Background: Imbalanced health care resource distribution has been central to unequal health outcomes and political tension around the world. Artificial intelligence (AI) has emerged as a promising tool for facilitating resource distribution, especially during emergencies. However, no comprehensive review exists on the use and ethics of AI in health care resource distribution.
Objective: This study aims to conduct a scoping review of the application of AI in health care resource distribution, and explore the ethical and political issues in such situations.
Methods: A scoping review was conducted following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). A comprehensive search of relevant literature was conducted in MEDLINE (Ovid), PubMed, Web of Science, and Embase from inception to February 2022. The review included qualitative and quantitative studies investigating the application of AI in health care resource allocation.
Results: The review involved 22 articles, including 9 on model development and 13 on theoretical discussions, qualitative studies, or review studies. Of the 9 on model development and validation, 5 were conducted in emerging economies, 3 in developed countries, and 1 in a global context. In terms of content, 4 focused on resource distribution at the health system level and 5 focused on resource allocation at the hospital level. Of the 13 qualitative studies, 8 were discussions on the COVID-19 pandemic and the rest were on hospital resources, outbreaks, screening, human resources, and digitalization.
Conclusions: This scoping review synthesized evidence on AI in health resource distribution, focusing on the COVID-19 pandemic. The results suggest that the application of AI has the potential to improve efficacy in resource distribution, especially during emergencies. Efficient data sharing and collecting structures are needed to make reliable and evidence-based decisions. Health inequality, distributive justice, and transparency must be considered when deploying AI models in real-world situations.
Global responses to COVID-19 are converging with the use of digital health and algorithms based on artificial intelligence (AI), impacting health care systems around the world . AI was partially founded by Alan Turing, and a machine or a process that could demonstrate intelligent behaviors in cognitive tasks, which can pass the Turing test, would be deemed as AI [ ]. Multiple AI techniques, such as fuzzy expert systems and Bayesian networks, have been applied both virtually and physically in the health care field [ ]. For example, clinical pathway analysis, a critical area in ensuring standard medical procedures, can be analyzed by pattern-mining procedures [ ]. Resource distribution includes the distribution of resources at strategic, tactical, and operational levels and is a key issue in health policy [ , ].
Luengo-Oroz et al proposed that the application of AI during the COVID-19 pandemic can be broken down into 3 scales: molecular, clinical, and societal . At the molecular level, protein structure prediction, novel nucleic acid testing, drug repurposing, and drug discovery all rely on AI and deep-learning algorithms [ - ]. At the clinical level, diagnosis, treatment, and prognosis all benefit from AI. For example, AI-based computed tomography diagnosis has been widely applied for identifying COVID cases [ , , ], alongside robotics and telemedicine that facilitate clinical processes. At the societal level, AI is applied in epidemiological research and social policymaking. In particular, AI-based case forecasting has been in use since the beginning of the pandemic [ , ]. The application of AI at the societal level can stratify population risk, facilitate diagnosis and testing, support the design of trials and drugs, and inform policymaking, relieving the burden of COVID-19 on health care systems and helping the society to better respond to the pandemic [ ].
The application of AI to decision-making processes in health care systems significantly precedes the COVID-19 pandemic [, ]. Health policy aims at providing health care to the population, and the decision-making process aims to address 2 core issues: screening and diagnosis, and treatment and monitoring [ ]. These 2 tasks are essential to the entire health care system. The policymaking process includes hypothesis generation, hypothesis testing, and action (or policy). AI can learn from past data, including health records, past insurance claims, and disease incidence and prevalence, to improve hypothesis generation and testing, and thus improve the quality of health care policymaking [ ].
In the health care system, resource distribution is an essential issue for policymakers, as resources are always scarce . For example, Kong et al argued that the primary problem in China’s health care system is the lack of high-quality health resources and the consequent supply-demand imbalance. They maintain that AI could benefit from China’s enormous data and has the potential to improve this unequal distribution of health resources [ ].
During the COVID-19 pandemic, imbalanced health care resource distribution has been one of the central issues causing unequal health outcomes and political tension [, ]. Ji et al observed that the higher COVID case-fatality rate in Wuhan city and Hubei province compared with other parts of China at the beginning of the pandemic could potentially be attributed to health care resource scarcity [ ]. Edejer et al projected that the cost of health care resources to combat the pandemic would continue to rise in low- and middle-income counties, and concluded that a comprehensive system of resource distribution is necessary [ ].
Health care resource distribution is determined by the supply-demand relationship, logistics, and governance structure [, ]. Using the COVID-19 response as an example, the severity of the pandemic can determine the health care resources required in each location, but the resources might not be distributed according to need [ ]. AI can be applied to study supply-demand, logistics, and patient characteristics, but the ethics and implications of the use of AI in policymaking remain important issues [ ].
Currently, there are no comprehensive reviews to provide an overall picture of the literature on the application of AI in resource distribution in health care settings, particularly with regard to societal and ethical aspects. This study aims to conduct a scoping review on the application of AI in health care resource distribution, particularly during the COVID-19 pandemic and to explore the ethics and implications of AI in health policymaking with regard to resource distribution.
Scoping Review Design
This scoping review follows the framework proposed by Arksey and O’Malley . Briefly, the review has the following 5 stages: (1) identifying the research question, “What are the roles of AI and machine learning in the allocation of health care resources, before and during the COVID-19 pandemic?”; (2) identifying suitable studies; (3) selecting studies for review; (4) consolidating the data; and (5) summarizing and reporting the results. This study complies with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) [ ] for reporting scoping review results.
Data Source and Search Strategy
Searches were conducted in MEDLINE (Ovid), PubMed, Web of Science, and Embase from inception to February 2022. The search featured 2 key terms: (1) artificial intelligence, including related terms such as big data and algorithm, and (2) health care resource allocation. The search terms were used with the “explode” feature where applicable. For example, in MEDLINE and Embase, we used exp artificial intelligence/ and exp resource allocation/, and in PubMed, relevant MeSH (Medical Subject Heading) terms were used. The search was individually designed and adapted for each database.
Inclusion and exclusion criteria were defined a priori. This scoping review includes qualitative and quantitative studies investigating the application of AI in health care resource allocation. Studies that are not relevant to AI or health care resource allocation were excluded, as were duplicate studies. The inclusion and exclusion criteria are summarized in.
Selection was conducted in 2 steps. First, titles and abstracts were screened for topic relevance and study design. Second, full texts of the remaining studies were screened to check for eligibility. All of the study selection processes were conducted in EndNote X9 (Clarivate).
|Type of study||Qualitative, quantitative, mixed method, and review studies in peer-reviewed journals||Letters, comments, conference abstracts, editorials, and theses|
|Language||English||All other languages|
|Study variables||Includes (1) artificial intelligence/machine learning and relevant terms and (2) allocation of health care resources||Does not include (1) artificial intelligence/machine learning and relevant terms or (2) allocation of health care resources|
|Study context||Health care resource allocation at either the population level or hospital level||All other resource allocation scenarios|
Selected studies were input into NVivo 12 (QSR International) for labeling and coding. Authors coded data of interest from the articles in NVivo 12 and extracted information regarding study author, study design, location, context, aim, main result, AI method under study, resource allocation situation, and policymaking relevance into a standardized Excel (Microsoft Corp) form.
Summarizing the Results
We employed an inductive approach to summarize the results from the included studies. First, the selected papers were grouped into 2 types: (1) studies of model development and validation of AI-based algorithms applied to health care resource distribution, and (2) qualitative studies, theoretical discussions, and review studies of the application of AI in health care resource distribution. For studies of model development and validation, we extracted the study objectives, resource distribution situations, AI model input variables, and policy relevance. For studies in the second category, objectives, resource distribution situations, discussed topics, and policy relevance were extracted. We further divided the input variables of the studies of model development and validation into 2 predefined categories: (1) ecological variables or variables at the group level, which included variables depicting characteristics at the population level, such as infant mortality in a region, local economic development, or disease prevalence and incidence; and (2) individual variables, which included variables that define individual characteristics such as diagnosis and age.
In total, 298 studies were identified in 4 databases after removing duplicates. After 1 round of screening for titles and abstracts, 255 studies were excluded due to irrelevant topics and unsuitable study designs. This left 43 studies for full-text screening. Of these, 2 were excluded because they were not directly relevant to health care, 8 because they were not related to resource distribution, 7 because they did not feature applications of AI, and 4 because of an inappropriate study design. In the end, 22 studies remained for qualitative synthesis. The PRISMA flow diagram for study selection is presented in.
Summary of the Characteristics of Studies on Model Development
The characteristics of the included studies on model development are summarized in. The included studies were published between 2013 and 2021. Of the 22 included studies, 9 focused on model development and validation [ - ]. Of these, 5 studies were conducted in emerging economies, including 2 in China [ , ], 2 in Brazil [ , ], and 1 in Ecuador [ ]. In developed countries, 3 studies were conducted. These included 1 in Germany [ ], 1 in the United Kingdom [ ], and 1 in the United States with a validation data set in China [ ]. One study was applied to a global context [ ].
Of the 9 studies, 4 focused on resource distribution at the health system level, including financial resources for public health in Brazil , health care resource distribution in health planning in Ecuador [ ], medical resource allocation in the hierarchical health system in China [ ], and medical equipment allocation in the global COVID-19 pandemic [ ]. The remaining 5 studies focused on resource allocation at the hospital level, including bed allocation in a London hospital [ ], day resources and bed allocation in a hospital in Munich, Germany [ ], human resources and medical materials in a public hospital in China [ ], medical resource allocation in a hospital in the capital of State of Minas Gerais in Brazil [ ], and medical resource allocation in clinics for COVID-19 patients in New York [ ].
|Reference||Objectives||Resource allocation situation||Input variables|
|Rosas et al (2013) ||To construct a financial resource allocation model using an artificial neural network||Financial resources for public health in Brazil||Mortality characteristics, proportion of teenage mothers, proportion of inadequate prenatal care, fertility rate, Gini index, proportion of elderly people in the population, literacy rate, ﬁnancing capacity per capita, percentage of people with income below half minimum wage, percentage of urban households with basic sanitation, and proportion of urban households served by garbage collection|
|Belciug & Gorunescu (2015) ||To propose a bed allocation and financial resource utilization strategy through queuing modeling and evolutionary computation||Bed allocation and financial resource utilization in the geriatric department of a London hospital||Bed inventory, arrival rate, mean service time, patient flow parameters, and holding and penalty cost and other cost considerations|
|Gartner & Padman (2015) ||To evaluate how early determination of diagnosis-related groups can be used for better allocation of scarce hospital resources||Hospital resources, including day resources and overnight resources (beds), validated in a mid-sized hospital near Munich, Germany||Primary and secondary diagnoses, clinical procedures, age, gender, and weight in newborns|
|Velez et al (2016) ||To present an artificial intelligence–based health planning model based on data from geospatial systems||Health care resource distribution in health planning in Ecuador||Geospatial variables based on the social determinants of health and geospatial patterns of territorial distribution in the allocation of equipment, supplies, and health services in relation to the availability, accessibility, and need of the population|
|Xu et al (2018) ||To propose a health resource allocation model based on mass customization to maximize revenue and customization||Allocation of doctors and other medical resources in a public hospital system in China||Distribution of medical stations, professional level of doctors (salary and seniority), patient preferences and illness severity, medical cost, and revenue|
|Yousefi et al (2018) ||To present a model based on agent-based simulation, machine learning, and a genetic algorithm for allocation of medical resources in emergency departments||Medical resource allocation in a teaching hospital in the capital of State of Minas Gerais in Brazil||Number of receptionists in the reception area; number of triage nurses in the triage room; number of laboratory technicians in the laboratory and X-ray room; and number of doctors, nurses, and nurse technicians in the suturing yellow zone, orthopedics department, surgical department, and clinical emergency area.|
|Zhang et al (2018) ||To propose a framework introducing a novel approach to multi-attribute decision-making problems in the picture fuzzy context||Medical resource allocation in the hierarchical medical treatment system in China||Patient diagnostic characteristics and hospital tiers|
|McRae et al (2020) ||To present a clinical decision-support system and mobile app to assist in COVID severity assessment, management, and care||Resource allocation during COVID in New York, with validation data sets from Wuhan, China||Outpatient score (age, gender, diabetes, cardiovascular comorbidities, and systolic blood pressure) and biomarker score (C-reactive protein, procalcitonin, and age)|
|Bednarski et al (2021) ||To study how reinforcement learning and deep-learning models can facilitate the redistribution of medical equipment during pandemics||Pandemics in the context of COVID||COVID risk factors by region, COVID mortality by region, and current demand for medical equipment|
Summary of the Characteristics of Studies Involving Reviews and Theoretical Discussions
The characteristics of studies involving reviews and theoretical discussions are summarized in. Of the 22 included studies, 13 were theoretical discussions, qualitative studies, or review studies [ - ]. Of those studies, 8 studies were qualitative discussions on the COVID-19 pandemic [ , , , , , , , ], with 2 in a Chinese context [ , ] and the rest in a global situation. The remaining 5 studies focused on other situations, with 1 focusing on resource allocation in intensive care units and hospital stay [ ], 1 on disease outbreaks and disasters [ ], 1 on diabetic retinopathy screening [ ], 1 on human resource allocation in health systems [ ], and 1 on medical information digitalization [ ].
|Reference||Objective||Resource allocation situation||Reviewed/discussed methods for the application of AIa during the COVID-19 pandemic|
|Rajkomar et al (2018) ||To explore how model design, biases in data, and interactions of model predictions with clinicians and patients exacerbate health inequalities||Intensive care unit and in-hospital stay length|
|Laudanski et al (2020) ||To analyze the applications of AI during COVID using the WHOb framework of pandemic evolution||Global COVID-19 pandemic|
|Adly et al (2020) ||To discuss the potential of using AI to prevent and control COVID||Global COVID-19 pandemic|
|Bernardo et al (2020) ||To present approaches for using technology to facilitate resource distribution in disasters and outbreaks||Disasters and disease outbreaks|
|Neves et al (2020) ||To discuss the basic principles of medical resource allocation choices during COVID||Global COVID-19 pandemic|
|Xie et al (2020) ||To present an overview of the application of AI technology in ophthalmology, with a focus on deep-learning systems||Diabetic retinopathy screening|
|Zou et al (2020) ||To present the COVID response of Shenzhen, China and discuss the potential of a successful model for COVID prevention and control||COVID-19 pandemic in Shenzhen, China|
|Basit et al (2021) ||To discuss the data sharing and collection process and the ethical considerations around pandemic data||Global COVID-19 pandemic|
|Huang et al (2021) ||To investigate China’s health informatization, especially during the COVID-19 pandemic||COVID-19 pandemic in China|
|Jain et al (2021) ||To discuss the implications of AI for employability by analyzing issues in the health care sector||Human resources in health systems|
|Lu et al (2021) ||To establish barriers that affect medical information digitalization innovation and development through interviews and a literature review||Medical information digitalization|
|Pereira et al (2021) ||To present interindividual variability and the roles it plays in the variability of COVID presentation and susceptibility.||Global COVID-19 pandemic|
|Röösli et al (2021) ||To discuss possible bias in the application of AI during the COVID-19 pandemic||Global COVID-19 pandemic|
aAI: artificial intelligence.
bWHO: World Health Organization.
Summary of the Policy Implications of the Selected Studies
The policy implications of studies on model development are relevant on 2 levels: (1) health system level [, , , ] and (2) hospital level [ - , , ], corresponding to situations where the models were applied. Detailed policy implications of the included studies on model development are summarized in . The qualitative and review studies focused largely on 2 issues: (1) how AI can promote the efficacy of resource allocation [ , , - , , , ] and (2) the ethics and equality issues associated with using AI systems [ , , ]. One study highlighted the lack of AI studies on resource distribution during COVID-19 [ ]. summarizes the policy implications of these studies.
|Rosas et al (2013) |
|Belciug & Gorunescu (2015) |
|Gartner & Padman (2015) |
|Velez et al (2016) |
|Xu et al (2018) |
|Yousefi et al (2018) |
|Zhang et al (2018) |
|McRae et al (2020) |
|Bednarski et al (2021) |
|Rajkomar et al (2018) |
|Laudanski et al (2020) |
|Adly et al (2020) |
|Bernardo et al (2020) |
|Neves et al (2020) |
|Xie et al (2020) |
|Zou et al (2020) |
|Basit et al (2021) |
|Huang et al (2021) |
|Jain et al (2021) |
|Lu et al (2021) |
|Pereira et al (2021) |
|Röösli et al (2021) |
aAI: artificial intelligence.
Case Study Comparison: China and Brazil
China and Brazil are both developing countries with a similar per capita gross domestic product (China: US $10,435 and Brazil: US $6797) . During the COVID-19 pandemic, Brazil has had one of the highest national overall cases and mortalities, as well as per capita cases and mortalities, with 29.5 million cases and 656,000 deaths as of March 2022 [ ]. China has had one of the lowest per capita infection rates in the world, with a total of 124,000 cases and 4636 deaths as of March 2022 [ ]. Given the similarity between the 2 countries in economic development and the enormous difference in COVID cases and mortalities, the resource distribution situation in the 2 countries is worth exploring.
Rosas et al  proposed a financial resource allocation algorithm for the public hospital system in Brazil based on mortality, socioeconomic characteristics, and income inequality. They argued that the choice of input variables for health care policymaking should consider the vulnerability of the population to being manipulated by those who manage public policy, the true representation of the factors of need, exemption from the process of political choice, and the availability of reliable data. The focus of the model was regional economic characteristics.
Zhang et al  proposed a model for the allocation of medical resources and tier classification of patients in China’s health system, with the input variables of patient characteristics and hospital tiers, and a focus on differentiation into different tiers based on patients’ disease severity. Xu et al [ ] proposed a health resource allocation model for the allocation of doctors and other medical resources in a public hospital system in China that considered the distribution of medical stations, the professional level of doctors (salary and seniority), patient preferences and illness severity, medical cost, and revenue.
Overall, the allocation of medical resources based on the models from the 3 studies demonstrated that the key considerations proposed by studies from China were the hospital tier system, the professional level of doctors, the geographical distribution of medical resources, and cost-effectiveness [, ]. However, the model proposed for Brazil focused on the regional economic situation [ ].
In this review, we compiled evidence on the application of AI in health resource distribution, especially regarding COVID-related policy. After synthesizing 22 articles, we found that AI-based models were proposed at both hospital (secondary care in inpatient settings) and health system (public health) levels and that theoretical discussions and reviews focused on the potential for AI to improve the efficacy of resource distribution and on the ethics of applying AI in health resource distribution. Two major themes emerged from the review. First, we found that AI-informed resource distribution strategies are impactful for health access and equality. Second, the approaches can be categorized ideologically into revisionist and conservative groups.
Impact of an AI-Informed Resource Distribution Strategy on Health Access and Equality
AI and machine learning have considerable potential to improve efficacy in resource distribution, especially during emergencies, such as the COVID-19 pandemic, where quick decisions are required based on evolving situations [, , ]. For example, health informatization, particularly digital contact tracing and AI-informed response design, played an instrumental role in responding to COVID in China and helped local governments to improve efficacy in allocating limited resources [ , ]. AI can also be used to interpret diagnostic results and patient characteristics in order to predict disease progression and allocation of medicines, hospital beds, and medical professionals at the hospital level [ , ].
However, very large amounts of data are necessary for AI algorithms to make reliable and evidence-based decisions . Health care institutions globally must therefore collect, record, and analyze data. This will help policymakers gather novel insights and translate the data into a prompt, equal, coordinated, and more successful response to the next pandemic [ , ]. As such, data collection must be institutionalized. The disparity in data collection capacity potentially exacerbates the gap in decision-making quality between countries [ , ]. For example, from the literature, China’s information infrastructure and data-sharing agreements expedited the data-gathering process, a possible consequence of the centralized government system that facilitated gathering data, which in turn made the data set larger and more comprehensive [ ]. In contrast, a selected study showed that Brazil’s decentralized government system, with heterogeneous policies on data privacy and data sharing, made the collection and consolidation of data difficult [ ]. However, caution should be taken in interpreting those results, as there is no evidence that the studies selected here are representative of the real situation in China or Brazil.
The included articles highlighted the importance of distributive justice and transparency in AI model design. The analysis conducted by Rajkomar et al emphasized that machine learning systems should be used proactively to advance health equality . They proposed that distributive justice should be a core principle in AI models, including during the design, deployment, and evaluation processes. This perspective would include equality in patient outcomes, performance for every sociodemographic group, and resource allocation for each group. As Neves et al noted, resource allocation by AI and in emergencies should build on basic ethical values, including the equal value of people, instrumental value, and priority for critical situations. Transparency is the key to gaining trust when distributing resources [ ].
Revisionist and Conservative Approaches in AI-Derived Resource Distribution
The build-up of AI models and implementation plans can be broadly categorized into revisionist and conservative approaches. In revisionist approaches, the models aim to revise the disparity in resource distribution by actively correcting the biases in previous decision-making processes. For example, the models proposed by Rosas et al  for financial resource allocation in Brazil emphasized consideration of income inequality, vulnerable populations, political choices, and the availability of reliable data. In conservative approaches, the models rely on traditional metrics, including supply and demand, profitability, and, perhaps most notably, previous decisions. This was demonstrated in a proposed model for the allocation of medical resources and tier classification of patients in China’s health system by Zhang et al [ ], where the input variables were patients’ characteristics and hospital tiers, and a model suggested by Xu et al [ ] for the allocation of doctors and other medical resources in a public hospital system in China, where the input variables included the distribution of medical stations, the professional level of doctors, patient preferences and illness severity, medical cost, and revenue. Doctor expertise, patient characteristics, hospital tier, and location are common variables in human decision-making, but AI has the potential to analyze the data more thoroughly.
However, despite the revisionist model proposed by Brazilian academics , health inequality is a prevailing issue in Brazil across states and social classes, both before [ ] and during the COVID-19 pandemic [ ]. Health inequality in Brazil increased across states from 1990 to 2016 [ ]. Comparatively, the health care access and quality index in China was higher than that in Brazil in 2016, suggesting better equality and health care access in China [ ]. However, due to the limitation of the research method, this study could not show the policymaking processes in both countries. From the selected studies alone, we observed that although proposing revisionist AI models to address health inequality should be encouraged, the application and practicality of using those models to inform health policy decisions and improve inequality should also be important considerations for researchers.
Strengths and Limitations
This is one of the first reviews to incorporate all available evidence qualitatively and provide a comprehensive picture of the model development and theoretical discussion on AI in medical resource distribution. Our results contribute to the ongoing discussion of applying AI in medical resource distribution and add novel insights into the social and ethical implications. Nonetheless, this study has several limitations. First, due to the scope of the study, we focused on published journal articles but did not examine policy documents or grey literature. This could have led to incompleteness in the collected information. Further studies could examine policy statements and grey literature to better understand intercountry differences. Second, we included only articles published in English and therefore might have overlooked publications in other languages. Third, there are potential sources of meaningful heterogeneity in this scoping review, including the diverse use of AI technologies, different study designs, and different locations. The analyses in this study could be affected by such heterogeneities. Fourth, this study is a qualitative overview of the general application of AI in health care resource distribution and is exploratory. We did not compare different levels of resource distribution and distinguish various machine learning methods in detail. Further studies are needed to explore and contrast different AI approaches at various resource distribution levels in detail. Lastly, due to the availability of evidence, we only compared studies from China and Brazil. We were only able to compare the differences between the 2 countries based on a few studies, which could not represent the real situation in either country. The comparison should be interpreted as exploratory and demonstrative.
This scoping review synthesized evidence on the application of AI in health resource distribution, particularly during the COVID pandemic. The included studies suggested that AI and machine learning have high potentials to improve efficacy in resource distribution, especially during sudden and evolving situations. A coordinated and continuous data sharing and collecting mechanism is needed for better data input so that AI can make reliable and evidence-based decisions. Various issues, including health inequality, distributive justice, and transparency, should be considered when deploying AI models. Such considerations are required for implementing revisionist AI models that can correct distribution inequality in actual policy processes.
Conflicts of Interest
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|AI: artificial intelligence|
Edited by B Malin, K El Emam; submitted 31.03.22; peer-reviewed by W Perveen, H Mehdizadeh, D Vurmaz, ER Khalilian, D Gartner; comments to author 14.06.22; revised version received 29.12.22; accepted 06.01.23; published 30.01.23Copyright
©Hao Wu, Xiaoyu Lu, Hanyu Wang. Originally published in JMIR AI (https://ai.jmir.org), 30.01.2023.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR AI, is properly cited. The complete bibliographic information, a link to the original publication on https://www.ai.jmir.org/, as well as this copyright and license information must be included.