AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning

• Published in: Applied sciences Vol. 14; no. 24; p. 11612
• Main Authors: Miller, Tymoteusz, Durlik, Irmina, Łobodzińska, Adrianna, Dorobczyński, Lech, Jasionowski, Robert
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2024
• Subjects: Accuracy; AI/ML ethical practices; Algorithms; Analysis; Artificial intelligence; Breast cancer; Case studies; Computational linguistics; domain expertise in AI; Ethical aspects; Ethics; Forecasts and trends; industry-specific AI applications; Innovations; Knowledge; Language processing; Machine learning; Mammography; Manufacturing industry; Medical diagnosis; Natural language interfaces; predictive analytics in AI; Robots; Simulation; Subject specialists; technological innovation in AI/ML; Trends; Poland; United States
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app142411612

This article delves into the critical integration of domain knowledge into AI/ML systems across various industries, highlighting its importance in developing ethically responsible, effective, and contextually relevant solutions. Through detailed case studies from the healthcare and manufacturing sectors, we explore the challenges, strategies, and successes of this integration. We discuss the evolving role of domain experts and the emerging tools and technologies that facilitate the incorporation of human expertise into AI/ML models. The article forecasts future trends, predicting a more seamless and strategic collaboration between AI/ML and domain expertise. It emphasizes the necessity of this synergy for fostering innovation, ensuring ethical practices, and aligning technological advancements with human values and real-world complexities.