Ethical Argument Essay: The Ethics of AI In HealthCare, A Mapping Review

 

With increasing evidence of the efficacy of artificial intelligence (AI) and its impact on healthcare, the following question is presented: if AI solutions are to be embedded in clinical practice, then how will we protect people from harm arising from unethical conduct? Furthermore, does AI have the capability of making an ethical decision? In this literature review, a mapping method was used to find literature across disciplinary boundaries that shed light on ethical issues unique to the use of AI algorithms in healthcare to address the questions previously stated.

AI can be defined as “an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human (Morley et al., 2020). An example of this is mapped by “decision tree” techniques (Harerimana et al., 2018). These techniques can be used to diagnose breast cancer tumors (Kuo et al., 2001), diagnose diabetes mellitus (Barakat et al., 2010); ensemble learning methods that can predict outcomes for cancer patients (Kourou et al., 2015), assist with drug discovery (Machancoses et al., 2018), and epidemiology (Hay et al., 2013).

As healthcare systems around the world are struggling with increasing costs and worsening outcomes (Topol, 2019), policymakers, politicians, clinical entrepreneurs, computer and data scientists argue that AI will be a part of the solution to this problem (Taddeo and Floridi, 2018) which is why AI is a is relevant topic today.

While the benefits of AI seem enticing, there is an ethical dilemma. The dilemma presented is not the cliche, “AI’s will replace clinicians”. It is instead, “how do we know if AI’s can make decisions that protect people from harm arising from unethical conduct?”

I would argue that even though AI is a new implementation in healthcare, on the ground of the ethical principle of beneficence and nonmaleficence, I am in support of this possibility. Beneficence and nonmaleficence are defined as acting to benefit patients and not to harm them (Jonsen, et al., 2015). In medical ethics, this means “the duty to try to bring about those improvements in physical or psychological health that medicine can achieve. These objective effects of diagnostic and therapeutic actions are, for example, diagnosing and curing an infection, treating cancer that leads to remission, facilitating the healing of a fracture” (Jonsen et al., 2015). According to the previous studies mentioned, AI may address those issues which leads me to believe AI in medicine is in fact ethical.

It seems we have to wait until AI is more common in practice for other dilemmas to present themselves as well as make improvements. However, even current FDA-approved medications and treatments have pros and cons. I believe AI would be no different as long as the patient benefit outweighs the risk.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

References

 

 

 

Álvarez-Machancoses, Ó., Fernández-Martínez, J.L., 2019.

Using artificial intelligence methods to speed up drug discovery. Expert Opin. Drug Discov. 14 (8), 769–777. https://doi.org/10.1080/17460441.2019.1621284.

Harerimana, G., Jang, B., Kim, J.W., Park, H.K., 2018.

Health big data analytics: a technology survey. IEEE Access 6, 65661–65678. https://doi.org/10.1109/ACCESS. 2018.2878254

Hay, S.I., George, D.B., Moyes, C.L., Brownstein, J.S., 2013.

Big Data Opportunities for Global Infectious Disease Surveillance.

Jonsen A.R., & Siegler M, & Winslade W.J.(Eds.), (2015)

Clinical Ethics: A Practical Approach to Ethical Decisions in Clinical Medicine, 8e. McGraw Hill. https://accessmedicine-mhmedical-com.york.ezproxy.cuny.edu/content.aspx?bookid=1521&sectionid=88812074

Kuo, W.-J., Chang, R.-F., Chen, D.-R., Lee, C.C., 2001.

Data mining with decision trees for diagnosis of breast tumor in medical ultrasonic images. Breast Canc. Res. Treat. 66 (1), 51–57. https://doi.org/10.1023/A:1010676701382.

Morley, J., Machado, C., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020).

The ethics of AI in health care: A mapping review. Social science & medicine (1982),     260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172

 

Taddeo, M., Floridi, L., 2018. How AI can be a force for good.

Science 361 (6404), 751–752. https://doi.org/10.1126/science.aat5991.

Topol, E.J., 2019. High-performance medicine: the convergence of human and artificial

intelligence. Nat. Med. 25 (1), 44–56. https://doi.org/10.1038/s41591-018-0300-7.