AI and Genomics: Crafting Precision Diagnostics in Future Pathology

In the field of pathology, the integration of artificial intelligence (AI) and genomics is paving the way for precision diagnostics that were once thought to be the realm of science fiction. This Research Topic delves into the transformative potential of combining AI technologies, such as deep neural networks, with genomic sequencing data to create personalised diagnostic solutions.

Current challenges include understanding disease mechanisms and accurately predicting disease outcomes, necessitating a paradigm shift toward personalised medicine. Studies have demonstrated the capabilities of AI in analysing complex patterns in genomic and histopathological data, yet many questions remain regarding the translation of these findings into clinical practice.

Recent advancements have shown that computational biomarkers, identified through AI, offer enhanced precision in disease outcome predictions. This has significant implications for personalised treatment strategies tailored to an individual’s genetic makeup and histopathological features.

Moreover, developments such as organ-on-a-chip models and in silico simulations hold the potential to increase AI's predictive capabilities in pathology. These innovations are being explored in significant studies, highlighting both the promise and ongoing debates in the clinical use of these technologies. However, the integration process and the ability to harness this data efficiently are current barriers requiring focused research efforts.

This Research Topic aims to explore the cutting-edge utilisation of AI and genomics in transforming pathology from a diagnostic into a therapeutic field. By providing tailored therapeutic strategies based on individual genetics, the goal is not just prediction but also enabling bespoke medical interventions.

To gather further insights into this expansive field, we welcome articles addressing, but not limited to, the following themes:

⦁ Deep learning models that integrate whole-slide imaging with genomic profiles to stratify patients for targeted therapies.
⦁ AI-guided discovery of actionable mutations or expression patterns from pathology specimens.
⦁ Digital twin models for simulating therapeutic outcomes based on patient-specific pathology-genomics data.
⦁ Explainable AI (XAI) approaches to ensure clinical interpretability and trust in therapeutic recommendations.
⦁ Case studies demonstrating the successful translation of AI-genomics models into clinical trials or therapeutic decision-making.
⦁ Pathology-Guided drug development: leveraging AI pathology-genomics models for early drug response prediction, trial enrichment, and adverse event forecasting.

These insights could potentially reshape the landscape of pathology, making personalised medicine a clinical reality.