Academic Journal
The ancient trading hubs of modern science: Bridging the divide between microscopists and data scientists.
| Τίτλος: | The ancient trading hubs of modern science: Bridging the divide between microscopists and data scientists. |
|---|---|
| Συγγραφείς: | Kassim YM; Department of Cell & Developmental Biology, University of California, San Diego, California, USA., Manor U; Department of Cell & Developmental Biology, University of California, San Diego, California, USA. |
| Πηγή: | Journal of microscopy [J Microsc] 2026 Jun; Vol. 302 (3), pp. 320-333. Date of Electronic Publication: 2026 May 19. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: Published for the Royal Microscopical Society by Blackwell Scientific Publications Country of Publication: England NLM ID: 0204522 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1365-2818 (Electronic) Linking ISSN: 00222720 NLM ISO Abbreviation: J Microsc Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford, Published for the Royal Microscopical Society by Blackwell Scientific Publications. |
| Ιατρικοί όροι (MeSH): | Microscopy*/methods , Data Science*/methods , Image Processing, Computer-Assisted*/methods, Artificial Intelligence ; Humans |
| Περίληψη: | Biomedical imaging is increasingly defined by a paradox: advances in microscopy now enable the routine generation of extraordinarily rich, high-dimensional datasets, yet the extraction of reproducible, biologically meaningful insight from those data often remains a major bottleneck. This mismatch has widened a growing divide between microscopists and experimental biologists, who generate complex image data, and data scientists, who develop the computational tools needed to interpret them. In this Perspective, we argue that this divide is not merely technical, but also cultural, shaped by different assumptions about validation, dissemination, workflow design, and what constitutes meaningful success. We highlight how artificial intelligence and computational microscopy are simultaneously overrated when treated as turnkey solutions and underrated when deployed thoughtfully to reshape experimental design, data acquisition, and biological discovery. We further argue that current validation practices often fail to capture biological fidelity, creating a dangerous gap between benchmark performance and real-world utility. Finally, we propose that imaging core facilities should evolve beyond service models into collaborative hubs and training grounds where acquisition, analysis, and interpretation are integrated from the outset. By fostering shared standards, iterative workflows, and cross-disciplinary training, core facilities can help bridge the divide between photons and pixels and enable a more rigorous, scalable, and biologically grounded future for imaging science. (© 2026 The Author(s). Journal of Microscopy published by John Wiley & Sons Ltd on behalf of Royal Microscopical Society.) |
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| Contributed Indexing: | Keywords: artificial intelligence; biology; data science; deep learning; machine learning; microscopy |
| Entry Date(s): | Date Created: 20260519 Date Completed: 20260716 Latest Revision: 20260716 |
| Update Code: | 20260717 |
| DOI: | 10.1111/jmi.70108 |
| PMID: | 42153235 |
| Βάση Δεδομένων: | MEDLINE |
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