AI in UK Radiology: Economic Evaluation Review Assignment Sample

Introduction - Cost Analysis of Artificial Intelligence in Radiology

AI has progressed from theoretical concepts to real-life applications that have touched every industry. The history of AI is rich in milestones both with regards to technology and application, from early rule based systems up until contemporary advances as machine learning and deep learning models. Established with an initial budget of £140 million, this funding stream aimed to facilitate various AI-focused projects and technologies, particularly targeting small to medium-sized enterprises (SMEs), with the goal of fostering innovation and growth within this sector (UK Parliament, 2020).

The purpose of the £123 million funding stream that was directed towards AI technologies in healthcare over four years was focused primarily on new application development and evaluation. These projects enhanced an advanced AI-driven breast cancer detection system, a mobile phone self-testing instrument and various clinical decision making tools. Its aim was to leverage the power of AI in bringing transformation within healthcare, achieving better diagnostic sensitivity and specificity with optimized outcomes for patients while offering increased efficiency (UK Parliament, 2020). AI in radiology can potentially reduce the costs associated with medical imaging by automating the analysis of images, thereby reducing the time radiologists spend on each case. This could lead to cost savings for healthcare providers.

As underlined by Garibay et al. (2023), the desire to evaluate economic effects within adopting AI operated technologies in health care strongly suffered from impediments The main challenge was the large variety of AI technologies, each one requiring specific evaluation metrics and a general absence of lasting evidence regarding their impact on efficiency. This was further complicated by the lack of uniform guidelines for economic evaluation in this industry. AI algorithms can assist in improving the accuracy of diagnoses made from radiological images. By identifying patterns that might be missed by the human eye, AI can contribute to earlier and more accurate diagnosis of diseases, potentially leading to better patient outcomes and reduced treatment costs. The absence of systematized methods of evaluating AI technologies in the healthcare sector, however, does highlight a key gap in understanding their economic impact. This gap directly encourages the need for scoping review. This review seeks to systematically uncover and analyze a wide variety of AI applications in healthcare so as it could identify, categorize and integrate the existing methodologies for economic evaluation. (Xie et al. 2020). The NHS AI in Health and The necessity for an evidence map stems from existing literature and the context of the NHS initiative. This project aims to create a comprehensive evidence map detailing the current landscape of economic evaluations of AI-empowered health technologies. AI technologies can be used for training purposes, helping radiologists and trainees to identify rare conditions or subtle anomalies in radiological images.

Aim and Objectives

Aim

This study aims to describe and evaluate the methodologies used to investigate the economic evidence including cost analysis (both direct and indirect costs), cost-effectiveness, and return on investment pertaining to AI-enabled technologies within the healthcare sector of radiology.

Objectives

• To describe the methods being used for studying the economic evidence of AI-enabled technologies in the healthcare sector of radiology.
• To analyse the findings and conclusions of the economic evidence of AI-enabled technologies in the healthcare sector
• Consider the extent to which methodological differences between studies may explain differences in conclusion drawn.
• To formulate recommendations to improve the economic feasibility of AI-enabled technologies in the healthcare sector during both the development and application stages.

Method

Type of Study and Rationale

This research adopts a pragmatic approach, as outlined by Allemang, Sitter, and Dimitropoulos (2022), to evaluate the economic evidence of AI-enabled technologies in healthcare. This approach prioritizes methods that yield practical, real-world insights and actionable outcomes, facilitating informed decision-making in healthcare settings. The economic evaluation also informs policy and regulation, ensuring that the integration of AI in healthcare is done in a way that is ethical, safe, and aligned with the broader goals of the healthcare system.

Inductive reasoning is applied in this study together with existing methodologies for economic evaluation as it pertains to the aims of the research. Following Ryder et al. (2019), the inductive part involves collecting specific examples of economic evidence based on AI-supported healthcare technologies to identify patterns, similarities and emergent themes, from bottoms upwards. At the same time, this study also incorporates established methods of economic evaluation to provide a structured framework. Understanding the economic impact of AI in radiology can guide future investments in technology and research, ensuring that funds are allocated to areas with the highest potential for positive impact.

In the introductory context of this study, the selection of a scoping review as the methodology is particularly justifiable given the broad and exploratory nature of the research objectives. A scoping review is a systematic process of mapping the existing literature in a specific field to identify key concepts, theories, sources of evidence, and gaps in the research (Arksey & O'Malley, 2005; Levac, Colquhoun, & O'Brien, 2010). A scoping review provides an opportunity for a structured and comprehensive assessment of literature covering different types of study designs, research methodologies, and economic evaluation frameworks used in studying AI-enabled technologies in healthcare (Peters et al. 2021). This methodological perspective fits the purpose of this study in mapping the economic landscape evidence while preserving diversity and changes related to AI applications use in healthcare contexts which is why a scoping review methodology justifies being chosen for this research.

Sources of Information

This study on evaluating the economic evidence of AI-enabled technologies in healthcare involves a systematic approach to data gathering from various reputable sources. The data sources will be peer-reviewed academic journals, conference proceedings and reputable databases such as PubMed, IEEE Xplore and Cochrane Library. These are selected as the sources for their rigorously checked content, which offers a plethora of scholarly articles and studies available on economic assessments of AI in healthcare. Furthermore, grey literature including reports from government health agencies, international organisations and white papers from industry bodies will be searched to capture real-world insights and perspectives.

These sources justify their inclusion, as together they are capable of painting a full picture regarding the economic environment to which AI technologies in healthcare belong. Peer-reviewed journals ensure academic rigour and credibility while databases compile a very large repository of empirical studies and theoretical frameworks (Lockwood, Santos and Pap, 2019). This scholarly knowledge is supplemented by the grey literature that provides actual contextual information and policy directions.

Data Collection Method

The methodology for this scoping review would consist of systematic selection and extraction of secondary data from peer-reviewed journal articles, which would match the aim to comprehensively study the economic evidence of AI-enabled technologies in healthcare. The use of secondary data such as articles from reputable journals allows access to reliable and scholarly information, thus enhancing the reliability and credibility of the findings. The secondary data collection procedure includes a review based on multiple electronic databases, such as Scopus, Web of Science, Embase, and IEEE Xplore. This inclusive approach attempts to cover a large scope of literature, to ensure inclusivity of different perspectives and insights within the domain. The keywords will be joined using the Boolean operators ‘AND’, ‘OR’, and ‘NOT’ to specify the search queries.

The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) will be used to search and screen the search results. Adherence to PRISMA provides methodological stringency, improves reproducibility and reduces bias thereby strengthening the validity and generalisability of the review process (Salameh et al. 2020). After the removal of the duplicate search results, the initial screening involves the use of inclusion and exclusion criteria for identifying the articles that are relevant to the focus of this study. The inclusion and exclusion criteria are listed in Table 1.

Table 1: Inclusion and Exclusion Criteria for Literature Search

So screening will be a 2 stage process first going through titles and abstracts and then for potentially relevant titles and abstracts you would obtain full text papers and then check these against the inclusion/exclusion criteria. Only those that get through this go to data extraction

Post-screening, the chosen articles will go through a detailed data extraction phase. The methodology used in the study is about a systematic form of analysis and extraction from current literature. Using the PRISMA framework, which is known for its stringent methodological structure in systematic reviews, and collecting data on study purposes; used methods; economic evaluation approaches; findings as well as conclusions from different articles will be conducted following a number of journal papers. This systematic process is essential for the detection of patterns, gaps and emergent trends within economic evaluations to AI-enabled technologies in healthcare. The analysis phase will be a rigorous synthesis of this data. Through the application of secondary data as well as compliance with PRISMA guidelines, the reliability level within this study increases. The synthesis that will follow not only describe the current landscape of economic evaluations on AI-based technologies in medicine but also form a strong basis for understanding major findings and developmental trends therein.

Data Processing and Analysis Method

In this study, the data processing and analysis methodology will use a structured approach of integrating data extraction, thematic analysis, and narrative synthesis to provide holistic evidence from the economic angle for AI-enabled technologies in healthcare. A data extraction table will be used to systematically collect relevant information from the chosen research articles (Peters et al. 2021). This table will include the most important variables, including study parameters, details of the AI technology used, methods to conduct an economic evaluation or those that report cost-effectiveness or economic impact.

What about critical appraisal?For both clinical study design and economic methods??

Data obtained will be subjected to rigorous thematic analysis using a deductive approach which involves categorising information according to predetermined themes drawn from the research objectives (Vaismoradi and Snelgrove, 2019). Themes will capture elements, including the form of economic evaluation methods used such as cost-effectiveness analysis or cost-utility assessment, health care settings where AI technologies were deployed and the nature of reported economic outcomes.

As thematic analysis is conducted, a narrative synthesis approach will be taken to integrate and interpret the results of the studies included in this review. This synthesis process entails the compilation and assimilation of information spanning from different studies to develop an integrated storyline that clarifies the overall trends, likenesses, and differences noted in the economic data pertaining to AI-driven healthcare technologies (Boutillier et al. 2019). It will help in bringing a fine detail of what has been reported on the economic outcomes, focusing on major trends, differences in methodologies used and possible consequences for decision-making. The mechanism of structured data extraction combined with thematic analysis, and narrative synthesis will give a broad picture concerning the methods used in economic evaluations of AI technologies in healthcare. This approach seeks to identify significant insights, explain the economic evidence around AI-empowered technologies and guide further research and healthcare policies.

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Ethics

Several important dimensions underlie the ethical considerations inherent in conducting a scoping review on the economic evaluation of AI-enabled technologies in the healthcare sector. For us as researchers, ethical principles and guidelines are a cardinal element in the entirety of the review process.

As well, it is necessary to consider the effect of AI-enabled technologies on different stakeholders in the healthcare ecosystem. To understand the possible effects of these technologies on patient autonomy, privacy and equity in healthcare delivery, ethical scrutiny will be applied (Haugdahl et al. 2020). The economic aspects, including the risk of worsening health disparities or resource allocation issues will also be under scrutiny. Since this scoping review will be conducted following ethical standards, it will apply rigorous methodologies, transparent reporting, and commitment to delivering a thorough, impartial synthesis of the economic evidence. To ensure that the study findings and recommendations are credible, it is important to undergo ethical oversight and vigilance at each stage of the review process.

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References

Allemang, B., Sitter, K. and Dimitropoulos, G., 2022. Pragmatism as a paradigm for patient?oriented research. Health Expectations, 25(1), pp.38-47.

Boutillier, C., Archer, S., Barry, C., King, A., Mansfield, L. and Urch, C., 2019. A conceptual framework for living with and beyond cancer: A systematic review and narrative synthesis. Psycho?Oncology, 28(5), pp.948-959.

Garibay, O., Winslow, B., Andolina, S., Antona, M., Bodenschatz, A., Coursaris, C., Falco, G., Fiore, S.M., Garibay, I., Grieman, K. and Havens, J.C., 2023. Six human-centred artificial intelligence grand challenges. International Journal of Human-Computer Interaction, 39(3), pp.391-437.

Haugdahl, H.S., Sandsæter, H.L., Lysne, M., Bjerkeset, O., Uhrenfeldt, L. and Horn, J., 2020. Protocol: Postpartum lifestyle interventions among women with pre-eclampsia: a scoping review protocol. BMJ Open, 10(5).

Lockwood, C., Santos, K.B.D. and Pap, R., 2019. Practical guidance for knowledge synthesis: Scoping review methods. Asian nursing research, 13(5), pp.287-294.

Peters, M.D., Marnie, C., Colquhoun, H., Garritty, C.M., Hempel, S., Horsley, T., Langlois, E.V., Lillie, E., O’Brien, K.K., Tunçalp, ?. and Wilson, M.G., 2021. Scoping reviews: reinforcing and advancing the methodology and application. Systematic reviews, 10(1), pp.1-6.

Peters, M.D., Marnie, C., Tricco, A.C., Pollock, D., Munn, Z., Alexander, L., McInerney, P., Godfrey, C.M. and Khalil, H., 2021. Updated methodological guidance for the conduct of scoping reviews. JBI evidence implementation, 19(1), pp.3-10.

Ryder, M., Jacob, E. and Hendricks, J., 2019. An inductive qualitative approach to explore Nurse Practitioners views on leadership and research: An international perspective. Journal of Clinical Nursing, 28(13-14), pp.2644-2658.

Salameh, J.P., Bossuyt, P.M., McGrath, T.A., Thombs, B.D., Hyde, C.J., Macaskill, P., Deeks, J.J., Leeflang, M., Korevaar, D.A., Whiting, P. and Takwoingi, Y., 2020. Preferred reporting items for systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA): explanation, elaboration, and checklist. bmj, 370.

Suri, H., 2020. Ethical considerations of conducting systematic reviews in educational research. Systematic reviews in educational research: Methodology, perspectives and application, pp.41-54.

UK Parliament, 2020. AI and Healthcare. Available at: https://researchbriefings.files.parliament.uk/documents/POST-PN-0637/POST-PN-0637.pdf [Accessed 02 January 2024].

Vaismoradi, M. and Snelgrove, S., 2019. Theme in qualitative content analysis and thematic analysis.

Xie, Y., Gunasekeran, D.V., Balaskas, K., Keane, P.A., Sim, D.A., Bachmann, L.M., Macrae, C. and Ting, D.S., 2020. Health economic and safety considerations for artificial intelligence applications in diabetic retinopathy screening. Translational vision science & technology, 9(2), pp.22-22.