# AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
## 2 The Synergy of AI/ML and Domain Knowledge
### Case Studies
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Industry** & **Case Study Description** & **Key Outcomes** \\ \hline Healthcare & Integration of domain knowledge in mammography for breast cancer diagnosis. By embedding medical expertise about breast tissue characteristics and cancer indicators into AI models, diagnostic accuracy was improved. Without incorporating this domain knowledge, AI models might miss subtle signs of cancer, leading to less accurate diagnoses. & Improved diagnostic accuracy leading to less accurate diagnoses. \\ \hline Manufacturing & Ford’s use of digital twins and predictive maintenance. By integrating engineering knowledge of machinery operations and failure patterns into AI systems, Ford optimized production stages and reduced downtime. Without domain-specific insights, AI might not accurately predict equipment failures or optimize maintenance schedules effectively. & Optimized production stages, reduced downtime, enhanced efficiency \\ \hline Finance & Implementation of fair and transparent loan approval processes integrating domain expertise in financial risk assessment. By incorporating financial regulations and risk assessment principles, AI models reduced bias in loan approvals. Without domain knowledge, AI systems might perpetuate existing biases or overlook regulatory compliance, leading to unfair lending practices. & Reduced bias in loan approvals, increased trust in financial services \\ \hline Retail & L’Oreal’s integration of diverse data sources like social media and weather patterns for demand forecasting. By applying knowledge of consumer behavior and market dynamics, the AI models accurately predicted demand. An AI system lacking this domain context might fail to correlate these diverse data points effectively, resulting in inaccurate forecasts and poor inventory management. & Accurate demand predictions, optimized inventory management \\ \hline Robotics & Use of domain knowledge in sim-to-real transfers to align simulated training environments with real-world conditions for better robotic task performance. By incorporating physical laws and environmental factors into simulations, robots performed tasks more effectively in real-world settings. Without integrating domain knowledge, discrepancies between simulations and actual conditions could hinder robotic performance. & Improved real-world task performance, better alignment with simulations \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Case studies: success stories of domain knowledge integration. Moreover, the absence of domain knowledge can erode trust and hinder the adoption of AI/ML solutions [38; 39]. Stakeholders and end-users, particularly in sectors with intricate and nuanced operational landscapes like healthcare [40] or finance [39], may be reluctant to embrace models that do not align with their experiential understanding or fail to address domain-specific constraints and expectations [41]. The ethical dimension is equally critical. Models ignorant of the contextual fabric may inadvertently perpetuate biases, propelling unfair or unethical outcomes [42]. This lack of sensitivity to the domain's ethical and societal fabric questions not only the model's fairness but also its acceptability and viability in real-world settings [43; 44] (see Table 3). Furthermore, the journey from problem identification to solution implementation becomes fraught with inefficiencies when stripped of domain insights. Models might chase after inaccurately defined problems or approach them in ways that are theoretically sound but practically inept [45; 46]. The interpretability of these models, a crucial aspect especially in sensitive domains, suffers too. Without domain-specific lenses to view and interpret the model's outputs, the results might remain cryptic or misleading, complicating the task of effectively communicating and leveraging these insights for decision-making [47; 48].