CLINICAL EXPERTISE × ARTIFICIAL INTELLIGENCE
Bringing 25+ years of clinical expertise into AI training, quality assurance, model evaluation, data annotation, and intelligent workflows.
I'm Dr. Engy Elgamal, a dentist with more than 25 years of clinical experience, now building hands-on experience in applied AI, data annotation, model evaluation, and quality assurance while bringing a strong clinical perspective to my work.
My approach to AI is centered on careful review, structured analysis, and understanding why outputs succeed or fail. I apply these principles across dataset preparation, model evaluation, error analysis, and AI output review, with additional domain knowledge in dental and medical imaging.
I bring a detail-oriented, quality-focused mindset to AI projects, with an emphasis on structured evaluation, clear feedback, practical problem-solving, and reliable outcomes.
25+ years of clinical experience, diagnostic decision-making, patient care, and attention to accuracy and detail.
Structured data review, labeling, annotation, and quality checking for AI datasets.
Preparing and reviewing data, evaluating AI outputs, and providing structured feedback to support AI quality.
Evaluating AI outputs, identifying errors and inconsistencies, and applying structured quality review to assess reliability.
Using AI tools and structured workflows to turn evaluation results into clear, actionable insights.
A clinical-AI project built around a dataset of 1,300+ panoramic dental X-rays to explore dataset preparation and quality, annotation review, model evaluation, error analysis, and performance comparison across 10+ AI models.
Prepared, reviewed, and quality-checked dental imaging data for annotation, model evaluation, and structured analysis.
Comparing predictions, class performance, confidence, and model behavior across versions.
Reviewing false positives, false negatives, localization, misclassification, and confidence patterns.
An AI evaluation agent designed to read model reports, compare results, and transform key differences into structured insights.
Practical AI training focused on model evaluation, data quality, structured review, prompt engineering, and applied AI workflows.
Continuous independent learning and hands-on practice in AI evaluation, data annotation, dataset preparation, quality assurance, prompt engineering, and AI-assisted workflows.
25+ years of clinical experience in dentistry, patient care, diagnostic assessment, and dental imaging, providing strong domain expertise for healthcare AI, medical imaging, and data evaluation.
DeepLearning.AI / Coursera
Applied machine learning concepts for medical diagnosis, model evaluation, performance assessment, and healthcare-focused AI workflows.
Anthropic
Applied the AI Fluency framework to effective human-AI collaboration, critical evaluation, responsible use, and clear communication of AI practices.
Anthropic
Foundations of AI fluency, including effective interaction with AI systems, responsible use, task delegation, and critical evaluation of AI outputs.
Anthropic
Understanding AI capabilities, limitations, common failure modes, and methods for critically evaluating AI-generated outputs.
Anthropic
Practical use of Claude for prompting, task delegation, iterative refinement, and effective human-AI collaboration.
I plan to continue expanding my applied AI experience through new projects in AI evaluation, data quality, healthcare AI, and intelligent workflows.
Building practical AI projects focused on evaluation, data quality, structured workflows, and reliable AI-assisted decision making.
Expanding my work with panoramic dental X-rays, annotation, image analysis, and AI-assisted evaluation.
Advancing my skills in AI evaluation, quality assurance, prompt engineering, error analysis, and the critical review of AI-generated outputs.
I'm always open to connecting, collaborating, and exploring new opportunities in AI. Feel free to get in touch.