MD
Doctors & specialists
Prediction, diagnostics, imaging, decision support, outcomes, and other clinically grounded AI questions.
Medical AI research mentorship
Turn clinical questions into rigorous AI research.
Practical research guidance for healthcare professionals who want to develop credible machine-learning and deep-learning studies, from the first idea to validation and manuscript strategy.
For research mentorship and methodological guidance. No publication guarantees.
Doctors
Dentists
Nurses
Pharmacists
Physical Therapists
Allied Health
Clinical Researchers
Who this is for
You bring the clinical insight. We structure the AI research around it.
You do not need to be a programmer or data scientist to begin. The goal is to turn a real clinical question into a defensible research plan.
DDS
Dentists & oral-health researchers
Dental imaging, caries, periodontal disease, oral cancer, treatment planning, and outcome prediction.
RN
Nurses & nursing researchers
Deterioration, readmissions, falls, triage, workload, patient safety, and nursing-outcomes research.
Rx
Pharmacists
Medication safety, adherence, pharmacotherapy outcomes, risk prediction, and clinical decision support.
PT
Physical therapists
Rehabilitation outcomes, movement analysis, recovery prediction, functional assessment, and AI applications in physical therapy research.
+
Allied health professionals
Laboratory medicine, public health, rehabilitation sciences, and other healthcare disciplines.
AI
Clinical & academic researchers
Researchers who need a clinician-informed partner for AI methodology, evaluation, validation, and reporting.
How I can help
A clear path from idea to research-ready project.
Support is adapted to your stage, whether you have only a question, an existing dataset, preliminary results, or a manuscript in progress.
01
Research question & feasibility
Clarify the clinical problem, define the target outcome, and assess whether AI/ML is appropriate.
02
Dataset strategy
Identify suitable public or institutional data sources, variables, labels, and data-splitting strategy.
03
ML / DL methodology
Select models and design a reproducible analysis plan suited to the research question and data type.
04
Evaluation & validation
Choose meaningful metrics, avoid leakage, assess calibration, explainability, and external validation where feasible.
05
Results & interpretation
Interpret model performance clinically and communicate limitations without overstating what the model can do.
06
Manuscript & journal strategy
Structure the study for transparent reporting, prepare the manuscript roadmap, and identify appropriate journal targets.
Research journey
What the process can look like.
Clinical question
Start with a real healthcare problem worth answering.
Research design
Define population, outcomes, dataset, methods, and evaluation.
Analysis & validation
Build, evaluate, interpret, and stress-test the research workflow.
Manuscript strategy
Translate the work into transparent scientific reporting and submission planning.
Ways to work together
Start with the level of support you actually need.
Choose the level of support that best matches your current research stage, from a focused strategy session to ongoing mentorship.
Focused
Research Strategy Session
A focused consultation for a specific medical-AI research idea or roadblock.
- Idea and feasibility review
- Dataset and methodology direction
- Key risks and next steps
- Practical research roadmap
Most useful for new projects
AI Research Launch
Structured support to turn an early clinical idea into a research-ready AI project.
- Research question refinement
- Dataset strategy
- Model and evaluation plan
- Validation and reporting roadmap
Ongoing
Research Mentorship
Longer-term guidance through the major stages of an AI research project.
- Regular research sessions
- Methods and analysis guidance
- Interpretation and manuscript support
- Submission strategy
About Dr Tee
Clinical thinking meets practical AI research.
Babatunde “Dr Tee” Ogunmiloro, M.D.
Physician and medical AI researcher focused on helping healthcare professionals transform clinically meaningful questions into rigorous, reproducible AI and machine-learning research. His work spans clinical prediction, medical imaging, model validation, explainable AI, digital health, and scientific publishing.
in View Dr Tee on LinkedInMDClinician-led perspective for healthcare research.
Published AI workExperience with clinical prediction and medical-imaging research.
External validationExperience testing clinical AI beyond the development dataset.
Peer reviewExperience evaluating digital-health research for scientific rigor.
FAQ
Before you book.
Do I need coding experience?
No. The support can begin at the research-question stage. The level of technical depth can be adapted to your background and project goals.
Do I need my own dataset?
No. If you do not yet have data, dataset strategy can be part of the research-planning process, including identifying suitable public datasets where appropriate.
Is this only for doctors?
No. The service is designed for healthcare professionals and clinical researchers, including doctors, dentists, nurses, pharmacists, allied-health professionals, residents, fellows, and postgraduate researchers.
Do you guarantee publication?
No. Research mentorship can improve study design, reporting, and submission strategy, but publication decisions remain with independent journals and reviewers.
Have a clinical question that could become an AI research project?
Book a research strategy session to assess feasibility, methodology, data needs, and the most practical next step.