My career path would involve more prospective study designs especially in developing medical devices. The frequency of an endpoint in a group receiving procedure A is compared to that in a group not receiving procedure A in prospective comparative non-randomized studies. The investigator has no control over how many patients are divided among each cohort. Observed evidence is all that it is. This kind of research cannot establish causation regarding the superiority of one treatment over another and does not ensure that the two groups will be comparable on other factors, particularly factors that have not been evaluated. The only permitted finding is that there is a difference between the two therapies.

I have not considered whether there should be blinding. Do you think blinding should occur when testing medical devices?

@magan-rhodes I think blinding can occur when testing medical devices. Blinding can help lessen the bias in medical devices. If a doctor is not particularly fond of a medical device and observes adverse reactions, he could attribute those reactions to the medical device. Statisticians analyzing the data will present unbiased data that can be compared to other analyses. Blinding while testing medical devices can involve the patient, the doctor, data collectors, laboratory staff, clinical trial managers and more. I would like to research if a medical device underwent a blind study.

Although I am not interested in pursuing a career that involves clinical studies specifically, I think it would be really interesting to do a retrospective study on the same treatment in different countries (specifically between first and third world countries). I think it would be interesting to see the effects of obtaining medical treatment in a country with a developing economy vs. a country with a higher GDP. A potential study could be how cancer is dealt with. Even if the treatments are the same (chemotherapy, etc.), would there be a difference in death rate because of other conditions caused by the environment? This could be things such as the quality of water, food, improper sterilization, etc. The environment could have a big role to play when it comes to medical problems. I think it would be interesting to look at past data with different countries, and if there are noticeable differences, to find out why that is. Retrospective studies are almost like detective work, which I think could be an interesting topic to investigate.

While I understand Meta-analysis has issues involving the differences in methodologies, populations, or other variables in addition to the quality and acceptance for viable information, it does however represent a good role for post-market research's. Furthermore, the consolidated evidence generated through this method can be instrumental in informing evidence-based practices and influencing healthcare policies. This approach not only allows for a broader perspective on a specific topic but also facilitates the identification of patterns, trends, and potential variations across different studies. I personally am interested to identify statistical trends in BME, and thus realized this would be the most interesting/suited to my experiences. 

My aim is to look into the applications of artificial intelligence in radiology. Therefore, for my career path, the best type of clinical study would undoubtedly be a Retrospective Study. Radiology, as a field, has amassed a vast amount of imaging data over the years. This rich repository of historical data provides an invaluable resource for someone like me, who is keen on harnessing the power of artificial intelligence. By analyzing this existing data, I can train and validate AI algorithms without the need for new patient enrollments. This not only speeds up the research process but also ensures that the AI models are trained on diverse and extensive datasets, enhancing their accuracy and reliability. Moreover, a retrospective approach allows me to assess the historical accuracy and efficiency of radiological diagnoses. By comparing traditional diagnostic methods with AI-assisted ones using past data, I can highlight the areas where AI can bring about significant improvements. This is crucial for gaining the trust of the medical community and for showcasing the tangible benefits of integrating AI into radiology. In essence, while other types of clinical studies have their merits, a retrospective study aligns perfectly with my objectives. It provides a pragmatic and efficient way to delve into the applications of AI in radiology, leveraging the wealth of existing data to pave the way for future innovations.