Ethical Applications of Artificial Intelligence: Evidence From Health Research on Veterans

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

BACKGROUND

Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners.

OBJECTIVE

In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research.

METHODS

Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy.

RESULTS

From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI's application in the realm of medicine.

CONCLUSIONS

The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

The Role of Artificial Intelligence Model Documentation in Translational Science: Scoping Review

BACKGROUND

Despite the touted potential of artificial intelligence (AI) and machine learning (ML) to revolutionize health care, clinical decision support tools, herein referred to as medical modeling software (MMS), have yet to realize the anticipated benefits. One proposed obstacle is the acknowledged gaps in AI translation. These gaps stem partly from the fragmentation of processes and resources to support MMS transparent documentation. Consequently, the absence of transparent reporting hinders the provision of evidence to support the implementation of MMS in clinical practice, thereby serving as a substantial barrier to the successful translation of software from research settings to clinical practice.

OBJECTIVE

This study aimed to scope the current landscape of AI- and ML-based MMS documentation practices and elucidate the function of documentation in facilitating the translation of ethical and explainable MMS into clinical workflows.

METHODS

A scoping review was conducted in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. PubMed was searched using Medical Subject Headings key concepts of AI, ML, ethical considerations, and explainability to identify publications detailing AI- and ML-based MMS documentation, in addition to snowball sampling of selected reference lists. To include the possibility of implicit documentation practices not explicitly labeled as such, we did not use documentation as a key concept but as an inclusion criterion. A 2-stage screening process (title and abstract screening and full-text review) was conducted by 1 author. A data extraction template was used to record publication-related information; barriers to developing ethical and explainable MMS; available standards, regulations, frameworks, or governance strategies related to documentation; and recommendations for documentation for papers that met the inclusion criteria.

RESULTS

Of the 115 papers retrieved, 21 (18.3%) papers met the requirements for inclusion. Ethics and explainability were investigated in the context of AI- and ML-based MMS documentation and translation. Data detailing the current state and challenges and recommendations for future studies were synthesized. Notable themes defining the current state and challenges that required thorough review included bias, accountability, governance, and explainability. Recommendations identified in the literature to address present barriers call for a proactive evaluation of MMS, multidisciplinary collaboration, adherence to investigation and validation protocols, transparency and traceability requirements, and guiding standards and frameworks that enhance documentation efforts and support the translation of AI- and ML-based MMS.

CONCLUSIONS

Resolving barriers to translation is critical for MMS to deliver on expectations, including those barriers identified in this scoping review related to bias, accountability, governance, and explainability. Our findings suggest that transparent strategic documentation, aligning translational science and regulatory science, will support the translation of MMS by coordinating communication and reporting and reducing translational barriers, thereby furthering the adoption of MMS.

Developing and Implementing Predictive Models in a Learning Healthcare System: Traditional and Artificial Intelligence Approaches in the Veterans Health Administration

Predicting clinical risk is an important part of healthcare and can inform decisions about treatments, preventive interventions, and provision of extra services. The field of predictive models has been revolutionized over the past two decades by electronic health record data; the ability to link such data with other demographic, socioeconomic, and geographic information; the availability of high-capacity computing; and new machine learning and artificial intelligence methods for extracting insights from complex datasets. These advances have produced a new generation of computerized predictive models, but debate continues about their development, reporting, validation, evaluation, and implementation. In this review we reflect on more than 10 years of experience at the Veterans Health Administration, the largest integrated healthcare system in the United States, in developing, testing, and implementing such models at scale. We report lessons from the implementation of national risk prediction models and suggest an agenda for research. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 5 is August 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Designing COVID-19 mortality predictions to advance clinical outcomes: Evidence from the Department of Veterans Affairs

Using administrative data on all Veterans who enter Department of Veterans Affairs (VA) medical centres throughout the USA, this paper uses artificial intelligence (AI) to predict mortality rates for patients with COVID-19 between March and August 2020. First, using comprehensive data on over 10 000 Veterans’ medical history, demographics and lab results, we estimate five AI models. Our XGBoost model performs the best, producing an area under the receive operator characteristics curve (AUROC) and area under the precision-recall curve of 0.87 and 0.41, respectively. We show how focusing on the performance of the AUROC alone can lead to unreliable models. Second, through a unique collaboration with the Washington D.C. VA medical centre, we develop a dashboard that incorporates these risk factors and the contributing sources of risk, which we deploy across local VA medical centres throughout the country. Our results provide a concrete example of how AI recommendations can be made explainable and practical for clinicians and their interactions with patients.