The University of Jordan :: Research Groups :: AI in Medicine and Dentistry (AIMeD)

Group Main Activities:

Applications: diagnostics, predictive analytics, treatment planning, or medical imaging

AI for diagnostics and AI for diagnostics: detecting diseases, analyzing medical images, and improving diagnostic accuracy.

Some key applications in medicine and dentistry include:

AI in Medicine

• Medical Imaging Analysis analyze X-rays, ultrasound, MRIs, CT, and nuclear medicine scans to detect conditions like tumors, fractures, or neurological disorders.

• Pathology & Histology: examine biopsy samples to identify cancer cells more accurately than traditional methods.

• Disease Detection: detect conditions like pneumonia, tuberculosis, or diabetic retinopathy from medical scans.

• Diagnostics Analysis: Analysis of medical diagnostic such as ECG and/or EEG for detection cardiac and neurological diseases.

AI Dentistry

• Dental X-ray Interpretation: analyze panoramic and periapical radiographs to identify cavities, periodontal disease, and bone loss.

• Oral Cancer Detection: identify early signs of oral cancer from images and patient data.

• Automated Diagnosis & Treatment Planning: suggest personalized treatment plans based on patient records and diagnostic images.

Predictive analytics: analyse historical data and predict future outcomes. This helps in:

• Disease Prediction: Identifying patients at risk of conditions like diabetes, cardiovascular diseases, or oral cancer before symptoms appear.

• Treatment Outcomes: Forecasting how a patient will respond to different treatments based on their medical history.

• Early Diagnosis: Detecting patterns in medical images (X-rays, ultrasound, MRIs, CT, and nuclear scans) to identify diseases at an early stage.

• Patient Management: Predicting hospital readmissions or complications after surgery.

Key goals for AI in Medicine and Dentistry (AIMed) research group:

Advancing AI Research in Healthcare
Enhancing AI Clinical Applications
Ethical & Regulatory aspects of AI Research in Healthcare
Education & Knowledge Sharing of AI usage in Healthcare
Interdisciplinary Collaboration

Goals in details

1. Advancing AI Research in Healthcare

Develop innovative AI models for diagnostics, Predictive analytics, treatment planning, and patient management.
Improve the accuracy and efficiency of AI-driven medical and dental imaging analysis.
Explore predictive analytics to forecast disease progression and treatment outcomes.

2. Enhancing AI Clinical Applications

Collaborate with hospitals, dental clinics, and research institutions to test AI applications in real-world settings.
Integrate AI tools into electronic health records (EHRs) for better decision-making.
Develop AI-powered chatbots or virtual assistants for patient support and triage.

3. Ethical & Regulatory aspects of AI Research in Healthcare

Investigate ethical concerns, biases, and challenges in AI-based healthcare.
Contribute to policy discussions on AI regulation in medicine and dentistry.
Ensure AI solutions comply with data privacy and medical ethics standards.

4. Education & Knowledge Sharing of AI usage in Healthcare

Organize workshops, conferences, and webinars to educate students and professionals on AI in healthcare.
Publish research papers in high-impact journals and contribute to open-source AI initiatives.
Offer training programs on AI tools and techniques for medical and dental practitioners.

5. Interdisciplinary Collaboration

Work with experts in AI, medicine, dentistry, bioinformatics, and data science.
Seek partnerships with universities, AI startups, and healthcare companies.
Apply for grants and funding to support AI-driven healthcare research projects.

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