Thats a good question. AI in clinical trials raises ethical concerns like bias in participant selection, opaque data analysis, and privacy risks. Bias can lead to unrepresentative trial groups, reducing the reliability of results, while opaque AI systems make findings hard to verify. Privacy issues arise from the need to manage large amounts of sensitive data, complicating informed consent. These challenges can undermine trust, reduce participation, and compromise the validity of results. Mitigating these issues requires diverse training data, explainable AI models, and strong privacy protections.

There are many ethical issues that could arise when using AI in clinical trials. When it comes to selection of participants, it is possible for AI systems to unintentionally show bias based on the data that was used to train them. An example of this is if the data used to teach the AI is not diverse, the AI may be more likely to choose similar demographics to what it was trained with when it is selecting participants. This bias can make the results of a study less applicable to the general population and break the trust of the participants if they perceive there is some level of bias in the selection. 

Another aspect of using AI in clinical trials that could be concerning is that it may not always be completely transparent in how data is analyzed. This could raise concerns to researchers about the reproducibility of the findings of the trial, which is extremely important in clinical trial. In general, people tend to trust human produced results and analysis more than data analyzed by AI, especially if they learn that the researchers are not sure exactly how the data was analyzed. 

Although AI has quite a lot of potential to benefit clinical trials, there are ethical questions it brings up as well. These include bias in AI models, the inability to adequately explain AI assessments to participants, the risks to privacy and security that come with handling sensitive medical data, the absence of accountability and transparency in AI systems, and concerns ensuring regulatory compliance. Optimizing AI's benefits while limiting its threats involves maintaining a balance between automation and human evaluation.