Artificial intelligence in healthcare: Proposals for policy development in South Africa

Naidoo, Saloshni; Bottomley, D; Naidoo, Meshandren; Donnelly, Dusty‐Lee; Thaldar, D W

doi:10.7196/sajbl.2022.v15i1.797

Cited by 14 publications

(12 citation statements)

References 12 publications

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“…The adversarial nature of the approaches to liability outlined above may be counter-productive to the proper regulation of AI technology-at least during its nascent stage. Naidoo et al (2022) argue that instead of prioritising questions such as "Who acted?" and "Was the act wrongful?," which causes persons involved to be antagonistic and defensive, the focus should shift to (a) learning how to better use AI in healthcare, and to (b) actively developing guidelines for AI developers and healthcare professionals who are using AI systems.…”

Section: Reconciliationmentioning

confidence: 99%

Liability for harm caused by AI in healthcare: an overview of the core legal concepts

Bottomley,

Thaldar

2023

Front. Pharmacol.

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The integration of artificial intelligence (AI) into healthcare in Africa presents transformative opportunities but also raises profound legal challenges, especially concerning liability. As AI becomes more autonomous, determining who or what is responsible when things go wrong becomes ambiguous. This article aims to review the legal concepts relevant to the issue of liability for harm caused by AI in healthcare. While some suggest attributing legal personhood to AI as a potential solution, the feasibility of this remains controversial. The principal–agent relationship, where the physician is held responsible for AI decisions, risks reducing the adoption of AI tools due to potential liabilities. Similarly, using product law to establish liability is problematic because of the dynamic learning nature of AI, which deviates from static products. This fluidity complicates traditional definitions of product defects and, by extension, where responsibility lies. Exploring alternatives, risk-based determinations of liability, which focus on potential hazards rather than on specific fault assignments, emerges as a potential pathway. However, these, too, present challenges in assigning accountability. Strict liability has been proposed as another avenue. It can simplify the compensation process for victims by focusing on the harm rather than on the fault. Yet, concerns arise over the economic impact on stakeholders, the potential for unjust reputational damage, and the feasibility of a global application. Instead of approaches based on liability, reconciliation holds much promise to facilitate regulatory sandboxes. In conclusion, while the integration of AI systems into healthcare holds vast potential, it necessitates a re-evaluation of our legal frameworks. The central challenge is how to adapt traditional concepts of liability to the novel and unpredictable nature of AI—or to move away from liability towards reconciliation. Future discussions and research must navigate these complex waters and seek solutions that ensure both progress and protection.

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Section: Reconciliationmentioning

confidence: 99%

Liability for harm caused by AI in healthcare: an overview of the core legal concepts

Bottomley,

Thaldar

2023

Front. Pharmacol.

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“…A comparative review of waste management in these regions reveals that while the USA has made significant strides in AIdriven waste management, Africa faces challenges such as a lack of awareness, environmental legislation, and limited financial resources (Godfrey et al, 2020;Ferronato & Torretta, 2019;Arakpogun et al, 2021). The environmental impacts of mismanaged waste are pervasive worldwide, affecting marine litter, air, soil, and water contamination (Naidoo et al, 2022). In Africa, the change in consumption habits has led to increased waste generation, posing serious threats to humanity if not addressed sustainably (Ogutu & Kathambi, 2023).…”

Section: Lessons Learned and Global Implicationsmentioning

confidence: 99%

Ai-Driven Waste Management Systems: A Comparative Review of Innovations in the Usa and Africa

Zamathula Queen Sikhakhane Nwokediegwu,

Ejike David Ugwuanyi,

Michael Ayorinde Dada

et al. 2024

Eng. sci. technol. j.

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The burgeoning challenges of waste management have propelled the integration of artificial intelligence (AI) into waste management systems, aiming to enhance efficiency, sustainability, and environmental impact. This abstract delves into the comparative review of AI-driven waste management innovations in the USA and Africa, illuminating the divergent strategies employed to address distinct contextual demands. In the USA, where waste management infrastructures are relatively advanced, AI technologies are leveraged to optimize waste collection routes, automate sorting processes, and enhance recycling efficiency. Machine learning algorithms analyze historical data to predict waste generation patterns, enabling municipalities to allocate resources more effectively. Additionally, robotic sorting systems equipped with computer vision contribute to the accurate segregation of recyclables, reducing contamination and promoting a circular economy. Conversely, in Africa, where waste management infrastructures may be less developed, AI applications prioritize scalable and adaptable solutions. Mobile applications powered by AI facilitate crowd-sourced waste reporting, enabling citizens to actively participate in waste management efforts. Furthermore, sensor-equipped smart bins optimize collection routes in real-time, improving resource utilization. The emphasis on community engagement and decentralized solutions reflects the unique challenges and opportunities present in African waste management contexts. Despite these regional disparities, common themes emerge, such as the role of data analytics, automation, and community involvement in shaping effective waste management systems. The comparative analysis underscores the importance of tailoring AI-driven innovations to the specific socio-economic and infrastructural landscapes of each region. Ultimately, understanding the nuanced approaches in the USA and Africa can inform a more holistic and globally adaptable framework for AI-driven waste management systems. Keywords: Al, Waste Management, USA, Africa, Innovation, Sanitation.

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“…While general AI regulation remains necessary, it is also vital to address the use of and relationship between AI software as goods that can be sold and the patient as a consumer in respect of the AI product or a healthcare service provided using the AI. Traditional fault-based liability regimes are difficult to implement in relation to harm caused by AI technologies as healthcare practitioners are required to foresee an error and take reasonable steps to meet the required standard of care ( Donnelly, 2022 ; Naidoo et al, 2022 ). In other words, the law regards a doctor as negligent when they fail to act as a reasonable practitioner would have done in that branch of the profession.…”

Section: Introductionmentioning

confidence: 99%

Mapping the regulatory landscape of AI in healthcare in Africa

Townsend,

Sihlahla,

Naidoo

et al. 2023

Front. Pharmacol.

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Introduction: Artificial intelligence (AI)-enhanced technology has seen unprecedented expansion in the recent past. This growth brings with it huge opportunities for the positive transformation of the economy, business, healthcare, and society. However, a critical question is whether, and to what extent, regulatory measures and mechanisms have been implemented to safeguard its design, development, and deployment. This paper offers a scoping exercise that maps the regulatory landscape of AI in healthcare (including health research) in certain African countries.Methods: This research is conducted across 12 African countries: Botswana, Cameroon, The Gambia, Ghana, Kenya, Malawi, Nigeria, Rwanda, South Africa, Tanzania, Uganda, and Zimbabwe. As limited specific AI legislation is found in these African countries, and because AI is informed by ancillary regulatory frameworks, we include data protection, digital health, consumer protection, and intellectual property in our research. A scoping review method was applied with a manual search of digital libraries with search terms customised for each repository consisting of core search terms for the various topics, including, among others, “law,” “regulation,” “artificial intelligence,” “data protection,” “intellectual property,” and “digital health”.Results and discussion: Analysis of the data demonstrated that while in the African countries under investigation there is no sui generis AI regulation, recent developments were found in areas that inform AI adoption, including in digital health, data protection, consumer protection, and intellectual property. Our findings highlight the fragmentation of the African AI regulatory landscape and illustrate the importance of continued AI regulatory development to ensure that Africa is well positioned for future AI adoption in health.

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Artificial intelligence in healthcare: Proposals for policy development in South Africa

Cited by 14 publications

References 12 publications

Liability for harm caused by AI in healthcare: an overview of the core legal concepts

Liability for harm caused by AI in healthcare: an overview of the core legal concepts

Ai-Driven Waste Management Systems: A Comparative Review of Innovations in the Usa and Africa

Mapping the regulatory landscape of AI in healthcare in Africa

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