From Histopathology to Artificial Intelligence: The Emerging Landscape of Surgical Diagnostics in the Era of Precision Healthcare
Keywords:
Artificial Intelligence, Pathology, Radiomics, Molecular Diagnostic Techniques, Precision Medicine, Diagnostic ImagingAbstract
Traditional tissue histopathology is being transformed by a multi-modal approach supported by artificial intelligence (AI) and molecular characterization in the field of surgical diagnostics. This editorial examines the convergence of digital pathology, advanced imaging, radiomics, and multi-omics technologies redefining surgical decision-making. It delves into the benefits of AI-powered devices in diagnostics, predicting treatment outcomes, and making real-time decisions during surgery. The integration of these technologies promises personalized surgical care but faces challenges including data standardization, algorithm validation, regulatory concerns, and workforce training. It concluded that surgical diagnostics isn't about replacing pathologists, but about adding machine intelligence to deliver precision healthcare.
References
1. Ariana A, Amin M, Pakneshan S, Dolan-Evans E, Lam AK. Integration of traditional and e-learning methods to improve learning outcomes for dental students in histopathology. J Dent Educ. 2016;80(9):1140-8. https://doi.org/10.1002/j.0022-0337.2016.80.9.tb06196.x
2. Huss R, Coupland SE. Software-assisted decision support in digital histopathology. J Pathol. 2020;250(5):685-92. https://doi.org/10.1002/path.5388
3. Bhargava R. Digital histopathology by infrared spectroscopic imaging. Annu Rev Anal Chem. 2023;16(1):205-30. https://doi.org/10.1146/annurev-anchem-101422-090956
4. Alajaji SA, Khoury ZH, Elgharib M, Saeed M, Ahmed AR, Khan MB, et al. Generative adversarial networks in digital histopathology: current applications, limitations, ethical considerations, and future directions. Mod Pathol. 2024;37(1):100369. https://doi.org/10.1016/j.modpat.2023.100369
5. Ciga O, Xu T, Martel AL. Self supervised contrastive learning for digital histopathology. Mach Learn Appl. 2022;7:100198. https://doi.org/10.1016/j.mlwa.2021.100198
6. Schnell M, Mittal S, Falahkheirkhah K, Mittal A, Yeh K, Kenkel S, et al. All-digital histopathology by infrared-optical hybrid microscopy. Proc Natl Acad Sci USA. 2020;117(7):3388-96. https://doi.org/10.1073/pnas.1912400117
7. Bahadir CD, Omar M, Rosenthal J, Marchionni L, Liechty B, Pisapia DJ, et al. Artificial intelligence applications in histopathology. Nat Rev Electr Eng. 2024;1(2):93-108. https://doi.org/10.1038/s44287-023-00012-7
8. Unger M, Kather JN. Deep learning in cancer genomics and histopathology. Genome Med. 2024;16(1):44. https://doi.org/10.1186/s13073-024-01315-6
9. Ma Y, Jamdade S, Konduri L, Sailem H. AI in Histopathology Explorer for comprehensive analysis of the evolving AI landscape in histopathology. NPJ Digit Med. 2025;8(1):156. https://doi.org/10.1038/s41746-025-01524-2
10. Singh G, Manjila S, Sakla N, True A, Wardeh AH, Beig N, et al. Radiomics and radiogenomics in gliomas: a contemporary update. Br J Cancer. 2021;125(5):641-57. https://doi.org/10.1038/s41416-021-01387-w
11. Beig N, Bera K, Tiwari P. Introduction to radiomics and radiogenomics in neuro-oncology: implications and challenges. Neuro-Oncol Adv. 2020;2(Suppl 4):iv3-14. https://doi.org/10.1093/noajnl/vdaa148
12. Fan H, Luo Y, Gu F, Tian B, Xiong Y, Wu G, et al. Artificial intelligence-based MRI radiomics and radiogenomics in glioma. Cancer Imaging. 2024;24(1):36. https://doi.org/10.1186/s40644-024-00682-y
13. Wang X, Wang L, Lin H, Zhu Y, Huang D, Lai M, et al. Research progress of CTC, ctDNA, and EVs in cancer liquid biopsy. Front Oncol. 2024;14:1303335. https://doi.org/10.3389/fonc.2024.1303335
14. Banys-Paluchowski M, Fehm TN, Grimm-Glang D, Rody A, Krawczyk N. Liquid biopsy in metastatic breast cancer: current role of circulating tumor cells and circulating tumor DNA. Oncol Res Treat. 2022;45(1-2):4-11. https://doi.org/10.1159/000520561
15. Pantel K. Liquid biopsy: blood-based analyses of ctDNA and CTCs. Clin Chem. 2021;67(11):1437-9. https://doi.org/10.1093/clinchem/hvab168
16. Liu Y, Wang C, Wang Z, Chen L, Li Z, Song J, et al. High-parameter spatial multi-omics through histology-anchored integration. Nat Methods. 2026;23(2):373-86. https://doi.org/10.1038/s41592-025-02926-6
17. Zhao T, Ren Y, Lu H, Kong Y. Decision level scheme for fusing multiomics and histology slide images using deep neural network for tumor prognosis prediction. Sci Rep. 2025;15(1):25479. https://doi.org/10.1038/s41598-025-09869-0
18. Lee M, Li G, Li H, Duan X, Birla MB, Chang TS, et al. Confocal laser endomicroscope with distal MEMS scanner for real-time histopathology. Sci Rep. 2022;12(1):20155. https://doi.org/10.1038/s41598-022-24210-9
19. Sabtu SN, Abdul Sani SF, Bradley DA, Looi LM, Osman Z. A review of the applications of Raman spectroscopy for breast cancer tissue diagnostic and their histopathological classification of epithelial to mesenchymal transition. J Raman Spectrosc. 2020;51(3):380-9. https://doi.org/10.1002/jrs.5774
20. Tong Y, Lu W, Yu Y, Shen Y. Application of machine learning in ophthalmic imaging modalities. Eye Vis. 2020;7(1):22. https://doi.org/10.1186/s40662-020-00183-6
21. Cooper M, Ji Z, Krishnan RG. Machine learning in computational histopathology: Challenges and opportunities. Genes Chromosomes Cancer. 2023;62(9):540-56. https://doi.org/10.1002/gcc.23177
22. Chen W, Zhang P, Tran TN, Xiao Y, Li S, Shah VV, et al. A visual–omics foundation model to bridge histopathology with spatial transcriptomics. Nat Methods. 2025;22(7):1568-82. https://doi.org/10.1038/s41592-025-02707-1
23. OpenAI. ChatGPT (GPT-4o) [Computer Software]. 2026 [cited 2026 May 20]. Available from: https://chatgpt.com
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