Artificial Neural Networks: Current Application Areasand Future Potential
This paper provides a comprehensive analysis of Artificial Neural Networks (ANNs) , exploring both their current real-world applications and future technological potential . Drawing from recent academic literature and industry breakthroughs, it examines ANN-driven innovations in healthcare, finance, autonomous vehicles, natural language processing, and image/video processing, supported by case studies such as Google DeepMind’s AlphaFold, Tesla’s Autopilot, and OpenAI’s GPT models. The study further investigates emerging frontiers—including Quantum Machine Learning , Neuromorphic Computing , Multimodal AI , and Sustainable AI —while critically
Resource information
- Authors
- Okur, Elif Ceren
- Research area
- Artificial intelligence
- Research topics
- Artificial intelligence not elsewhere classified, Neural networks, Machine learning not elsewhere classified, Human-computer interaction, Building science, technologies and systems, Computational neuroscience (incl. mathematical neuroscience and theoretical neuroscience), Information systems for sustainable development and the public good, Medical robotics
- Publication date
- 2025
- DOI
- 10.6084/m9.figshare.29920799
- Upstream last modified
- Mar 29, 2026
- License
- Creative Commons Attribution 4.0 International
- Last record update
- Nov 5, 2026
About this resource
This paper provides a comprehensive analysis of Artificial Neural Networks (ANNs) , exploring both their current real-world applications and future technological potential . Drawing from recent academic literature and industry breakthroughs, it examines ANN-driven innovations in healthcare, finance, autonomous vehicles, natural language processing, and image/video processing, supported by case studies such as Google DeepMind’s AlphaFold, Tesla’s Autopilot, and OpenAI’s GPT models. The study further investigates emerging frontiers—including Quantum Machine Learning , Neuromorphic Computing , Multimodal AI , and Sustainable AI —while critically addressing technical limitations , ethical challenges , and environmental concerns . By combining technical insight with an ethics-focused perspective, this work offers actionable recommendations for researchers, developers, and policymakers to guide responsible, transparent, and energy-efficient AI development.