AI-Assisted Diagnostic Support

What You'll Learn

In this lesson, you will learn how AI is being used to support — not replace — clinical diagnosis. You will explore AI applications in medical imaging, laboratory analysis, and differential diagnosis generation. You will also learn how to critically evaluate AI diagnostic suggestions and understand when AI performs well and when it falls short.

AI in Medical Imaging

Medical imaging is where AI has made the most measurable clinical impact. The reason is straightforward: images are data-rich, patterns can be subtle, and the volume of scans continues to grow faster than the radiology workforce.

How AI Imaging Tools Work

AI imaging models are typically trained on millions of labeled images. For example, a chest X-ray AI might be trained on datasets where radiologists have annotated findings like nodules, pneumothorax, or cardiomegaly. The model learns to recognize these patterns and can then flag them in new images.

These tools do not make diagnoses. They generate alerts and highlight regions of interest for the radiologist to review.

FDA-Cleared Imaging AI

As of 2025, the FDA has cleared over 900 AI-enabled medical devices, with radiology comprising the largest category. Key examples include:

• Viz.ai LVO Detection — Analyzes CT angiography images and alerts stroke teams when it detects a large vessel occlusion. Studies show it reduces door-to-groin-puncture time by 20-30 minutes — a difference that directly impacts patient outcomes in stroke care.
• Aidoc — Flags critical findings on CT scans including pulmonary embolism, cervical spine fractures, and intracranial hemorrhages. It reprioritizes the radiologist's worklist so urgent cases are read first.
• iCAD ProFound AI — Assists with breast cancer detection in mammography and digital breast tomosynthesis. Studies show it can reduce false negatives while maintaining specificity.
• IDx-DR (Digital Diagnostics) — An autonomous AI system that detects diabetic retinopathy from retinal images. Notably, it is one of the few AI systems authorized to make a screening decision without a specialist review, designed for use in primary care settings.

Pathology and Dermatology

AI is also advancing in pathology, where deep learning models analyze tissue slides to identify cancerous cells, and in dermatology, where smartphone-based AI can assess skin lesions. However, these tools are generally less mature than radiology AI and should be used with appropriate caution.

AI for Differential Diagnosis

Large language models are increasingly capable of generating differential diagnoses when given a clinical scenario. Research published in journals including JAMA Internal Medicine and Nature Medicine has shown that models like GPT-4 can generate reasonable differential diagnosis lists that include the correct diagnosis at rates comparable to, and sometimes exceeding, physician-generated lists in controlled studies.

How to Use LLMs for Diagnostic Reasoning

If your organization permits it, you can use AI as a "thinking partner" for complex cases:

1. Present the clinical scenario — Describe the patient's symptoms, history, lab results, and imaging findings (without PHI in non-compliant tools, or using de-identified information).
2. Ask for a differential diagnosis — The AI will generate a ranked list of possibilities.
3. Challenge the AI — Ask it to explain its reasoning, consider alternative diagnoses, or account for additional findings.
4. Use it as a checklist — The primary value is ensuring you have not overlooked a diagnosis, especially for rare conditions.

Important Limitations

• AI can hallucinate medical information. It may cite studies that do not exist or describe diagnostic criteria incorrectly. Always verify AI suggestions against trusted sources like UpToDate, DynaMed, or primary literature.
• AI does not examine patients. It cannot assess a patient's appearance, palpate an abdomen, or detect the subtle cues that experienced clinicians pick up during a physical examination.
• AI lacks longitudinal context. It does not know your patient's history, social circumstances, preferences, or the nuances of their presentation over time.
• Performance varies by condition. AI performs better for common conditions with clear diagnostic criteria and less well for rare diseases, atypical presentations, and multisystem disorders.

AI in Laboratory Medicine

AI is also supporting diagnostics through laboratory analysis:

• Automated blood smear analysis — AI can classify blood cells and flag abnormalities, reducing manual microscopy time.
• Sepsis prediction models — Algorithms analyze vital signs, lab values, and nursing notes in real time to predict sepsis hours before clinical recognition.
• Genomic analysis — AI accelerates the interpretation of genetic sequencing data, helping identify pathogenic variants more quickly.

The Radiologist's Perspective

It is worth addressing a common misconception: AI is not replacing radiologists. The workload in radiology has increased dramatically — imaging volume grows 5-8% annually while the workforce grows 1-2%. AI helps radiologists manage this volume by handling routine screening and flagging critical findings, allowing them to focus their expertise on complex cases and clinical consultations.

The same principle applies across specialties. AI handles pattern recognition at scale; clinicians provide judgment, context, and the human elements of care.

Responsible Use in Practice

When using AI for diagnostic support:

• Never rely solely on AI for a diagnosis. It is one input among many.
• Document your clinical reasoning. If an AI tool contributed to your diagnostic workup, note it appropriately.
• Stay current. AI diagnostic tools are updated regularly. Make sure you understand the current version's capabilities and limitations.
• Report errors. If an AI tool misses a finding or generates a false positive, report it through your organization's channels. This feedback improves the systems over time.

Key Takeaways

• AI imaging tools flag abnormalities and prioritize worklists but do not make diagnoses — radiologists and clinicians provide the final interpretation
• Over 900 FDA-cleared AI medical devices exist, with radiology leading in adoption and clinical impact
• LLMs can generate useful differential diagnosis lists but may hallucinate medical information and lack the context of a physical examination
• AI in laboratory medicine supports blood analysis, sepsis prediction, and genomic interpretation
• Always treat AI diagnostic suggestions as one input among many, verify against trusted sources, and document your clinical reasoning