You will get Audit Your Biomedical AI Model for Clinical Reliability and Robustness
Project details
Most biomedical AI models fail during clinical trials because they lack two things: scientific rigor and transparency. I provide a "Clinical-Grade Audit" for your biosignal models (EEG, ECG, EMG) to ensure they are ready for real-world deployment.
What sets this project apart is my PhD-level methodology and track record. As the co-developer of the ALFABEATS platform, I helped achieve a validated 94.0% accuracy in detecting Major Depressive Disorder (MDD) using EEG data.
I don't just "check" your code. I perform a deep scientific audit:
Data Leakage Check: I verify that your validation protocols use strict subject-wise stratified splitting to ensure performance isn't artificially inflated.
Domain Shift Analysis: I stress-test your model against external public datasets (like CHB-MIT or TUH EEG) to ensure it generalizes across different populations and hardware.
Explainability (XAI) Integration: I implement SHAP values and saliency maps to identify physiological biomarkers, turning your "black box" into a trusted clinical tool.
I bring 15+ years of enterprise AI leadership (ex-PwC) and current PhD research at the University of Zagreb to your project.
What sets this project apart is my PhD-level methodology and track record. As the co-developer of the ALFABEATS platform, I helped achieve a validated 94.0% accuracy in detecting Major Depressive Disorder (MDD) using EEG data.
I don't just "check" your code. I perform a deep scientific audit:
Data Leakage Check: I verify that your validation protocols use strict subject-wise stratified splitting to ensure performance isn't artificially inflated.
Domain Shift Analysis: I stress-test your model against external public datasets (like CHB-MIT or TUH EEG) to ensure it generalizes across different populations and hardware.
Explainability (XAI) Integration: I implement SHAP values and saliency maps to identify physiological biomarkers, turning your "black box" into a trusted clinical tool.
I bring 15+ years of enterprise AI leadership (ex-PwC) and current PhD research at the University of Zagreb to your project.
AI Development Type
Deep Learning, Knowledge Representation, Model TuningAI Tools
MLflow, PyTorchAI Development Language
PythonWhat's included
| Service Tiers |
Starter
$1,500
|
Standard
$3,500
|
Advanced
$7,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 1 | 2 | 3 |
AI Model Integration | - | - | |
Detailed Code Comments | |||
Knowledge Graph | - | - | |
Model Documentation | |||
Ontology | - | ||
Source Code | - | ||
Taxonomy |
Optional add-ons
You can add these on the next page.
Live consultations
+$250
Expedited Delivery
+$500Frequently asked questions
About Node
PhD | Explainable AI (XAI) for Biomedical Signals | MedTech Product St
Zagreb, Croatia - 2:33 am local time
Most AI models fail in healthcare because clinicians cannot trust a "black box". I bridge the gap between high-performance machine learning and clinical practicality. With over 15 years of AI leadership and current PhD research at the University of Zagreb, I build diagnostic systems that are as transparent as they are accurate.
Key Outcomes Delivered:
Validated Diagnostic Accuracy: I co-developed the ALFABEATS platform, achieving up to 94.0% accuracy in detecting Major Depressive Disorder (MDD) using EEG data.
Explainable Frameworks (XAI): I integrate SHAP values, saliency maps, and feature permutation to provide clinicians with physiological evidence for every prediction.
Computational Efficiency: My work with algorithms like HydraMultiRocket Plus and ROCKET reduces training times from days to minutes while maintaining state-of-the-art performance.
Regulatory & Ethical Alignment: I apply rigorous subject-wise validation and cross-dataset sanity checks to ensure models generalize across diverse populations and hardware.
Technical Specializations
Signal Processing: EEG, ECG, and EMG analysis using Python, MNE, and mne-icalabel.
State-of-the-Art TSC: Implementation of Hydra, MultiRocket, InceptionTime, and HIVE-COTE v2.
Deep Learning: Spatio-temporal CNNs (STM-CNN) and Transformer-based architectures.
Strategy: AI roadmapping, ROI analysis (MBA), and pilot study coordination with institutions like UCSF.
Why Partner With Me?
I don’t just deliver code; I deliver clinical-grade assets. Whether you are a start-up raising a pre-seed round (as I have done with NeuroCube) or an OEM looking to integrate AI into monitoring equipment, I ensure your product is scientifically sound and ethically robust.
Steps for completing your project
After purchasing the project, send requirements so Node can start the project.
Delivery time starts when Node receives requirements from you.
Node works on your project following the steps below.
Revisions may occur after the delivery date.
Discovery & Data Ingestion
I will review your model architecture, codebase, and raw biosignal data (EDF/set formats). We establish baseline metrics and clinical objectives.
Rigor & Reliability Audit
I test for "temporal leakage" and verify your subject-wise validation protocols to ensure the results are clinically sound and not overfit.