Artificial intelligence-based computer aided detection (AI-CAD) in the fight against tuberculosis: Effects of moving health technologies in global health

The integration of human and non-human actors to advance healthcare delivery: unpacking the role of actor-network theory, a systematic literature review

BACKGROUND

There is an increasing public, societal and policy imperative for effective integration of healthcare delivery systems. Central to integration in healthcare is a focus on people-centred health, access, patient empowerment, interprofessional teamwork and collaboration between all healthcare stakeholders - difficult to achieve in current silo-driven bureaucratic health organisations. Therefore, actor-network theory (ANT) offers a theoretical approach to understanding the complexities of healthcare delivery by unpacking the type of actor's interplay between social elements and immaterial objects, their interactions, interdependencies and power dynamics.

AIMS

The first of its type, this systematic review aims to identify, synthesise, and appraise extant literature on the use and application of ANT in healthcare contexts.

METHODS

This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO. The authors generated a search strategy utilising 31 Boolean terms, conducting electronic searches of MEDLINE, CINAHL Complete, SCOPUS, PubMed, APA PsycINFO, Business Source Complete and Academic Search Complete. The studies obtained were evaluated for inclusion based on their alignment with the specified inclusion and exclusion criteria. Studies were independently evaluated by the authors, with all data synthesised using a thematic analysis.

RESULTS

From an initial 2,533 studies, the systematic review included 103 studies which utilised ANT within a healthcare context. The analysis of the studies identified trends in the application of ANT across healthcare which we categorised into four themes: healthcare delivery systems, technology and data, integrated care, and innovation management. The findings demonstrated variability and fragmentation in the application of ANT, often diverging from its fundamental principles.

CONCLUSIONS

Decluttering the literature suggests three dimensions for understanding the relationships of actors, unidimensional ANT - based on single actors, bi-dimensional ANT, the relationship between two actors and multi-dimensional ANT, where human and non-human actors interact to impact healthcare outcomes. The limited number of studies on the use of ANT for integrated healthcare research highlights both its importance to the topic and the considerable research gap that must be addressed.

Breaking the threshold: Developing multivariable models using computer-aided chest X-ray analysis for tuberculosis triage

OBJECTIVES

Computer-aided detection (CAD) software packages quantify tuberculosis (TB)-compatible chest X-ray (CXR) abnormality as continuous scores. In practice, a threshold value is selected for binary CXR classification. We assessed the diagnostic accuracy of an alternative approach to applying CAD for TB triage: incorporating CAD scores in multivariable modeling.

METHODS

We pooled individual patient data from four studies. Separately, for two commercial CAD, we used logistic regression to model microbiologically confirmed TB. Models included CAD score, study site, age, sex, human immunodeficiency virus status, and prior TB. We compared specificity at target sensitivities ≥90% between the multivariable model and the current threshold-based approach for CAD use.

RESULTS

We included 4,733/5,640 (84%) participants with complete covariate data (median age 36 years; 45% female; 22% with prior TB; 22% people living with human immunodeficiency virus). A total of 805 (17%) had TB. Multivariable models demonstrated excellent performance (areas under the receiver operating characteristic curve [95% confidence interval]: software A, 0.91 [0.90-0.93]; software B, 0.92 [0.91-0.93]). Compared with threshold scores, multivariable models increased specificity (e.g., at 90% sensitivity, threshold vs model specificity [95% confidence interval]: software A, 71% [68-74%] vs 75% [74-77%]; software B, 69% [63-75%] vs 75% [74-77%]).

CONCLUSION

Using CAD scores in multivariable models outperformed the current practice of CAD-threshold-based CXR classification for TB diagnosis.

Artificial intelligence and the politics of avoidance in global health

For decades, global health actors have centered technology in their interventions. Today, artificial intelligence (AI) is emerging as the latest technology-based solution in global health. Yet, AI, like other technological interventions, is not a comprehensive solution to the fundamental determinants of global health inequities. This article gathers and critically appraises grey and peer-reviewed literature on AI in global health to explore the question: What is avoided when global health prioritizes technological solutions to problems with deep-seated political, economic, and commercial determinants? Our literature search and selection yielded 34 documents, which we analyzed to develop seven areas where AI both continues and disrupts past legacies of technological interventions in global health, with significant implications for health equity and human rights. By focusing on the power dynamics that underpin AI's expansion in global health, we situate it as the latest in a long line of technological interventions that avoids addressing the fundamental determinants of health inequities, albeit at times differently than its technology-based predecessors. We call this phenomenon the 'politics of avoidance.' We conclude with reflections on how the literature we reviewed engages with and recognizes the politics of avoidance and with suggestions for future research, practice, and advocacy.

AI for Detection of Tuberculosis: Implications for Global Health

Tuberculosis, which primarily affects developing countries, remains a significant global health concern. Since the 2010s, the role of chest radiography has expanded in tuberculosis triage and screening beyond its traditional complementary role in the diagnosis of tuberculosis. Computer-aided diagnosis (CAD) systems for tuberculosis detection on chest radiographs have recently made substantial progress in diagnostic performance, thanks to deep learning technologies. The current performance of CAD systems for tuberculosis has approximated that of human experts, presenting a potential solution to the shortage of human readers to interpret chest radiographs in low- or middle-income, high-tuberculosis-burden countries. This article provides a critical appraisal of developmental process reporting in extant CAD software for tuberculosis, based on the Checklist for Artificial Intelligence in Medical Imaging. It also explores several considerations to scale up CAD solutions, encompassing manufacturer-independent CAD validation, economic and political aspects, and ethical concerns, as well as the potential for broadening radiography-based diagnosis to other nontuberculosis diseases. Collectively, CAD for tuberculosis will emerge as a representative deep learning application, catalyzing advances in global health and health equity. Keywords: Computer-aided Diagnosis (CAD), Conventional Radiography, Thorax, Lung, Machine Learning Supplemental material is available for this article. © RSNA, 2024.

Speed, accuracy, and efficiency: The promises and practices of digitization in pathology

Digitization is often presented in policy discourse as a panacea to a multitude of contemporary problems, not least in healthcare. How can policy promises relating to digitization be assessed and potentially countered in particular local contexts? Based on a study in Denmark, we suggest scrutinizing the politics of digitization by comparing policy promises about the future with practitioners' experience in the present. While Denmark is one of the most digitalized countries in the world, digitization of pathology has only recently been given full policy attention. As pathology departments are faced with an increased demand for pathology analysis and a shortage of pathologists, Danish policymakers have put forward digitization as a way to address these challenges. Who is it that wants to digitize pathology, why, and how does digitization unfold in routine work practices? Using online search and document analysis, we identify actors and analyze the policy promises describing expectations associated with digitization. We then use interviews and observations to juxtapose these expectations with observations of everyday pathology practices as experienced by pathologists. We show that policymakers expect digitization to improve speed, patient safety, and diagnostic accuracy, as well as efficiency. In everyday practice, however, digitization does not deliver on these expectations. Fulfillment of policy expectations instead hinges on the types of artificial intelligence (AI) applications that are still to be developed and implemented. Some pathologists remark that AI might work in the easy cases, but this would leave them with only the difficult cases, which they consider too burdensome. Our particular mode of juxtaposing policy and practice throws new light on the political work done by policy promises and helps to explain why the discipline of pathology does not seem to easily lend itself to the digital embrace.

Tweaking algorithms. Technopolitical issues associated with artificial intelligence based tuberculosis detection in global health

Computer-aided detection algorithms based on artificial intelligence are increasingly being tested and used as a means for detecting tuberculosis in countries where the epidemic is still present. Computer-aided detection tools are often presented as a global solution that can be deployed in all the geographical areas concerned by tuberculosis, but at the same time, they need to be adjusted and calibrated according to local populations' characteristics. The aim of this article is to analyze the tensions between the standardization of computer-aided detection algorithms and their local adaptation and the political issues associated with these tensions. We undertook a qualitative analysis of practices associated with tuberculosis detection algorithms in different contexts, contrasting the perspectives of various stakeholders. Algorithms embed the promise of standardization through automation and the bypassing of variable human expertise such as that of radiologists, they are nonetheless objects of local practices that we have characterized as "tweaking." This work of tweaking reveals how the technology is situated but also the many concerns of the users and workers (insertion in care, control over infrastructure, and political ownership). This should be better considered to truly make computer-aided detection innovative tools for tuberculosis management in global health.

Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations

BACKGROUND

Artificial intelligence (AI), which combines computer science with extensive datasets, seeks to mimic human-like intelligence. Subsets of AI are being applied in almost all fields of medicine and surgery.

AIM

This review focuses on the applications of AI in healthcare settings in developing countries, designed to underscore its significance by comprehensively outlining the advancements made thus far, the shortcomings encountered in AI applications, the present status of AI integration, persistent challenges, and innovative strategies to surmount them.

METHODOLOGY

Articles from PubMed, Google Scholar, and Cochrane were searched from 2000 to 2023 with keywords including AI and healthcare, focusing on multiple medical specialties.

RESULTS

The increasing role of AI in diagnosis, prognosis prediction, and patient management, as well as hospital management and community healthcare, has made the overall healthcare system more efficient, especially in the high patient load setups and resource-limited areas of developing countries where patient care is often compromised. However, challenges, including low adoption rates and the absence of standardized guidelines, high installation and maintenance costs of equipment, poor transportation and connectivvity issues hinder AI's full use in healthcare.

CONCLUSION

Despite these challenges, AI holds a promising future in healthcare. Adequate knowledge and expertise of healthcare professionals for the use of AI technology in healthcare is imperative in developing nations.