In a South African context, this innovation can help address health inequality particularly disparities in screening and treatment. However, this can only be achieved if it is applied in public sector. Public sector serves 71% of the South African population and is funded by the State. Public health care facilities in South Africa often grapple with constrained resources, significantly impacting access to quality care for many citizens. These limitations manifest as shortages in human resources, essential medications, and critical equipment. Additionally, concerns around waste management and infrastructure maintenance further exacerbate the situation.

This lack of resources disproportionately affects individuals from lower socioeconomic backgrounds who rely on these facilities due to limited financial means to access health care from private facilities. This creates a situation of limited distributive justice, where access to essential health care services is not equitably distributed amongst the population. This highlights the urgency for interventions that address these disparities and ensure that vulnerable populations have access to the quality care they deserve. The potential of AI in breast cancer imaging to improve patient outcomes through earlier diagnoses, personalised treatment plans, and ultimately, a reduction in breast cancer mortality rates is significant.

However, the technology’s current development and prevalence within private organisations raises concerns about affordability and equitable access, particularly in resource-constrained settings like South Africa. The cost of procuring such technology will most likely be significant, which will be a barrier for State funded facilities. 

While private facilities catering to a limited portion of the population, approximately 29%, may have the financial means to acquire this technology, a utilitarian perspective compels us to consider the potential for maximising overall benefit. In this case, the ethical principle of utilitarianism argues for prioritising broader accessibility to ensure the technology serves the greater good of the South African population. While private facilities may possess the financial means to acquire this technology, its true potential for good lies in serving the broader population. The ability to prevent deaths and improve countless lives through early detection far outweighs the benefits of a technology confined to the privileged few.

Should the technology be introduced in a South African context, and hopefully it will be, the conversation should be geared towards using this technology in the public sector to maximise its use. The technology promises accurate breast cancer image analysis with limited human assistance. This can be used in rural areas where there are no radiologists on site or to assist facilities burdened with large number caseloads and limited health care workers on site. The deployment of AI technology in public health facilities transcends mere economic considerations. It embodies the core principle of health care as a human right, enshrined in the South African constitution. Limiting access solely to those with financial means creates a stark ethical challenge, exacerbating existing health care disparities within a country already grappling with significant socioeconomic inequalities. By ensuring equitable access, we can harness the power of AI to create a more just and effective breast cancer screening system for all South Africans.

Furthermore, this necessitates exploring strategies to make AI-powered screening financially viable within the public health care system. This could involve public-private partnerships, exploring cost-effective implementation models, and potentially leveraging international collaborations to make this life-saving technology more readily available to all. While the potential of AI for breast cancer screening is undeniable, we must acknowledge the ethical challenges it presents.

One of the most concerning issues is bias. AI algorithms are trained on vast datasets, and if these datasets lack sufficient representation of African populations, it can lead to biased decision-making and poor clinical outcomes.  This has significant implications for accuracy. AI trained primarily on European or Western data may struggle to interpret mammograms or ultrasounds from individuals with different skin tones or breast tissue densities. Inaccurate readings could lead to missed diagnoses or unnecessary biopsies, posing a real health risk.

It is paramount to advocate for responsible development and implementation of AI in health care. This requires inclusive dataset to ensure the technology can be applicable to all individuals across the globe.

 Written by: Nomfundo Maseko

References:

Dlamini, Z., Molefi, T., Khanyile, R., Mkhabele, M., Damane, B., Kokoua, A., Bida, M., Saini, K.S., Chauke-Malinga, N., Luvhengo, T.E. and Hull, R., 2023. From Incidence to Intervention: A Comprehensive Look at Breast Cancer in South Africa. Oncology and Therapy, pp.1-11.

Image: https://www.biomedcentral.com/collections/spot-breast-cancer

The post AI for Breast Cancer Screening and Diagnosis first appeared on KHACA.

Conversely, this is sometimes not the case as seen in many instances such as with the: “Age-and education-related effects on cognitive functioning in Colored South African women” from Stellenbosch University’s “Intelligence & Slaves Exports from Africa” a collaboration between the University of Cape Town (UCT) & University of Kinshasa; and “Why are black South African students less likely to consider studying biological science” from UCT.

What could be considered unethical decision-making in research studies is also noted with international collaborations. For example, the collaboration between the Universities of Oxford and UCT for a drug trial on a TB vaccine using infants in SA as participants, where researchers of this study used a technique of ‘picking and mixing’, basically misrepresenting their research findings to report the desired result that will assist them in gaining access to desired research subjects or participants.

The above examples are only based on a few research studies; there are or may be many more if we were to intentionally look closely at research studies. Howbeit, there is not much work reported concerning the use (distributive justice) of innovations to be able to link them with unethical decisions (specifically) and how such decisions may have affected socio-economic values. Therefore, one can assume that unethical decisions are made in terms of how innovations from bedside to the market are made. Clearly, this may require research studies that are intentional in order to evaluate and indicate this. But in the absence of such, it is difficult to really say.

In addition, when looking at the level of reported corruption and cases of corruption in South Africa, Africa, one can only wonder how such corruption or corrupt acts have affected and/or influenced decision-making in fields such as science and innovation. Particularly if we were to look at the above-mentioned cases, what could be revealed if we were to weigh and/or compare the researchers or decision-makers actions to corruption? What impact (social and socio-economic) do we think these cases have on society at large and their contribution to public mistrust of science, apart from other societal and economic issues? Maybe once we start understanding and taking ethical decision-making very seriously and the impact it has on values (economic values being one of those values), we will begin to give this a priority in science and innovation.

Part of corruption is caused by the actions of a moral agent who acts unethically; this action is the making of a decision. Which is the same as what happened in those research cases and what could happen in research, development, and innovation. But why then don’t we see political parties and civil rights organisations fighting against such cases as they do with corruption? Is it because they don’t see unethical decision-making as corruption? Or because they don’t understand the impact it has—not only the moral impact but also the economic and research impacts? Perhaps this is because in many of the cases, they tend to fight for or highlight these unethical decisions, which are termed corruption and mainly reported in monetary values, which is an important factor that makes individuals and organisations see how much is being (mis)spent unlike in research and innovation. This could mean that if people were able to place a monetary value on unethical research and innovation, they would give this a priority as they do with other corruption cases. Therefore, we may need to change how we report unethical cases by indicating the monetary losses and the impact they have on socio-economic justice for us to take decision-making in research and development seriously. 

So how do we do that? We do that by attaching monetary values to our (un)ethical decisions. If we can do this, we will take ethical decision-making seriously, especially in science and innovation. The effect and impact that corruption has on society as well as the economy are the same as with unethical research studies and the unjust distributions and allocations of innovations and technologies from research. Not only that, but they also have other socio-economic effects for future research studies, caused by the buildup of mistrust that may result from these types of research studies. Therefore, it is imperative that ethical decision-making be considered a tool that can play a role in increasing and improving the economy and result in better socio-economic justice, equity, and equality.

Since unethical decision-making is not only a violation of human rights and dignity, but it also has a huge impact on socio-economic developments. History has shown us how research where participants are misled, mistreated, or their rights are violated has impacted their trust in science as well as their voluntary participation in research, even if it’s for their own gain. The same goes for innovation, but maybe not as evident as with research studies since many of the decisions made are based on policies and/or legislation. I am of the opinion that, the COVID-19 vaccines have shown us the importance of having an ethical decision-making framework regarding science and innovations. Since we now have cases of mistrust in new health technologies with all the misinformation shown through the newspapers, individual cases, and social media reports on these vaccines. Albeit, I am only assuming this based on the results or impact of what transpired during the COVID-19 vaccination rollout: that an ethical decision-making process for COVID-19 vaccines was not applied, or if it was, the consequences were not significant enough to indicate ethical decision-making considerations. However, decisions are made by an individual(s), which may affect the socioeconomic value to some degree and affect equality and social justice, irrespectively.

Therefore, how do we measure the link between ethical decision-making and economic value in science and innovation? How do we determine the impact that it has on society? Is it even necessary to measure the economic value of decision-making? How will the determined economic value influence ethical decision-making tools and/or frameworks? What values or ethical principles should we use to inform our decision-making? What economic theories can be used as well?

These and many more questions are needed to understand how (un)ethical decision-making can affect socio-economic justice, equity, and equality. Certainly, one thing is for sure: we need to start applying ethical decision-making to research and innovations for a better society and economic gain. But to also distinguish the monetary value of (un)ethical decision-making in science and innovation. 

 Written by: Fikile M. Mnisi, Ph.D

The post Considering Ethical Decision Making in Research and Innovation for Socio-Economic Justice and Equality first appeared on KHACA.