Ms. Naseebia Khan is a researcher specializing in Artificial Intelligence for Healthcare, recognized for her interdisciplinary contributions spanning medical image analysis, clinical data analytics, and multimodal AI systems. Her research integrates deep learning, statistical modeling, and biomedical sciences to address real-world clinical and regulatory challenges, with a sustained emphasis on scalability, privacy preservation, explainability, and clinical relevance.

Research Interests

• Medical image analysis and computer-aided diagnosis

• Deep learning architectures for disease detection and classification

• Multimodal AI systems for speech, hearing, and neurodevelopmental disorders

• Natural language processing for healthcare automation

• Privacy-preserving and explainable AI for clinical applications

Key Scientific Contributions

• Developed advanced deep learning methodologies for medical image analysis, improving diagnostic accuracy and clinical decision support.

• Led applied AI and statistical modeling initiatives for clinical safety analytics and post-marketing surveillance.

• Invented a privacy-preserving multimodal AI framework for early detection and personalized assessment of hearing, speech, and speech-sound disorders.

• Bridged biological sciences and data science to enable clinically deployable, sustainable, and ethically aligned AI-driven healthcare solutions.

Publications & Scholarly Output

Ms. Khan has authored multiple peer-reviewed journal articles, review papers, and conference proceedings published in reputed platforms, including Cureus, IEEE conference proceedings, Recent Patents on Engineering, and indexed diagnostic pathology journals. Her scholarly work reflects a strong balance of methodological innovation and translational clinical applicability in AI-driven healthcare research.

Patents & Innovation

AWARE: An AI-Powered Multimodal Assessment System for Early Detection and Personalized Evaluation of Hearing, Speech, and Speech-Sound Disorders
U.S. Provisional Patent Application No. 63/856,620 (Pending)

Scientific Significance:
This invention introduces an explainable, privacy-preserving multimodal AI framework designed for accessible, scalable screening and personalized assessment, addressing critical privacy, accessibility, and clinical adoption challenges in speech and hearing healthcare.

Peer Review & Scientific Engagement

• Verified Peer Reviewer, Web of Science

• Author of peer-reviewed journal articles and conference proceedings in AI for healthcare, medical image analysis, and biomedical data science

• Published contributor to the International Conference on Knowledge Engineering and Communication Systems (ICKECS)

Eligibility for Outstanding Researcher Award

Ms. Naseebia Khan is highly deserving of the Outstanding Researcher Award based on her original research contributions, interdisciplinary innovation, patented technology, and consistent scholarly output in AI-driven healthcare. Her work demonstrates scientific rigor, societal impact, and strong translational relevance, particularly in advancing privacy-aware and clinically scalable AI solutions. Her active engagement as a peer reviewer and contributor to international scientific forums further underscores her professional recognition and influence within the global research community.