# AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
## 2 The Synergy of AI/ML and Domain Knowledge
### Case Studies  
The significance of domain knowledge in AI/ML projects can be substantially appreciated through two distinct case studies: the use of mammography for breast cancer diagnosis and the training of robots for real-world tasks. In the realm of healthcare, specifically in breast cancer diagnosis using mammograms, the inclusion of domain knowledge profoundly enhances the performance and interpretability of AI models. Traditionally, computer vision approaches to mammogram image analysis, like employing convolutional neural networks (CNNs), provide a solid baseline [26; 27]. However, when domain-specific insights--such as understanding the importance of features like breast density asymmetry as an indicator of cancer--are integrated, the models become significantly more powerful. Researchers developed a multi-view, multi-task (MVMT) [28; 29] system that mirrors the workflow of radiologists, predicting not only the diagnosis but also auxiliary targets like signs, suspicion, and breast density for each mammogram view. This approach not only improved the diagnostic accuracy (with the area under the ROC curve reaching 0.855, which is 0.018 higher than the baseline) [29] but also enhanced the model's interpretability and interaction with medical professionals. The domain knowledge contributed to a refined feature set, improved accuracy, and more meaningful interactions between the model and human experts. In the field of robotics, the role of domain knowledge becomes evident in bridging the gap between simulated training environments and real-world applications, a process often termed \"sim2 real\" [30] (see Table 2). Simulations, while crucial for training algorithms, cannot perfectly replicate real-world conditions. Domain knowledge assists in identifying and incorporating critical factors, such as actuator noise, into the simulations [31]. Actuator noise, representing the variability in a robot's movements or actions in the real world, is crucial for ensuring that the behavior of robots in simulations aligns with their real-world counterparts [30]. Research indicates that aligning the noise levels in simulations with those in the real world results in better performance of the robots, both in simulations and in real-world tasks [31; 32]. This highlights the invaluable role of domain knowledge in creating more realistic and effective simulation environments for robotic training [32]. The integration of domain knowledge in AI/ML processes, from the initial problem specification and data understanding to the final model evaluation and interpretation, significantly enhances the robustness, accuracy, and applicability of the models [33]. This integration ensures that the developed models not only excel in technical performance but also resonate with and are relevant to the specific needs and nuances of the domain they are intended for [34]. These case studies underline the transformative impact of incorporating domain expertise into AI/ML projects, emphasizing that the fusion of technical prowess with deep, contextual understanding results in solutions that are not only technically sound but also deeply aligned with the domain-specific realities and requirements. Results and Discussion on the Limitations of AI/ML When Used Without Proper Contextual Understanding  
When AI/ML is employed without a thorough contextual understanding, a cascade of limitations may arise, subtly undermining the performance, reliability, and reception of the models [8]. In scenarios devoid of domain expertise, there is a heightened risk of data misinterpretation [8; 35]. Models might erroneously latch onto irrelevant patterns or noise, mistaking these for meaningful insights [36]. This jeopardizes not only the model's precision but also its ability to generalize beyond the training data, rendering it less effective in real-world applications [37].