Soroush Sadeghnejad

The integration of artificial intelligence (AI) and natural language processing (NLP) in healthcare has demonstrated transformative potential for disease diagnosis and clinical decision support. This paper presents a comprehensive analysis of current AI and NLP methodologies in healthcare, examining their applications across medical imaging, clinical text analysis, and diagnostic decision support systems. We systematically evaluate performance metrics from 50+ peer-reviewed studies, revealing that AI systems achieve diagnostic accuracies ranging from 88.7% to 96.2% across different medical specialties, often matching or exceeding human expert performance. Our analysis identifies key implementation challenges including data quality, algorithmic transparency, and clinical workflow integration. We propose a framework for responsible AI deployment in healthcare settings and discuss emerging trends in multimodal diagnostic approaches. The findings suggest that while AI and NLP technologies offer significant clinical benefits, successful implementation requires careful attention to technical validation, ethical considerations, and stakeholder engagement.

Amirmohammad Zarif Shahsavan Nejad, Helia Mokhtari, Azita Shirazipour, Soroush Sadeghnejad, Amirmahdi Zarif Verified by AVIS CertificateFIRA World Summit 2026Submitted 17 Sept 2026

This study presents a comprehensive Open Innovation model, infused with Systems Thinking principles, to investigate the FIRA Innovation & Business League’s role as a global platform for propelling robotics and emerging technologies forward. By framing the league as a multi-stakeholder ecosystem, the research delineates a layered framework that elucidates interactions among startups, established organizations, and the wider community. Employing a mixed-methods approach, the study illuminates how the league enables knowledge exchange, technology transfer, and entrepreneurial collaboration. The findings underscore that this integrated model accelerates innovation cycles, bolsters startup performance, and cultivates robust linkages among industry, academia, and government. A SWOT analysis further unveils the challenges and opportunities inherent in maintaining open innovation amid a swiftly evolving technological terrain. The paper culminates in underscoring the pivotal role of problem-driven innovation formats and sustainable ecosystem architectures, with special attention to the league’s forthcoming iterations, including its anticipated 2026 expansion into Canada.

Amirmahdi Zarif, Amirmohammad Zarif Shahsavan Nejad, Jacky Baltes, Kuo-Yang Tu, Soroush Sadeghnejad Verified by AVIS CertificateFIRA World Summit 2026Submitted 17 Sept 2026

The development of humanoid robots, particularly in enhancing human-robot interaction, is a rapidly advancing field. This paper explores the simulation of the Thormang humanoid robot and the development of advanced grasping models using machine learning techniques. Our research delves into the intricacies of kinematics, dynamics, and computer vision, paving the way for more intuitive and effective humanrobot interactions. Using and comparing different 6D pose models such as CASAPose, we continue to perform the provided tasks in object manipulation.

Armin Baratian Sarabi, Amin Arami, Amirmohammad Zarif Shahsavan Nejad, Amirmahdi Zarif, Soroush Sadeghnejad Verified by AVIS CertificateFIRA World Summit 2026Submitted 17 Sept 2026

The integration of artificial intelligence (AI) and natural language processing (NLP) technologies into healthcare systems represents a paradigm shift in clinical practice, medical research, and healthcare administration. This comprehensive review examines the current state of AI and NLP applications in disease diagnosis and broader healthcare contexts. We systematically analyze methodological approaches, implementation strategies, and empirical outcomes across major application domains including diagnostic systems, clinical decision support, medical imaging analysis, electronic health record processing, patient monitoring, and drug discovery. Our analysis of 80+ peer-reviewed studies reveals remarkable progress in diagnostic accuracy, workflow efficiency, and personalized medicine capabilities. However, significant challenges persist in areas of data quality, algorithmic transparency, clinical integration, and regulatory compliance. Ethical considerations regarding patient privacy, algorithmic bias, and the changing nature of medical decision-making are critically examined. The review concludes with an assessment of emerging research directions and recommendations for advancing the responsible implementation of AI and NLP technologies in healthcare settings. This work provides a comprehensive framework for understanding the transformative potential of these technologies while acknowledging the complex technical, clinical, and ethical considerations that must guide their development and deployment

Amirmohammad Zarif Shahsavan Nejad, Azita Shirazipoor, Soroush Sadeghnejad Verified by AVIS CertificateFIRA World Summit 2025Submitted 23 Jul 2025

This study delivers a comprehensive and specialized evaluation of the Proportional-Integral-Derivative (PID), Pure Pursuit, and Stanley controllers for path-tracking in autonomous vehicles, conducted within the AVIS (Autonomous Vehicle Intelligent Software) Engine. Utilizing an advanced analytical geometric vision algorithm enhanced with sliding window techniques, the analysis rigorously assesses controller performance across varied driving scenarios. Key metrics, including Mean Squared Error (MSE), Cross-Track Error (CTE), Steering Over Time, Normalized Lateral Deviation, and Angular Error Over Time, are derived from 1000 simulation runs at 100 Hz. The study provides an in-depth exploration of each controller’s operational principles, parameter optimization, and dynamic response, incorporating insights from the AVIS Engine’s physics layer and vehicle dynamics model. These findings offer critical guidance for designing robust autonomous navigation systems tailored to diverse operational contexts.

Armin Baratian Sarabi, Amin Arami, Amirmohammad Zarif Shahsavan Nejad, Amirmahdi Zarif Shahsavan Nejad, Soroush Sadeghnejad Verified by AVIS CertificateFIRA World Summit 2025Submitted 23 Jul 2025

The development of humanoid robots, particularly in enhancing human-robot interaction, is a rapidly advancing field. This paper explores the simulation of the Thormang humanoid robot and the development of advanced grasping models using machine learning techniques. Our research delves into the intricacies of kinematics, dynamics, and computer vision, paving the way for more intuitive and effective human-robot interactions. Using and comparing different 6D pose models such as CASAPose, we continue to perform the provided tasks in object manipulation.

Armin Baratian Sarabi, Amin Arami, Amirmohammad Zarif Shahsavan Nejad, Amirmahdi Zarif Shahsavan Nejad, Soroush Sadeghnejad Verified by AVIS CertificateFIRA World Summit 2024Submitted 16 Jun 2024