Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries

Alami, Hassane; Rivard, Lysanne; Lehoux, Pascale; Hoffman, Steven J.; Cadeddu, Stéphanie B.M.; Savoldelli, Mathilde; Samri, M.; Ahmed, Mohamed Ali Ag; Fleet, Richard; Fortin, Jean‐Paul

doi:10.1186/s12992-020-00584-1

Cited by 119 publications

(96 citation statements)

References 44 publications

(63 reference statements)

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“…While evidence suggests that global health capacity building initiatives are beneficial, little is known about their success in the MENA region and especially regarding women’s health in refugee populations, given the scarcity of case studies on this issue [ 13 ]. In fact, based on a recent systematic review, there is an increasing need for data on the effectiveness of capacity building programs that target CHWs, given their potential to provide non-specialized and essential services in low-resource settings [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating a capacity building program on women’s health for displaced community health workers in fragile settings in Lebanon

et al. 2021

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Background Displaced populations in fragile settings experience health disparities that are seldom attended to. Task-shifting, which involves training non-specialized community health workers (CHW) to deliver basic education and health services is a favorable strategy to address this problem, however very little data exist on this topic in the Middle East region. We conducted a long-term evaluation of the Women’s Health Certificate delivered to Syrian refugees and host community in informal tented settlements in Lebanon under the Mobile University for Health (MUH) program. The training was delivered through a mobile classroom approach that incorporated a blended learning modality. Methods We collected short-term data from the 42 trained CHW (knowledge assessments and satisfaction measures) during the delivery of the intervention between March and August 2019, and long-term data (semi-structured interviews with 8 CHW and focus group discussion with 9 randomly selected community members) one year later between July and August 2020. The evaluation approach was informed by the Kirkpatrick evaluation model, and the qualitative data were analyzed using qualitative content analysis. Results Data from the CHWs and community members were triangulated, and they showed that the training enhanced access to education due to its mobile nature and provided opportunities for CHWs to engage and interact with learning material that enhanced their knowledge and favorable behaviors regarding women’s health. In turn, CHWs were empowered to play an active role in their communities to transfer the knowledge they gained through educating community members and providing women’s health services and referrals. Community members benefited from the CHWs and called for the implementation of more similar training programs. Conclusion This is one of few studies reporting a long-term community-level evaluation of a task-shifting program on women’s health among displaced populations in Lebanon. Our findings support the need to increase funding for similar programs, and to focus on delivering programs for a variety of health challenges. It is also essential to enhance the reach and length of recruitment to wider communities, to design concise, interactive, and engaging sessions, and to provide tools to facilitate circulation of learning material, and resources for referrals to health services.

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Section: Introductionmentioning

confidence: 99%

Evaluating a capacity building program on women’s health for displaced community health workers in fragile settings in Lebanon

et al. 2021

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“…Considering this, there is a need for public health to adopt greater vigilance to better choose the innovations that are most likely to sustainably improve the health of populations, including the most vulnerable, and planetary health. Global public health must then ensure that the resources used serve to obtain the best possible result at the individual and collective levels [50].…”

Section: Confinement In Technological Determinism: Technical Innovatimentioning

confidence: 99%

“…In addition, public health responses, such as social distancing or the closure of schools or non-essential services, can generate negative impacts on psychosocial health, social isolation, family relationships, behavior related to health, and education among the most socially and materially disadvantaged [66]. In this perspective, some authors have criticized the fact that artificial intelligence has been limited only in its technical possibilities in public health [50,67]. As a result of this simplistic vision, its utilization has not taken into account questions related to health equity and how social determinants will be addressed [50,67,68].…”

Section: Equity Issues Facing Market Lawsmentioning

confidence: 99%

“…In this perspective, some authors have criticized the fact that artificial intelligence has been limited only in its technical possibilities in public health [50,67]. As a result of this simplistic vision, its utilization has not taken into account questions related to health equity and how social determinants will be addressed [50,67,68].…”

Section: Equity Issues Facing Market Lawsmentioning

confidence: 99%

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Are Health Innovations Techno-economic or Social Values in a period of a Health Crisis? A Reflection on the Field of Public Health through the Examples of Ebola and Covid-19

Niang¹,

Dupéré²,

Alami³

et al. 2020

Preprint

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Health innovations are generally oriented on a techno-economic vision. In this perspective, technologies are seen as an end in themselves, and there is no arrangement between the technical and the social values of innovation. This vision prevails in sanitary crises, in which management is carried out based on the search for punctual, reactive, and technical solutions to remedy a specific problem without a systemic/holistic, sustainable, or proactive approach. This paper attempts to contribute to the literature on the epistemological orientation of innovations in the field of public health. Taking the Covid-19 and Ebola crises as examples, the primary objective is to show how innovation in health is oriented towards a techno-economic paradigm. Second, we propose a repositioning of public health innovation towards a social paradigm that will put more emphasis on the interaction between social and health dimensions in the perspective of social change.

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“…AI use in LMIC can help in eradicating health inequalities and reduce the burden on the health care systems. 13 AI-based tools are already being explored to be used in LMIC, to ease the burden on resource-limited settings to improve outcomes.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review

Naseem

Akhund

Arshad

et al. 2020

J Prim Care Community Health

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Background: In the face of the current time-sensitive COVID-19 pandemic, the limited capacity of healthcare systems resulted in an emerging need to develop newer methods to control the spread of the pandemic. Artificial Intelligence (AI), and Machine Learning (ML) have a vast potential to exponentially optimize health care research. The use of AI-driven tools in LMIC can help in eradicating health inequalities and decrease the burden on health systems. Methods: The literature search for this Scoping review was conducted through the PubMed database using keywords: COVID-19, Artificial Intelligence (AI), Machine Learning (ML), and Low Middle-Income Countries (LMIC). Forty-three articles were identified and screened for eligibility and 13 were included in the final review. All the items of this Scoping review are reported using guidelines for PRISMA extension for scoping reviews (PRISMA-ScR). Results: Results were synthesized and reported under 4 themes. (a) The need of AI during this pandemic: AI can assist to increase the speed and accuracy of identification of cases and through data mining to deal with the health crisis efficiently, (b) Utility of AI in COVID-19 screening, contact tracing, and diagnosis: Efficacy for virus detection can a be increased by deploying the smart city data network using terminal tracking system along-with prediction of future outbreaks, (c) Use of AI in COVID-19 patient monitoring and drug development: A Deep learning system provides valuable information regarding protein structures associated with COVID-19 which could be utilized for vaccine formulation, and (d) AI beyond COVID-19 and opportunities for Low-Middle Income Countries (LMIC): There is a lack of financial, material, and human resources in LMIC, AI can minimize the workload on human labor and help in analyzing vast medical data, potentiating predictive and preventive healthcare. Conclusion: AI-based tools can be a game-changer for diagnosis, treatment, and management of COVID-19 patients with the potential to reshape the future of healthcare in LMIC.

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Artificial intelligence in health care: laying the Foundation for Responsible, sustainable, and inclusive innovation in low- and middle-income countries

Cited by 119 publications

References 44 publications

Evaluating a capacity building program on women’s health for displaced community health workers in fragile settings in Lebanon

Evaluating a capacity building program on women’s health for displaced community health workers in fragile settings in Lebanon

Are Health Innovations Techno-economic or Social Values in a period of a Health Crisis? A Reflection on the Field of Public Health through the Examples of Ebola and Covid-19

Exploring the Potential of Artificial Intelligence and Machine Learning to Combat COVID-19 and Existing Opportunities for LMIC: A Scoping Review

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