When Practising Doctors Label the Truth: Making Medical AI Robust, Traceable, and Regulator‑Ready

The hype surrounding medical AI often conjures images of powerful algorithms operating out of the clusters of a remote datacenter, but in practice, building clinically-viable technology is far more grounded. For too long, AI followed a linear path: tech firms gathered data, built algorithms, and forced them into clinical workflows. This “black box” approach led to a high rate of failure. Models were often brittle, non-compliant, and could not handle the messiness of real-world patient data. They struggled with fragmented records, nuanced longitudinal histories, or the subjectivity in radiology reports.

Today’s bottleneck isn’t compute but clinical validity and regulatory readiness. The missing piece was domain expertise - the intuitive knowledge gained over years of clinical practice. Increasingly, the most sophisticated AI models in life sciences today are emerging not from technologists but from a more surprising source: practicing doctors. They are shaping ground truth, design, validation, and supervision of the AI solutions.

Doctors in the AI Training Loop Medical data is messy, multimodal, and deeply context-dependent. A phrase in a physician’s note, the subtle contour of a lesion on an MRI, or the sequencing of clinical events over time can dramatically shift a diagnosis. Without expert annotation, models collapse. Healthcare professionals now step in to provide the precision AI needs. They structure imaging data, validate edge cases, and codify what “ground truth” means in a clinical context. In pharmacovigilance, adverse-event detection hinges on nuanced coding. For biomarkers, subtle image features or lab trends separate signals from noise. To scale this effort, the global fund of healthcare knowledge must be leveraged so that algorithms are free from bias. Providers bring domain judgment that prevents model drift and reduces downstream regulatory risk. Their involvement ensures that AI reflects not just abstract patterns but real-world diagnostic logic. A regulatory-first approach follows three steps:

• Design with clinical intent - annotation schemas harmonized to ontologies like SNOMED or MedDRA, mapped to regulatory endpoints.
• Annotate with traceability - AI-assisted pre-labels reviewed by clinicians, quality checkpoints, and structured metadata.
• Deliver with validation - datasets packaged with digital signatures, audit trails, benchmarks, and documentation fit for FDA/EMA review.

The Scale Problem

Yet physician involvement creates a paradox. Their time is scarce, and diverting them from patient care is unsustainable. The solution is borrowed from the clinical world of medicine, wherein physician-extenders multiply the reach of experts. To ensure data quality is not sacrificed, this industrialized approach to large-scale knowledge capture requires multiple teams working together with specialized tools to securely manage the dataflow. Tasks are evaluated by complexity, signal to noise ratio, cognitive load, and subjectivity and workflows are tested and designed to maximize efficiency and quality. This balance is achieved through structured, tiered pipelines. For example, iMerit may integrate Radiology subspecialists with primary care providers and validate through a dual-shore process to meet US FDA requirements. The result is accuracy anchored in medical expertise without creating bottlenecks.

Building Regulatory-Ready AI

Creating one expert-validated dataset is doable. Scaling it for global trials or pharmacovigilance is harder. In life sciences, “good enough” data won’t do - better models emerge right after launch. AI for drug development or patient care must withstand FDA/EMA scrutiny, demanding clinically precise, fully traceable pipelines backed by strong infrastructure. For AI to be deployed in a clinical setting, it must meet stringent regulatory standards. At iMerit, workfl ows are designed around GxP alignment, HIPAA compliance, and 21 CFR Part 11 requirements. Each project ensures data with audit trails and quality matching the standard of care, so models entering regulatory review are as defensible as the science itself. This lowers risk, shortens timelines, and gives pharma and med-tech confi dence to move AI from research to deployment without regulatory setbacks.

Real-World Applications

The impact of doctor-led data intelligence is already visible across domains:

• Ambient Scribe Technology: Data enrichment and summarization to expand functionality, broaden into subspecialities, and drive patient engagement.
• Medical imaging in drug development: Expert-guided annotation of MRI, CT, and pathology images enables AI to identify biomarkers and assess treatment response.
• Pharmacovigilance: Doctors and annotators together map adverse drug reactions from unstructured narratives into MedDRA codes with contextual detail - boosting precision in safety monitoring.
• Biomarker discovery: Multimodal pipelines integrate histopathology, genomics, and clinical data, with physicians ensuring alignment across modalities.

The Strategic Lesson

Life-sciences leaders must invest early in domain-led annotation and regulatory pipelines. It cuts timelines, avoids rework, and ensures clinical validity. Experts in the loop aren’t optional, they are essential. This isn’t outsourcing medicine to machines; it’s teaching machines medicine.

About Sina Bari: Sina Bari MD is a leading medical device and information technology innovator. His knowledge and expertise are well-respected across the industry as a considered voice in the development of Medical Artifi cial Intelligence. Sina completed his undergraduate training at Stanford University, where he studied Electrical Engineering and Medicine. He then completed his Plastic and Reconstructive Surgery residency at Yale University School of Medicine. While there, he also completed a fellowship in Microsurgery and Hand Reconstruction. He has been recognized with numerous awards, including the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Barack Obama in 2016. Sina is the AVP at iMerit’s MedAI division and heading the healthcare vertical.