Biomedical AI & Medical Imaging

Transforming Healthcare with Intelligent Insights: Driving Precision Diagnostics and Therapeutic Innovations through AI-Powered Bioengineering and Advanced Medical Imaging.

AI models and systems for diagnostics, EEG, clinical imaging, and regulatory-aligned healthcare solutions
Subtopics:
• ML for EEG signal processing (highly cited)
• GenAI for multimodal diagnostics
• HIPAA/FDA-compliant validation workflows
• Clinical UX and explainability frameworks

My Contribution: My work pioneers the application of artificial intelligence and machine learning to complex biomedical data, particularly focusing on Electroencephalography (EEG) signal processing. I develop novel AI frameworks for analyzing neural time-series patterns, enabling deeper insights into brain activity and facilitating advancements in neurodiagnostics. Furthermore, my research explores the unique capabilities of advanced imaging modalities like Terahertz (THz) for medical applications, including the early detection of disease symptoms, bridging the gap between cutting-edge physics and clinical utility.

Publications & Patents:

A review on machine learning for EEG signal processing in bioengineering

K. Ahi
IEEE Reviews in Biomedical Engineering 14, 204-218
2020
Cited by: 466
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Highlight: This highly-cited review provides a comprehensive overview of machine learning's transformative role in EEG signal processing, setting a benchmark for future research in bioengineering and neurodiagnostics.
Survey of terahertz photonics and biophotonics

K. Ahi
Optical Engineering 59 (6), 061629-061629
2020
Cited by: 28
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Highlight: This survey explores the burgeoning fields of terahertz photonics and biophotonics, highlighting the potential for advanced imaging and sensing in biological and medical applications.
Increased cerebrovascular sensitivity to endothelin-1 in a rat model of obstructive sleep apnea: a role for endothelin receptor B

K. Ahi
Journal of Cerebral Blood Flow & Metabolism 35 (3), 402-411
2015
Cited by: 28
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Highlight: This fundamental biomedical research elucidates a critical mechanism in obstructive sleep apnea, identifying a key pathway for cerebrovascular dysfunction. (Note: While not direct AI, it demonstrates foundational biological research relevant to medical understanding).
A machine learning framework for analysis of resting-state eeg in patients

K. Ahi
Technical University of Denmark
2022
Cited by: 2
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Highlight: This work proposes a novel machine learning framework for analyzing resting-state EEG, offering a powerful tool for patient diagnostics and neurological research.
Developing Novel Machine Learning Approaches for Distinguishing Neural Time-Series Patterns

K. Ahi
Icahn School of Medicine at Mount Sinai
2023
Cited by: (New publication, citations building)
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Highlight: This research focuses on developing sophisticated machine learning methods to differentiate subtle neural time-series patterns, advancing the precision of brain activity analysis.
Mentality: Amamba-based approach towards foundation models for eeg

K. Ahi
2022
Cited by: 1
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Highlight: This paper explores an Amamba-based approach for creating foundation models specifically designed for EEG analysis, pushing the frontier of generalizable neural signal processing.
Transformers for EEG classification: architectures, pre-training, and applications to epileptic seizure forecasting

K. Ahi
2022
Cited by: 1
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Highlight: This work investigates the application of Transformer architectures for EEG classification, demonstrating their potential for highly accurate epileptic seizure forecasting.
Method and system for enhancing resolution of terahertz imaging and detection of symptoms of COVID-19, cold, and influenza

K. Ahi
US Patent 11,510,586
2022
Cited by: 1
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Highlight: This patent introduces a groundbreaking method for utilizing high-resolution terahertz imaging to non-invasively detect symptoms of respiratory illnesses, offering a novel diagnostic tool.