# The Diagnostic Classification of the Pathological Image Using Computer Vision
## 9 Challenges and Future Directions
### Explainable AI
By tackling these challenges, the field of pathological image classification using computer vision is set to revolutionize disease detection, enable more precise diagnoses, and ultimately improve patient care through tailored treatment plans [182; 188]. The integration of AI with advanced microscopy techniques like multiphoton microscopy (MPM) is poised to revolutionize biomedical research and diagnostic pathology. AI algorithms will be crucial in optimizing image acquisition and processing in MPM, including real-time adaptive imaging, image restoration and super-resolution, and noise reduction. AI could dynamically adjust imaging parameters based on the sample's characteristics, optimizing resolution and reducing phototoxicity [192]. Deep learning models will continue to improve image quality, enabling the extraction of more detailed information from MPM data [12]. Advanced AI techniques will further enhance the signal-to-noise ratio in MPM images, allowing for clearer visualization of cellular structures. Future research will likely focus on integrating MPM with other imaging modalities, including AI-driven image fusion and multimodal feature extraction. Algorithms will be developed to seamlessly combine data from MPM with different techniques like OCT or quantitative phase imaging (QPI) [12]. AI models will be trained to identify and correlate features across different imaging modalities, providing a more comprehensive view of biological systems. AI will enhance the diagnostic potential of MPM, such as automated disease classification, predictive diagnostics, and personalized medicine. Deep learning models will be refined to accurately classify diseases based on MPM images, potentially surpassing human expert performance [12]. AI algorithms will be developed to predict disease progression and treatment outcomes based on subtle changes in MPM images [12]. AI-powered MPM analysis could help tailor treatments to individual patients based on their unique cellular characteristics [12]. In addition, some research will focus on developing AI systems specifically tailored for MPM, such as self-enhancing AI, meta-learning approaches, and explainable AI. Systems like SEMPAI (Self-Enhancing Multi-Photon Artificial Intelligence) will be further developed to optimize prior knowledge integration and data representation for MPM analysis [193]. Meta-learning approaches will enable AI models to learn from smaller datasets, which is crucial for rare diseases or specialized research areas [193]. Developing interpretable AI models will be a priority to provide insights into the decision-making process and increase trust in AI-assisted diagnoses [12]. Further, some future research will explore synergies between AI-enhanced MPM and other cutting-edge technologies, including organ-on-a-chip, CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats)-based techniques, and single-cell omics. AI-powered MPM could be used to analyze complex 3D cellular structures in organ-on-a-chip models, advancing drug discovery and toxicology studies. AI could help analyze MPM images of CRISPR-edited cells, providing insights into gene function and disease mechanisms. Integrating AI-enhanced MPM with single-cell sequencing data could provide unprecedented insights into cellular heterogeneity and function. By pursuing these research directions, the integration of AI with MPM and other advanced microscopy techniques has the potential to significantly advance our understanding of biological systems and improve diagnostic capabilities in pathology. Synthetic data generation and SSL play crucial roles in overcoming current limitations in AI and machine learning, while also shaping future advancements in these fields. Synthetic data generation addresses the challenges of data scarcity and privacy concerns. It allows organizations to create large, diverse datasets without compromising sensitive information. This is particularly valuable in fields like healthcare, where patient data privacy is paramount.