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Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries.

ABSTRACT: Digital Pathology (DP) and Artificial Intelligence (AI) can be useful in low- and middle-income countries; however, many challenges exist. The United Nations developed sustainable development goals that aim to overcome some of these challenges. The sustainable development goals have not been applied to DP/AI applications in low- to middle income countries. We established a framework to align the 17 sustainable development goals with a 27-indicator list for low- and middle-income countries (World Bank/WHO) and a list of 21 essential elements for DP/AI. After categorization into three domains (human factors, IT/electronics, and materials + reagents), we permutated these layers into 153 concatenated statements for prioritization on a four-tiered scale. The two authors tested the subjective ranking framework and endpoints included ranked sum scores and visualization across the three layers. The authors assigned 364 points with 1.1-1.3 points per statement. We noted the prioritization of human factors (43%) at the indicator layer whereas IT/electronic (36%) and human factors (35%) scored highest at the essential elements layer. The authors considered goal 9 (industry, innovation, and infrastructure; average points 2.33; sum 42), goal 4 (quality education; 2.17; 39), and goal 8 (decent work and economic growth; 2.11; 38) most relevant; intra-/inter-rater variability assessment after a 3-month-washout period confirmed these findings. The established framework allows individual stakeholders to capture the relative importance of sustainable development goals for overcoming limitations to a specific problem. The framework can be used to raise awareness and help identify synergies between large-scale global objectives and solutions in resource-limited settings.

SUBMITTER: Piya S

PROVIDER: S-EPMC10225661 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature

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Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries.

Piya Sumi S Lennerz Jochen K JK

Frontiers in medicine 20230515

Digital Pathology (DP) and Artificial Intelligence (AI) can be useful in low- and middle-income countries; however, many challenges exist. The United Nations developed sustainable development goals that aim to overcome some of these challenges. The sustainable development goals have not been applied to DP/AI applications in low- to middle income countries. We established a framework to align the 17 sustainable development goals with a 27-indicator list for low- and mi ...[more]

PMID: 37256085

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Project description:BackgroundThe ambitious development agenda of the Sustainable Development Goals (SDGs) requires substantial investments across several sectors, including for SDG 3 (healthy lives and wellbeing). No estimates of the additional resources needed to strengthen comprehensive health service delivery towards the attainment of SDG 3 and universal health coverage in low-income and middle-income countries have been published.MethodsWe developed a framework for health systems strengthening, within which population-level and individual-level health service coverage is gradually scaled up over time. We developed projections for 67 low-income and middle-income countries from 2016 to 2030, representing 95% of the total population in low-income and middle-income countries. We considered four service delivery platforms, and modelled two scenarios with differing levels of ambition: a progress scenario, in which countries' advancement towards global targets is constrained by their health system's assumed absorptive capacity, and an ambitious scenario, in which most countries attain the global targets. We estimated the associated costs and health effects, including reduced prevalence of illness, lives saved, and increases in life expectancy. We projected available funding by country and year, taking into account economic growth and anticipated allocation towards the health sector, to allow for an analysis of affordability and financial sustainability.FindingsWe estimate that an additional $274 billion spending on health is needed per year by 2030 to make progress towards the SDG 3 targets (progress scenario), whereas US$371 billion would be needed to reach health system targets in the ambitious scenario-the equivalent of an additional $41 (range 15-102) or $58 (22-167) per person, respectively, by the final years of scale-up. In the ambitious scenario, total health-care spending would increase to a population-weighted mean of $271 per person (range 74-984) across country contexts, and the share of gross domestic product spent on health would increase to a mean of 7·5% (2·1-20·5). Around 75% of costs are for health systems, with health workforce and infrastructure (including medical equipment) as the main cost drivers. Despite projected increases in health spending, a financing gap of $20-54 billion per year is projected. Should funds be made available and used as planned, the ambitious scenario would save 97 million lives and significantly increase life expectancy by 3·1-8·4 years, depending on the country profile.InterpretationAll countries will need to strengthen investments in health systems to expand service provision in order to reach SDG 3 health targets, but even the poorest can reach some level of universality. In view of anticipated resource constraints, each country will need to prioritise equitably, plan strategically, and cost realistically its own path towards SDG 3 and universal health coverage.FundingWHO.

Project description:ObjectiveScalp electroencephalograms (EEGs) are critical for neurological evaluations, particularly in epilepsy, yet they demand specialized expertise that is often lacking in many regions. Artificial intelligence (AI) offers potential solutions to this gap. While existing AI models address certain aspects of EEG analysis, a fully automated system for routine EEG interpretation is required for effective epilepsy management and healthcare professionals' decision-making. This study aims to develop an AI-augmented model for automating EEG seizure tracking, thereby supporting a sustainable digital care pathway for epilepsy (DCPE). The goal is to improve patient monitoring, facilitate collaborative decision-making, ensure timely medication adherence, and promote patient compliance.MethodThe study proposes an AI-augmented framework using machine learning, focusing on quantitative analysis of EEG data to automate DCPE. A focus group discussion was conducted with healthcare professionals to find the problem of the current digital care pathway and assess the feasibility, usability, and sustainability of the AI-augmented system in the digital care pathway.ResultsThe study found that a combination of random forest with principal component analysis and support vector machines with KBest feature selection achieved high accuracy rates of 96.52% and 95.28%, respectively. Additionally, the convolutional neural networks model outperformed other deep learning algorithms with an accuracy of 97.65%. The focus group discussion revealed that automating the diagnostic process in digital care pathway could reduce the time needed to diagnose epilepsy. However, the sustainability of the AI-integrated framework depends on factors such as technological infrastructure, skilled personnel, training programs, patient digital literacy, financial resources, and regulatory compliance.ConclusionThe proposed AI-augmented system could enhance epilepsy management by optimizing seizure tracking accuracy, improving monitoring and timely interventions, facilitating collaborative decision-making, and promoting patient-centered care, thereby making the digital care pathway more sustainable.

Dataset Information

Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries.

Publications

Sustainable development goals applied to digital pathology and artificial intelligence applications in low- to middle-income countries.

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