AI Healthcare Insight

From overlooked imaging phenotypes to multimodal, human-guided precision diagnosis Author: Dr. SB Lee Introduction: When the Image Contains the Diagnosis but the Pattern Is Too Rare to Recognize A rare disease does not necessarily produce a rare image. That distinction may become one of the most important concepts in the future of Medical Imaging AI. A patient with an uncommon genetic, metabolic, neuromuscular, skeletal, vascular, or multisystem disorder may undergo the same computed tomography (CT), magnetic resonance imaging (MRI), ultrasound, radiography, or positron emission tomography (PET) examinations performed for thousands of other patients. The difference is that the diagnostic signal may be distributed across multiple organs, subtle in appearance, incompletely documented in the clinical history, or recognizable only when several seemingly unrelated findings are considered together. This is where the traditional diagnostic model becomes vulnerable. The radiologist may identi...

  AI-Powered Detection of Hidden Cardiac Anomalies By Dr. SB Lee Introduction Medical imaging AI has become increasingly effective at detecting abnormalities that clinicians specifically ask it to find. However, one of the more difficult challenges lies elsewhere: detecting clinically meaningful abnormalities that were never part of the original clinical question. A CT examination performed for suspected aortic disease, pulmonary embolism, abdominal pathology, or cancer staging may contain important information about many other anatomical structures. This creates a fundamental challenge for the next generation of medical AI: Can artificial intelligence identify an unexpected abnormality, determine whether it is real, characterize it anatomically, and help the radiologist decide whether it matters clinically? A rare congenital cardiac anomaly such as a quadricuspid pulmonary valve provides an excellent model for examining this problem. The objective is not simply to teach an AI syst...

  Key Concept: Healthcare Cybersecurity Risks in AI Systems Date: August 25, 2026 by Ph. D. Giljae Lee Artificial intelligence is becoming embedded in clinical decision-making, radiology workflows, patient communication, operational analytics, and medical-device software. Yet the security problem is changing faster than many hospital security programs. A conventional cyberattack attempts to steal information, disrupt availability, or obtain unauthorized access. An attack against an AI-enabled clinical system can do something more subtle: alter the information on which a clinical decision is based without obviously breaking the system itself . Consider an imaging department in which an AI algorithm analyzes CT studies for pulmonary embolism. The PACS remains available. The DICOM study opens normally. The algorithm returns a result. The radiologist sees the familiar interface. But if an attacker has manipulated the model, its input pipeline, model weights, inference environment, or...

  Key Concept: Generative AI in healthcare — August 25, 2026 by Ph. D. Giljae Lee Healthcare is entering an uncomfortable phase of artificial intelligence adoption. The question is no longer whether clinicians will encounter generative AI; they already do. The harder question is whether hospitals can integrate systems that generate text, recommendations, summaries, images, and workflow actions without creating a new layer of clinical risk . The distinction matters. A chatbot that drafts a discharge summary is fundamentally different from an AI system that synthesizes a patient's longitudinal record and proposes a diagnostic pathway. The first may save documentation time. The second can influence clinical reasoning. Physician adoption is accelerating. The American Medical Association reported that more than 80% of surveyed physicians were using AI professionally in 2026, with applications ranging from research summarization to documentation and patient communication. [1] Yet adopti...