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There are several ethical concerns with the implementation of artificial intelligence $($AI$)$ and machine learning in healthcare. Two issues that immediately come to mind are the accuracy level and potential bias of AI and machine learning models. In several of my business analytics courses, we discussed the general ethical implications of AI and machine learning and it$$s always discussed how even the best algorithms make mistakes. Obviously humans also make mistakes, but with something as important as healthcare $($or autonomous vehicles and weaponized unmanned aerial vehicles as other examples$),$ it$$s often still important to have human judgement present. Although AI can be beneficial in quickly reviewing radiology images and producing detailed reports $($as one example$),$ there could be devastating consequences to the patient in the event the model makes a mistake that goes undetected $($Pesheva$,2023).$ Harvard researchers recently discovered that automated scoring systems rating the accuracy of these AI models $$failed to reliably identify clinical errors in the AI reports, some of them significant$$ and that $$The analysis showed that compared with human radiologists, automated scoring systems fared worse in their ability to evaluate the AI$-$generated reports. They misinterpreted and, in some cases, overlooked clinical errors made by the AI tool.$($Pesheva$,2023).$ Additionally, AI algorithms also have the potential to $$hallucinate$,$ or produce completely incorrect or nonsensical results by perceiving fictitious trends in the data used to train the model $($Hatem et al$.,2023).$ And depending on the dataset used to train the models, there can be significant bias which also impacts model performance $($Hatem et al$.,2023).$ While learning how to develop machine learning algorithms using R and Python, I was taught the importance of selecting a training dataset that$$s representative of the population the algorithm is intended to be deployed on$.$ For example, if a healthcare$-$related machine learning model is trained on a dataset that is $80\%$ female when the population is more of a $50/50$ split, the model may learn different trends and therefore produce misleading results when used on the population. Lastly, other ethical considerations especially in a healthcare setting include: patient privacy and data protection; informed consent on treatments$/$procedures$,$ programming errors, and data privacy; social inequalities; potential job displacement; and lack of empathy in AI applications $($Farhud & Zokaei, $2021).$

Overall, I view AI and machine learning more as tools to empower clinicians and nurses in better treating patients $$ not tools to replace them. However, healthcare workers need to be informed of the limitations of AI and machine learning algorithms in order to safely leverage these tools in a healthcare setting.