Leveraging Responsible, Explainable, and Local Artificial Intelligence Solutions for Clinical Public Health in the Global South

Kong, Jude Dzevela; Akpudo, Ugochukwu Ejike; Effoduh, Jake Okechukwu; Bragazzi, Nicola Luigi

doi:10.3390/healthcare11040457

Cited by 7 publications

(4 citation statements)

References 113 publications

(164 reference statements)

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“…Jude Kong shared the experience of the “Africa-Canada Artificial Intelligence and Data Innovation Consortium” during Covid-19 and how this can be leveraged and capitalized for other respiratory diseases ( https://acadic.org/covid-19-dashboards/ ) ( Kong et al., 2023 ; Stevenson et al., 2021 ). The consortium, established in 2020 with a focus on fostering knowledge sharing, operates across ten African countries.…”

Section: Use Case Of Early Warning Systemsmentioning

confidence: 99%

See 1 more Smart Citation

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

Nunes,

Thommes,

Fröhlich

et al. 2024

Infectious Disease Modelling

Self Cite

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Section: Use Case Of Early Warning Systemsmentioning

confidence: 99%

“…During the pandemic, the consortium devised a comprehensive modelling and forecasting strategy ( Kong et al., 2023 ). Their approach revolved around sourcing data from community-led organizations and partnering with telecommunications giants, Orange and MTN, to collect vital community-level information.…”

Section: Use Case Of Early Warning Systemsmentioning

confidence: 99%

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

Nunes,

Thommes,

Fröhlich

et al. 2024

Infectious Disease Modelling

Self Cite

View full text Add to dashboard Cite

“…The opacity of some AI models in how predictions are achieved (eg, in neural networks) is a common point of critique and reason for nonadoption. Explainable AIs enable public health experts, health care providers, and patients to understand how AI-based systems arrive at their decisions and recommendations [52][53][54][55][56][57]. This can help to address issues of bias and discrimination in health care and public health, which can arise when algorithms are based on flawed or incomplete data sets.…”

Section: Emerging Ai Trends and Their Possible Role In Digital Public...mentioning

confidence: 99%

Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities

Leal Neto,

Von Wyl

2024

JMIR Public Health Surveill

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The recent SARS-CoV-2 pandemic underscored the effectiveness and rapid deployment of digital public health interventions, notably the digital proximity tracing apps, leveraging Bluetooth capabilities to trace and notify users about potential infection exposures. Backed by renowned organizations such as the World Health Organization and the European Union, digital proximity tracings showcased the promise of digital public health. As the world pivots from pandemic responses, it becomes imperative to address noncommunicable diseases (NCDs) that account for a vast majority of health care expenses and premature disability-adjusted life years lost. The narrative of digital transformation in the realm of NCD public health is distinct from infectious diseases. Public health, with its multifaceted approach from disciplines such as medicine, epidemiology, and psychology, focuses on promoting healthy living and choices through functions categorized as “Assessment,” “Policy Development,” “Resource Allocation,” “Assurance,” and “Access.” The power of artificial intelligence (AI) in this digital transformation is noteworthy. AI can automate repetitive tasks, facilitating health care providers to prioritize personal interactions, especially those that cannot be digitalized like emotional support. Moreover, AI presents tools for individuals to be proactive in their health management. However, the human touch remains irreplaceable; AI serves as a companion guiding through the health care landscape. Digital evolution, while revolutionary, poses its own set of challenges. Issues of equity and access are at the forefront. Vulnerable populations, whether due to economic constraints, geographical barriers, or digital illiteracy, face the threat of being marginalized further. This transformation mandates an inclusive strategy, focusing on not amplifying existing health disparities but eliminating them. Population-level digital interventions in NCD prevention demand societal agreement. Policies, like smoking bans or sugar taxes, though effective, might affect those not directly benefiting. Hence, all involved parties, from policy makers to the public, should have a balanced perspective on the advantages, risks, and expenses of these digital shifts. For a successful digital shift in public health, especially concerning NCDs, AI’s potential to enhance efficiency, effectiveness, user experience, and equity—the “quadruple aim”—is undeniable. However, it is vital that AI-driven initiatives in public health domains remain purposeful, offering improvements without compromising other objectives. The broader success of digital public health hinges on transparent benchmarks and criteria, ensuring maximum benefits without sidelining minorities or vulnerable groups. Especially in population-centric decisions, like resource allocation, AI’s ability to avoid bias is paramount. Therefore, the continuous involvement of stakeholders, including patients and minority groups, remains pivotal in the progression of AI-integrated digital public health.

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“…The implementation of AI in Cameroon in the delivery of healthcare in Cameroon has witnessed several attempts particularly in the areas of disease detection, medical data management, drug discovery, and clinical decisionmaking [9]. For instance, the Cameroonian government launched a project in 2018 to develop a diagnostic tool driven by AI for spotting cervical cancer, one of the main causes of mortality for women in Cameroon [10]. Additionally, some firms like Gifted Mom and Waspito in Cameroon are employing AI chat bots to deliver telemedicine, health counseling, and education to underserved areas in Cameroon [11].…”

Section: Introductionmentioning

confidence: 99%

AI in the Management of HIV: Case Study Cameroon

ANGYIBA Serge,

NGNOTOUOM NGNOKAM Tania,

KOUDOM Patrice

et al. 2023

Int J Virol AIDS

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With the spread of HIV, the world is currently confronting an unprecedented problem. The application of artificial intelligence (AI) to the management of HIV is a hotly debated subject. Over 6,000 people are newly diagnosed with HIV each year in the United States, where an estimated 1.2 million people have the disease (CDC, 2020). HIV-positive individuals need ongoing medical care as their condition worsens in order to track the virus' development and ensure that the right therapy is being given. Recent studies have demonstrated that AI can increase the speed and accuracy of HIV detection. For instance, in a study by Northwestern University researchers, a deep learning algorithm was used to recognize HIV-positive individuals from a collection of medical pictures. The system outperformed manual diagnosis in properly identifying HIV-positive individuals with 94.2% accuracy (Kumar, et al., 2020). This shows that HIV diagnosis based on AI might be more accurate and effective. Improvements to patient-specific treatment strategies are also being made using AI. For instance, in a study by the University of California, San Francisco, tailored treatment regimens were created by analyzing the medical data of patients who tested positive for HIV using a machine learning algorithm. The algorithm was able to provide treatment plans that were more accurate than those produced using conventional techniques, indicating that treatment plans based on AI may be more efficient (Hernandez, et al., 2020). AI can also be used to monitor the development of the virus in people who are HIVpositive. To track the HIV virus in patients, for instance, researchers at Stanford University utilized an AI-based system. The algorithm was able to correctly forecast the virus' development, enabling medical professionals to more closely monitor the patient's condition and modify treatment regimens as necessary (Wang, et al., 2020). With quicker and more accurate diagnoses, better patient-specific treatment programs, and better tracking of the virus' course, AI has the potential to completely transform how HIV is controlled. According to recent study, AI may be a useful weapon in the battle against HIV, delivering more effective and efficient treatment for patients who are HIV-positive.

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Leveraging Responsible, Explainable, and Local Artificial Intelligence Solutions for Clinical Public Health in the Global South

Cited by 7 publications

References 113 publications

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report

Digital Transformation of Public Health for Noncommunicable Diseases: Narrative Viewpoint of Challenges and Opportunities

AI in the Management of HIV: Case Study Cameroon

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