TY  - EJOU
AU  - Madanchian, Mitra 
AU  - Taherdoost, Hamed 

TI  - AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice
T2  - Computers, Materials \& Continua

PY  - 2024
VL  - 81
IS  - 2
SN  - 1546-2226

AB  - This comparative review explores the dynamic and evolving landscape of artificial intelligence (AI)-powered innovations within high-tech research and development (R&D). It delves into both theoretical models and practical applications across a broad range of industries, including biotechnology, automotive, aerospace, and telecommunications. By examining critical advancements in AI algorithms, machine learning, deep learning models, simulations, and predictive analytics, the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies. The review integrates both qualitative and quantitative data derived from academic studies, industry reports, and real-world case studies to showcase the tangible impacts of AI on product innovation, process optimization, and strategic decision-making. Notably, it discusses the challenges of integrating AI within complex industrial systems, such as ethical concerns, technical limitations, and the need for regulatory oversight. The findings reveal a mixed landscape where AI has significantly accelerated R&D processes, reduced costs, and enabled more precise simulations and predictions, but also highlighted gaps in knowledge transfer, skills adaptation, and cross-industry standardization. By bridging the gap between AI theory and practice, the review offers insights into the effectiveness, successes, and obstacles faced by organizations as they implement AI-driven solutions. Concluding with a forward-looking perspective, the review identifies emerging trends, future challenges, and promising opportunities in AI-powered R&D, such as the rise of autonomous systems, AI-driven drug discovery, and sustainable energy solutions. It offers a holistic understanding of how AI is shaping the future of technological innovation and provides actionable insights for researchers, engineers, and policymakers involved in high-tech Research and Development (R&D).
KW  - Deep learning; high-tech research and development; theoretical models; practical applications; neural networks; predictive analytics; innovation strategies

DO  - 10.32604/cmc.2024.057094