When a lab report screenshot included a dismissive note about "hemolysis," both human doctors and a vision-enabled AI made the same mistake of ignoring a critical data point. This highlights how AI can inherit human biases embedded in data presentation, underscoring the need to test models with varied information formats.

The lack of a placebo arm in some adjuvant trials is not necessarily a fatal flaw. One expert view is that it mirrors real-world practice where treatments are known. This perspective places trust in the investigators' ability to assess disease progression accurately without blinding.

Text descriptions of physical pain are often vague. To improve an AI coach's helpfulness, use multi-modal inputs. Uploading a photo and circling the exact point of pain or a video showing limited range of motion provides far more precise context than words alone.

An effective AI strategy in healthcare is not limited to consumer-facing assistants. A critical focus is building tools to augment the clinicians themselves. An AI 'assistant' for doctors to surface information and guide decisions scales expertise and improves care quality from the inside out.

Data from the CAPItello trial showed a significant number of patients with PTEN deficiency experienced radiological progression without a corresponding PSA increase. This challenges the standard reliance on PSA for monitoring in high-risk prostate cancer and suggests a need for more frequent, personalized imaging protocols to detect progression earlier.

AI identified circulating tumor DNA (ctDNA) testing as a highly sensitive method for detecting cancer recurrence earlier than scans or symptoms. Despite skepticism from oncologists who deemed it unproven, the speaker plans to use it for proactive monitoring—a strategy he would not have known about otherwise.

Medicine excels at following standardized algorithms for acute issues like heart attacks but struggles with complex, multifactorial illnesses that lack a clear diagnostic path. This systemic design, not just individual doctors, is why complex patients often feel lost.

The Rampart study's use of the Leibovic score for risk stratification is a key strength. Unlike traditional TNM staging, this score more heavily weights tumor grade, which clinicians find to be a more granular and clinically relevant predictor of recurrence risk than just tumor size.

By continuously feeding lab results and treatment updates into GPT-5 Pro, the speaker created an AI companion to validate the medical team's decisions. This not only caught minor discrepancies but, more importantly, provided immense peace of mind that the care being administered was indeed state-of-the-art.