Raw LLM Responses

There are four pillars of medical ethics including beneficence (doing good), non-maleficence (to do no harm), autonomy (giving the patient freedom to choose freely where they are able), and finally, justice (ensuring fairness). This Ted Talk is extremely compelling, because it touches on the potential for early cancer diagnosis using AI software. The speaker expresses the safety of implementing these practices into a clinical setting. However, what stuck out to me after listening to this was the pillar of beneficence. I feel this would have a great impact on health care beneficence, and the effort of medical personnel to do good for their patients, however my concern is the nature of blending physical human being with AI technology when it comes to diagnostic measures. If we are to implement this technology for early detection, do we have the correct treatment to tend to our patients in that stage of their progression, especially if their symptoms are undetectable clinically at the time of early detection. Additionally, justice comes to mind. This has the potential to be a great asset and advocate for justice in medicine, or the potential to do the opposite. Who would have access to this type of technology? Who is to say that insurance companies would not gain this type of knowledge and hold it against their patients, especially if there are patients in an economic situation by which they are unable to seek expensive treatment at the time. I do believe this model has great potential to help seek answers for patients and guide physicians in diagnoses that are early and accurate. However, I feel the emerging regulations the speaker talks about are important components to be considered with this technology being used regularly in practice. A specifically important topic touched on is the justice aspect with regards to the availability of the technology to recognize different skin colors and ethnicities and their respective presentations. Preventing biases within the technology is crucial in providing care that is inclusive to all patients. It is comforting to hear this model is capable of learning and adapting, and that the speaker is aware that making sure a diverse range of patients can be served equally by this technology is prioritized moving forward.