility issue in artificial intelligence. We hope that JAIR’s reproducibility initiative will inspire similar efforts at other top-tier journals.

Ensuring Open Research Collaboration with the Open Science Framework: The Experience of the SPSTL SB RAS

Openness and accessibility of scientific research are becoming increasingly important aspects of promoting open science and reproducible research. Open scientific practices provide many benefits to both researchers and science as a whole. The Open Science Framework enables the application of open and collaborative research practices in the scientific community – from creating a data management plan, pre-registering one’s research and using version control to exchanging data and other research materials or publishing a preprint for rapid dissemination. The objective of the study was to analyze the OSF platform in order to identify the main functions and capabilities for open scientific communication and the use of this tool within the framework of the scientific project “Development of a Model for the Functioning of a Scientific Library in the Information Ecosystem of Open Science” of the State Public Scientific Technological Library of the Siberian Branch of the Russian Academy of Sciences to comply with the principles of open science. The author analyzed the OSF tools for creating projects, pre-registrations, posting preprints, as well as the possibilities of integration with various third-party services, such as Dropbox, GitHub, Google Drive, etc. Examples of projects and components are provided. The process of creating a project, adding components, metadata, creating preregistration is described. The results show that the user-friendly interface and variety of functions allow the OSF to be integrated into a researcher’s workflow and provide an opportunity to document and archive research projects, materials and data. They also facilitate the exchange of materials and data as well as promote research transparency. Russian researchers working in a wide range of scientific fields can use this open science tool to ensure open scientific collaboration and promote open and reproducible scientific research.

Exploring the Intersection of Hegemonic Masculinity, Sexuality, and Addiction in Men: A Qualitative Study.

Background/Objectives: In our society, as well as in many other parts of the world, sexuality is shaped through gender-differentiated socialization. This process compels individuals to align their desires, behaviors, emotions, and thoughts with the expectations of normative sexuality, especially hegemonic heterosexuality. The primary objective of this current research was to examine the influence of hegemonic masculinity on the sexuality of men struggling with addiction. Method: This study employed a qualitative approach, specifically using conventional content analysis. To ensure research quality and transparency, the study adhered to the Standards for Reporting Qualitative Research (SRQR) and the Consolidated Criteria for Reporting Qualitative Studies (COREQ). Fourteen participants from a therapeutic community for addiction treatment in Spain were selected through purposive sampling. Data collection involved semi-structured interviews, supplemented by participant observation. The sample size was determined based on the principle of data saturation. Results: The findings reveal that factors such as gender-differentiated socialization, particularly the values associated with hegemonic masculinity (e.g., the pressure to maintain constant sexual availability and sexual initiative), alongside excessive engagement with prostitution and pornography, are closely linked to substance abuse, the development of addictions, and the emergence of sexual dysfunctions in men. The study also highlights the role of challenges related to oppression rooted in heteronormativity-understood as the imposition of rigid norms governing sexual orientation and behavior-in shaping sexual problems and in the origin and maintenance of addictions in men. Conclusions: It is essential to implement an intervention strategy that promotes egalitarian masculinities within the treatment of men with addiction issues, emphasizing a biopsychosocial approach to sexuality that integrates a gender perspective. Additionally, it is crucial to incorporate gender-sensitive interventions into rehabilitation programs for both men and women, ensuring a comprehensive understanding of each group's specific needs and fostering the development of healthy, equitable relationships in the context of addiction treatment.

Releasing synthetic data from the Avon Longitudinal Study of Parents and Children (ALSPAC): Guidelines and applied examples

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort. Since its inception in the early 1990s, the study has collected over thirty years of data on approximately 15,000 mothers, their partners, and their offspring, resulting in over 100,000 phenotype variables to date. Maintaining data security and participant anonymity and confidentiality are key principles for the study, meaning that data access is restricted to bona fide researchers who must apply to use data, which is then shared on a project-by-project basis. Despite these legitimate reasons for restricting data access, this does run counter to emerging best scientific practices encouraging making data openly available to facilitate transparent and reproducible research. Given the rich nature of the resource, ALSPAC data are also a valuable educational tool, used for teaching a variety of methods, such as longitudinal modelling and approaches to modelling missing data. To support these efforts and to overcome the restrictions in place with the study’s data sharing policy, we discuss methods for generating and making openly available synthesised ALSPAC datasets; these synthesised datasets are modelled on the original ALSPAC data, thus maintaining variable distributions and relations among variables (including missing data) as closely as possible, while at the same time preserving participant anonymity and confidentiality. We discuss how ALSPAC data can be synthesised using the ‘synthpop’ package in the R statistical programming language (including an applied example), present a list of guidelines for researchers wishing to release such synthesised ALSPAC data to follow, and demonstrate how this approach can be used as an educational tool to illustrate longitudinal modelling methods.

Adapting FAIR Evaluation to Photon and Neutron Facilities

The FAIR principles have become essential in establishing transparent and trustworthy research practices. However the FAIR principles are guidelines indicating the features expected for data to be FAIR, and do not stipulate evaluation criteria. Consequently,there has been a proliferation of approaches to FAIR evaluation to substantiate claims for FAIR-ness, establish baselines, and measure improvement. Some approaches are focussed on FAIR-ness of individual datasets, others of repositories; some require extensive human evaluation, others use automation. However, within some scientific domains, data generation and management follow well-defined processes that result in datasets annotated with metadata and archived in repositories. Existing FAIR evaluation methods consider in less detail the contribution of the processes used in collecting and analysing data and how these enable FAIR-ness. We describe the evaluation approach adopted for FAIR self-evaluation for Photon and Neutron Research Infrastructures (PaN RI’s). We review selected examples of existing FAIR evaluation frameworks designed to enable assessment at different levels, and outline four dimensions that characterise them. As no existing framework met our specific need to focus on FAIR workflows and processes inPaN RIs, it was necessary to select, combine, and adapt existing frameworks, and we developed an approach drawing heavily on the original FAIR principles, the RDA FAIR Data Maturity Model, and FAIRsFAIR’s CoreTrustSeal+FAIRenabling framework. Post-evaluation feedback from ExPaNDS partners indicated that they found the FAIR self-evaluation a useful and valuable exercise forunderstanding current levels of FAIR-ness at their facilities and for articulating what implementations they have in progress or planned to support FAIR in future.

Treatment-induced ripple effect: a systematic review exploring the abscopal phenomenon in Glioblastoma multiforme.

This systematic review aimed to collate and synthesize the available literature on the abscopal effect in Glioblastoma multiforme (GBM) neoplasms, focusing on the reported biochemical mechanisms driving the abscopal effect. A systematic search was conducted in PubMed, Cochrane Database of Systematic Reviews, and Epistemonikos from inception to May 1, 2023. Studies exploring the abscopal effect in GBM were included. The Clinical Relevance Assessment of Animal Preclinical research (RAA) tool was used to assess methodological quality of preclinical studies. Data on preclinical models, biochemical mechanisms, and outcomes were extracted and synthesized systrmatically. Out of a total of 7 studies, five preclinical studies met the inclusion criteria. The studies utilized various in vivo mouse models, including bilateral tumor models and immunohumanized mice. Key biochemical mechanisms identified included immunogenic cell death, danger-associated molecular pattern release, macrophage activation, and enhanced T cell responses. Combinatorial approaches involving oncolytic virotherapy, nanoparticle-based treatments, radiation therapy, and immune checkpoint inhibitors showed promise in inducing abscopal effects. Significant tumor growth inhibition and improved survival were reported in treated animals. However, the RAA analysis highlighted concerns regarding research transparency and internal validity across studies. This systematic review highlighted the potential of the abscopal effect in GBM, demonstrating its ability to enhance anti-tumor immune responses both locally and systemically. The synergistic effects of combinatorial approaches showed promise for improving outcomes. However, the low methodological quality of existing studies underscored the need for more rigorous preclinical research. Future studies should focus on improving research transparency, exploring the abscopal effect in other primary CNS neoplasms, and translating these findings into clinical trials to assess safety and efficacy in humans.

Prevalence of transparency and reproducibility-related research practices in music psychology (2017–2022)

Social sciences are navigating an unprecedented period of introspection about the credibility and utility of disciplinary practices. Reform initiatives have emphasized the benefits of various transparency and reproducibility-related research practices; however, the adoption of these across music psychology is unknown. To estimate the prevalence, a manual examination of a random sample of 239 articles out of 1,192 articles published in five music psychology journals between 2017 and 2022 was carried out. About half of the articles were publicly available (112/239) and 39% share some of the research materials, but 5% share raw data and 1% analysis scripts. Pre-registrations were not observed in the sample. Most articles (82%) included a funding disclosure statement, but conflict of interest statements were less common (27%). Replication studies were rare (3%). Additional searches of replication studies were conducted beyond the sample. These analyses did not find substantially more replication studies in music psychology. In general, the results suggest that transparency and reproducibility-related research practices were far from routine in music psychology. The findings establish a baseline that can be used to assess future progress toward increasing the credibility and openness of music psychology research.

Challenges of open data in aquatic sciences: issues faced by data users and data providers

Free use and redistribution of data (i.e., Open Data) increases the reproducibility, transparency, and pace of aquatic sciences research. However, barriers to both data users and data providers may limit the adoption of Open Data practices. Here, we describe common Open Data challenges faced by data users and data providers within the aquatic sciences community (i.e., oceanography, limnology, hydrology, and others). These challenges were synthesized from literature, authors’ experiences, and a broad survey of 174 data users and data providers across academia, government agencies, industry, and other sectors. Through this work, we identified seven main challenges: 1) metadata shortcomings, 2) variable data quality and reusability, 3) open data inaccessibility, 4) lack of standardization, 5) authorship and acknowledgement issues 6) lack of funding, and 7) unequal barriers around the globe. Our key recommendation is to improve resources to advance Open Data practices. This includes dedicated funds for capacity building, hiring and maintaining of skilled personnel, and robust digital infrastructures for preparation, storage, and long-term maintenance of Open Data. Further, to incentivize data sharing we reinforce the need for standardized best practices to handle data acknowledgement and citations for both data users and data providers. We also highlight and discuss regional disparities in resources and research practices within a global perspective.

Putting Legal Research on Trial. A Reflection of Challenges Investigating Terrorism Prosecutions in England and Wales

In 2024, governments continue to grapple with legal and political ramifications posed by repatriating foreign terrorist fighters (FTFs). Criminal justice systems and governments taking different approaches to returning individuals suspected of committing acts of terrorism overseas has become an area of international contestation. Inconsistency in repatriating FTFs has highlighted differences between legal systems across Europe, Asia, and North America. In England and Wales, growing interest in the repatriation and prosecution of terrorists has outlined barriers to effective research on terrorism trials and legal decision-making processes. Dismantling barriers to researching the courtroom is central to promoting legal transparency and accountability, understanding how appropriate legal responses to terrorism are articulated and enforced, and drawing attention to the potential impact of implicit biases in the prosecution of terrorists in the criminal justice system. The study of terrorism trials and sentencing is an interdisciplinary endeavour which allows for the fields of law, terrorism studies, and criminology to investigate intersections between criminality, justice, and national security. However, relatively little work has explored these junctures. This article outlines methodological, practical, and conceptual barriers which hamper research on terrorism trials in England and Wales and suggests new methods which can promote transparent interdisciplinary legal research.

The Critical Role of Introspective Analysis in Modern Research Practices

Introspective analysis is a crucial element in modern research practices, as it enhances the rigour, relevance, and ethical integrity of research practices across various disciplines. By examining the interplay between self-reflection and methodological approaches, introspective analysis fosters deeper understanding and critical thinking, encouraging researchers to confront biases, assumptions, and ethical dilemmas inherent in their work. Incorporating introspective practices into research design, data interpretation, and dissemination of findings can lead to a culture of accountability and transparency in research, promoting collaboration, and ultimately improving the quality and impact of scholarly outputs. This discourse underscores the necessity of integrating introspective analysis into the research process, positioning it as a vital component for advancing knowledge and addressing the challenges faced by the global research community. By engaging in introspective analysis, researchers can unearth underlying motivations that may affect their interpretation of data, selection of methodologies, and interactions with research participants. This reflective practice can lead to the refinement of research questions, ensuring they are not only relevant but also ethically sound. By engaging in self-reflection, researchers can confront their ethical responsibilities toward participants, stakeholders, and the broader community, fostering a culture of accountability and ensuring that researchers remain aware of their impact and obligations. In conclusion, the critical role of introspective analysis cannot be overstated as the research landscape continues to evolve. By embracing self-reflection as an essential component of research, researchers can contribute to more innovative and comprehensive solutions to complex problems.

Access to and use of Data for better Healthcare: A Plea for a cooperative data and Research Infrastructure of Statutory and Private Health Insurers and the Network University Medicine (NUM)

With the Network of University Medicine (NUM) and the Medical Informatics Initiative (MII), the BMBF is funding two pioneering, structure-building research measures that are now being merged. The data integration centers (DIZ) of the MII are to be consolidated in the NUM. The aim is to establish a standardized research infrastructure within which the existing data from the clinical routine care of the 36 German university hospitals, from clinical cohorts and clinical-epidemiological studies can be used for various research questions upon request and via coordinated processes. The legal basis for this was the MII's "Informed Broad Consent", which had been agreed upon with ethics committees and data protection authorities and implemented in all NUM locations, with a so-called "health insurance module" that allows the collection and linking of routine medical data from statutory health insurance funds (GKV) and private health insurers (PKV) as a category of care-related data (VeDa). Linking this routine data with data from hospital information systems offers particularly high potential, as no single data source provides a complete picture of medical care and the two data sources complement each other optimally. The aim now is to integrate this routine data into the NUM's secure, transparent and participatory research infrastructure in a strategic partnership with statutory health insurance funds and private health insurance companies. This promotes Germany in its role as a research location and makes a decisive contribution to improving the quality and safety of healthcare in Germany in an evidence-based manner.

Evaluating the Reproducibility and Verifiability of Nutrition Research: A Case Study of Studies Assessing the Relationship Between Potatoes and Colorectal Cancer.

The credibility of nutritional research is dependent on the rigor with which studies are conducted and the ability for independent assessment to be performed. Despite the importance of these, more work is needed in the field of nutrition to buttress the trustworthiness of nutrition research. To develop and apply a process for evaluating the rigor, reproducibility, and verifiability of nutritional research, using the relationship between potato consumption and Colorectal cancer (CRC) as a case study. We updated existing systematic reviews to include studies on potatoes and CRC, assessing their design, execution, and reporting quality. We attempted to reproduce and verify the results of included studies by requesting raw data from authors and following statistical methods as described in the publications. Rigor was evaluated using four different tools: ROBINS-E, STROBE-Nut, Newcastle-Ottawa scale, and additional criteria related to transparency. Eighteen studies were included, none of which publicly share data. We managed to access data for only two studies, successfully reproducing and verifying the results for one. The majority of studies exhibited a high risk of bias, with significant limitations in reporting quality and methodological rigor. Research on the relationship between potato consumption and CRC risk is insufficiently reproducible and verifiable, undermining the trustworthiness of its findings. This study highlights the need for improving transparency, data sharing, and methodological rigor in nutritional research. Our approach provides a model for assessing the credibility of research in other areas of nutrition.

CALIFRAME: a proposed method of calibrating reporting guidelines with FAIR principles to foster reproducibility of AI research in medicine.

Procedural and reporting guidelines are crucial in framing scientific practices and communications among researchers and the broader community. These guidelines aim to ensure transparency, reproducibility, and reliability in scientific research. Despite several methodological frameworks proposed by various initiatives to foster reproducibility, challenges such as data leakage and reproducibility remain prevalent. Recent studies have highlighted the transformative potential of incorporating the FAIR (Findable, Accessible, Interoperable, and Reusable) principles into workflows, particularly in contexts like software and machine learning model development, to promote open science. This study aims to introduce a comprehensive framework, designed to calibrate existing reporting guidelines against the FAIR principles. The goal is to enhance reproducibility and promote open science by integrating these principles into the scientific reporting process. We employed the "Best fit" framework synthesis approach which involves systematically reviewing and synthesizing existing frameworks and guidelines to identify best practices and gaps. We then proposed a series of defined workflows to align reporting guidelines with FAIR principles. A use case was developed to demonstrate the practical application of the framework. The integration of FAIR principles with established reporting guidelines through the framework effectively bridges the gap between FAIR metrics and traditional reporting standards. The framework provides a structured approach to enhance the findability, accessibility, interoperability, and reusability of scientific data and outputs. The use case demonstrated the practical benefits of the framework, showing improved data management and reporting practices. The framework addresses critical challenges in scientific research, such as data leakage and reproducibility issues. By embedding FAIR principles into reporting guidelines, the framework ensures that scientific outputs are more transparent, reliable, and reusable. This integration not only benefits researchers by improving data management practices but also enhances the overall scientific process by promoting open science and collaboration. The proposed framework successfully combines FAIR principles with reporting guidelines, offering a robust solution to enhance reproducibility and open science. This framework can be applied across various contexts, including software and machine learning model development stages, to foster a more transparent and collaborative scientific environment.

Data sharing in circadian rhythm and mental health research: current status, challenges, recommendations and future directions.

Data sharing is a cornerstone of modern scientific research, playing a critical role in fostering greater collaboration, enhancing reproducibility, transparency and efficiency of scientific discoveries, and integrating diverse data sources....

The reporting of disproportionality analysis in pharmacovigilance: spotlight on the READUS-PV guideline.

Disproportionality analyses are the most-commonly used study design used in the post-marketing phase to detect suspected adverse drug reactions in individual case safety reports. Recent years have witnessed an exponential increase in published articles on disproportionality analyses, thanks to publicly accessible databases. Unfortunately, this trend was accompanied by concerns on lack of transparency and misinterpretation of results, both generating unjustified alarm and diluting true signals into overwhelming noise. The READUS-PV guideline for reporting disproportionality analysis was developed to tackle this emerging issue. In this perspective article, we describe the rationale behind the development of the READUS-PV guideline, the first collaborative initiative to harmonize the reporting of disproportionality analyses. The adoption of the checklists will assist researchers, regulators, and reviewers in the reporting, assessment, and publication of disproportionality analyses. Acknowledging the challenges ahead of effective implementation, we advocate for a global endorsement by Pharmacology Journals. A wide dissemination of the READUS-PV guideline is crucial to foster transparency and reproducibility of pharmacovigilance research, supporting an effective exploitation of disproportionality analysis among other irreplaceable post-marketing research tools to ensure drug safety.

Detecting and Mitigating the Clever Hans Effect in Medical Imaging: A Scoping Review.

The Clever Hans effect occurs when machine learning models rely on spurious correlations instead of clinically relevant features and poses significant challenges to the development of reliable artificial intelligence (AI) systems in medical imaging. This scoping review provides an overview of methods for identifying and addressing the Clever Hans effect in medical imaging AI algorithms. A total of 173 papers published between 2010 and 2024 were reviewed, and 37 articles were selected for detailed analysis, with classification into two categories: detection and mitigation approaches. Detection methods include model-centric, data-centric, and uncertainty and bias-based approaches, while mitigation strategies encompass data manipulation techniques, feature disentanglement and suppression, and domain knowledge-driven approaches. Despite the progress in detecting and mitigating the Clever Hans effect, the majority of current machine learning studies in medical imaging do not report or test for shortcut learning, highlighting the need for more rigorous validation and transparency in AI research. Future research should focus on creating standardized benchmarks, developing automated detection tools, and exploring the integration of detection and mitigation strategies to comprehensively address shortcut learning. Establishing community-driven best practices and leveraging interdisciplinary collaboration will be crucial for ensuring more reliable, generalizable, and equitable AI systems in healthcare.