Our NYITCOM students, working at the Milan Toma, published a paper titled 'SMOTE vs. SMOTEENN: A Study on the Performance of Resampling Algorithms for Addressing Class Imbalance in Regression Models,' exploring methods for handling class imbalances in machine learning models.

Our latest NYITCOM study explored machine learning for diagnosing lumbar disc hernia & spondylolisthesis, and analyzing related biomechanical features. Impressed by our students' dedication as always. DOI:10.3390/biomedinformatics5010003 #AI #Orthopedics #MachineLearning

2024 was a phenomenal year for our Milan Toma. We're just 1 citation away from hitting 4-digit citations (999 and counting!). Huge thanks to our incredible NYITCOM students and collaborators who made this milestone possible. Your hard work and dedication continue to inspire.

Announcing our latest study in Discover Data! Our NYITCOM students (Jose, Lewis, Satti, Steinberg, Toufexis & Nasef) analyzed knee X-rays to detect osteoarthritis severity.

Presenting at the 5th Biotechnology Conference at New York Tech! Demonstrating how to evaluate AI models for training robustness to mitigate clinical risks in healthcare applications. Thanks to College of Engineering and Computing Sciences and NYITCOM for organizing this valuable event. #HealthcareAI #BiotechConference

Match day at NYITCOM Proud to have been a small part of their journey.

Our latest NYITCOM publication in the journal of AI: "Clinical Applicability of Machine Learning Models for ECG Classification", high accuracy != clinical applicability. DOI: 10.3390/ai6030059

Our NYITCOM students are the best! Congratulations to everyone for developing methods and publishing on the automated recognition of epileptic EEG signals in Advanced Neurology. DOI: 10.36922/an.7941

Join me in New York Tech Big Give, especially our OMM students, helping them attend the AAO Convocation and other OMM conferences, donate to the OMM Department’s Fund; 100% of donations go toward funding student attendance at osteopathic conferences. biggive.nyit.edu

Collaboration between NYITCOM and ETIC at the College of Engineering and Computing Sciences: Two medical students trained AI for ECG abnormality detection from a clinical angle, while two engineering students approached the same challenge from an engineering perspective. Both papers are now published! New York Tech

Organizing a special issue on AI in Medical Diagnostics on Cureus. If you’re working on ML, DL, predictive analytics, augmented clinical diagnostics, I invite you to submit your papers for review: cureus.com/collections/14…

🚀 Call for Papers: Special Issue on "AI-Assisted Medical Diagnostics" 🧠 📚 Guest Editor: Dr. Milan Toma. 🔍 Explore the full details and submission guidelines here: mdpi.com/journal/algori… #machinelearning #healthcare #medicaldiagnosisalgorithms #datadrivenmedicalsolutions

Could stretchable chinstraps on helmets reduce brain stress during impacts? 🧠💥 Our latest NYITCOM (Collaboration between NYIT Sports Medicine & NYITCOM-Osteopathic Medicine Center) study published in 'Applied Sciences' explores this question! #HelmetSafety

As demonstrated in our latest NYITCOM publication, we advocate that future ML-driven diagnostic studies adhere to the recent U.S. FDA's recommended credibility assessment by analyzing training dynamics alongside traditional performance metrics. DOI: 10.3390/info16060432

Caleb Neal is our NYITCOM student. He played semi-professional hockey in Sweden as a graduate student at Augusta University. Read his article on 'How Hockey Can Prevent Brain Injury Among Players.' brainfacts.org/diseases-and-d…

Using AI to detect ECG abnormalities, #NYITCOM's Dr. Milan Toma Milan Toma and student doctor Daniel Nasef teamed up with engineering students to train AI models on anonymized data. Their goal: improve accuracy and efficiency in cardiac diagnoses. buff.ly/Dn1CSuf

Our latest NYITCOM publication on training clinically actionable machine learning models in healthcare (ensuring a more comprehensive assessment of model robustness and clinical applicability); co-authored with three outstanding student doctors! DOI: 10.3390/a18060343

Our latest NYITCOM literature review on integrating artificial intelligence in clinical practice, hospital management, and health policy is now published in Journal of hospital management and health policy, DOI: 10.21037/jhmhp-24-138

Our latest research, "EEG-based Pain Detection," published. It was a collaboration between NYITCOM and the Medical University of Vienna (MedUni Wien), highlighting the importance of transparent ML pipelines for clinically relevant pain assessment. DOI: 10.20935/AcadBiol7790

From our recent NYITCOM paper: rooted in clinical science, our priorities differ from those of purely engineering teams; rather than approaching the work solely from algorithmic vantage point, we maintained a focus on clinical translation. DOI: 10.3390/bioengineering12080845