We present an autonomous platform that extracts clinical images and confidential patient data from electronic databases, rigorously de-identifies them to meet HIPAA and other regulatory standards, and channels the data through advanced analytics tools. This system, initially created to deliver quantifications to support medical verdicts, needed specialist interpretation to become useful. The high level of automation of the data vehicle was pushed ahead not only for the necessity of having numbers that facilitate verdict creation, but also because confidentiality enforcement suggests the minimal necessary human intervention. Our last development involved the incorporation of large language models, and subsequently, the assisting vehicle evolved into a research-based knowledge generation tool, thereby significantly accelerating evidence-based discovery. The presented proof of concept demonstrates that knowledge generalization is feasible without human oversight, opening a controversial debate: Is AI in Clinical Research a menacing disruption or catalyst for cognitive breakthroughs? In this document, we extended the capabilities of the previously developed PACS vehicle to abandon the one-by-one working mechanisms and extract numbers from a population using two algorithms. The numbers are then sent to the LLM API system, which automatically generates a report, providing the vehicle with cognitive insights tailored to suit the needs of any audience.