I’ve noticed that clinical research involves so many moving parts — from ethical approvals to data collection and analysis. Every step seems designed to minimize risk and bias. I get why this is important, but it also makes the process long and expensive.
Do you think there’s a way to simplify clinical research without compromising safety and data quality?
Clinical research in medical device development is complex because it involves multiple layers of scientific, ethical, and regulatory challenges. Each device must be tested rigorously to ensure it is safe and effective for human use, which requires carefully designed studies and extensive data collection. Researchers must also comply with strict ethical guidelines to protect participants’ rights and well-being. In addition, navigating the regulatory requirements from agencies such as the FDA or EMA can be time-consuming and costly. The integration of engineering, medicine, and data science adds another level of complexity, as each field has its own standards and methodologies. Ultimately, this complexity exists to ensure that new medical devices truly benefit patients while minimizing risks.
I agree with both of you that the complexity of clinical research makes everything more time-consuming and stems from the ethical and regulatory layers. After reading the clinical trial protocol that Dr. Simon provided, I can see how much coordination is needed. The IRB approval to informed consent to monitoring adverse events to statistical analysis. Each phase has its own regulations and system of checks to make sure the subjects are safe and the results are reliable. For example, in the lumbar fusion study, the subject can only get enrolled in the study after a strict screening, training, and IRB approval. All of these steps add time but are crucial to ensure accountability. If I were the patient, I would also want all these steps to be in play to ensure no unnecessary risk to my life and health.
However, with that said, I think some simplification is possible. With better data integration and the introduction of AI monitoring, data collection can become more streamlined, and ensuring that proper standards are followed will also become easier. Currently, data collection is not streamlined, and if there are deviations from the clinical study, then they have to be reported manually. However, with a secure digital infrastructure, reporting and management become automated, and the checking of these reports would be manual to ensure no issues.
However, there are issues with privacy and HIPAA with this, so making sure that all data is encrypted and following local and federal regulations is essential. Do you think clinical research will move toward this hybrid model with AI documentation and monitoring while the researchers focus more on the patient interaction? Or do you think AI is too dangerous to use in this sector?
