Brief Reports
Vol. 5 No. 2 (2026)
The small numbers of medicine and the big numbers of artificial intelligence: a curated-data approach to appropriateness in laboratory medicine and coagulation
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Published: 15 April 2026
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Inappropriateness in medical decision-making remains a persistent challenge, particularly in laboratory medicine. This phenomenon is largely driven by cognitive inadequacy, defined as the growing mismatch between the exponential expansion of medical knowledge and the limited capacity of human cognition to assimilate and apply it in real time. Artificial intelligence (AI), especially in its narrow form, designed and trained to perform specialized tasks, offers practical tools to mitigate this gap. This article examines the potential role of AI in improving prescription appropriateness in laboratory medicine, with a focus on coagulation diagnostics, and proposes a novel AI-based system that supports physicians at the point of decision-making through the use of curated, expert-annotated data.
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Citations
1. Rajpurkar P, Irvin J, Zhu K, et al. CheXNet: radiologist-level pneumonia detection on chest X-rays with deep learning. arXiv [Preprint] 2017;1711.05225.
2. Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists. Ann Oncol 2018;29:1836-42. DOI: https://doi.org/10.1093/annonc/mdy520
3. De Fauw J, Ledsam JR, Romera-Paredes B, et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat Med 2018;24:1342-50. DOI: https://doi.org/10.1038/s41591-018-0107-6
4. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature 2020;577:89-94. DOI: https://doi.org/10.1038/s41586-019-1799-6
5. Stokes JM, Yang K, Swanson K, et al. A deep learning approach to antibiotic discovery. Cell 2020;180:688-702. DOI: https://doi.org/10.1016/j.cell.2020.01.021
6. Artificial Intelligence Index Report 2025. Stanford: Stanford HAI; 2025. Available from: https://hai.stanford.edu/ai-index/2025-ai-index-repor
7. Senior AW, Evans R, Jumper J, et al. Improved protein structure prediction using potentials from deep learning. Nature 2020;577:706-10. DOI: https://doi.org/10.1038/s41586-019-1923-7
8. Lippi G, Plebani M. Lights and shadows of artificial intelligence in laboratory medicine. Adv Lab Med 2025;6:1-3. DOI: https://doi.org/10.1515/almed-2025-0024
9. Belkin M, Bergen L, Danks D, et al. Does AI already have human-level intelligence? The evidence is clear. Nature 2026;650:36-40. DOI: https://doi.org/10.1038/d41586-026-00285-6
10. Gresele P. Artificial intelligence and machine learning in hemostasis and thrombosis. Bleeding Thromb Vasc Biol 2023;2:105. DOI: https://doi.org/10.4081/btvb.2023.105
11. Ma R, Yu W, Tang J, et al. Machine learning in the prediction of venous thromboembolism: a systematic review and meta-analysis. J Med Internet Res 2025;27. DOI: https://doi.org/10.2196/preprints.77339
12. Marinelli R, Mazzetto A, Camerotto A, et al. Electronic prescription codes and AI: benefits and opportunities from blood collection centers’ access to report generation. Riv Ital Med Lab 2025;21:288–94. DOI: https://doi.org/10.23736/S1825-859X.25.00302-0
13. Faggin F. Irriducibile. Milano: Mondadori; 2023.
14. Cristianini N. Sovrumano. Bologna: Il Mulino; 2025.
15. Narayanan A, Kapoor S. AI snake oil: what artificial intelligence can do, what it can’t, and how to tell the difference. Princeton: Princeton University Press; 2024. DOI: https://doi.org/10.1515/9780691249643
16. Levitin DJ. The organized mind. Boston: E.P. Dutton; 2014.
17. Zheng J, Meister M. The unbearable slowness of being: why do we live at 10 bits/s? Neuron 2025;113:192–204. DOI: https://doi.org/10.1016/j.neuron.2024.11.008
18. Harari YN. Sapiens: da animali a dei. Firenze: Bompiani; 2019.
19. Kennedy J. Pathogenesis. Firenze: Bompiani; 2024.
20. Reason J. Human error. Roma: EPC; 2014.
21. Burke P. Ignorance: a global history. New Haven: Yale University Press; 2023. DOI: https://doi.org/10.12987/9780300271263
22. Esho A, Chong YP, Choy KW, et al. Clinical decision support in laboratory medicine: a review of clinical, organisational and financial impacts with emphasis on patient outcomes. Pathology 2025;57:285-92. DOI: https://doi.org/10.1016/j.pathol.2024.11.005
23. Cadamuro J, Ibarz M, Cornes M, et al. Managing inappropriate utilization of laboratory resources. Diagnosis (Berl) 2019;6:5-13. DOI: https://doi.org/10.1515/dx-2018-0029
24. Vrijsen BEL, Naaktgeboren CA, Vos LM, et al. Inappropriate laboratory testing in internal medicine inpatients: prevalence, causes and interventions. Ann Med Surg 2020;51:48-53. DOI: https://doi.org/10.1016/j.amsu.2020.02.002
25. Cappelletti P. Praticare l’appropriatezza in medicina di laboratorio: un’introduzione. Riv Ital Med Lab 2013;9:1-7. DOI: https://doi.org/10.1007/s13631-012-0079-3
26. Cappelletti P. Praticare l’appropriatezza in medicina di laboratorio: un aggiornamento. Riv Ital Med Lab 2016;12:65-9. DOI: https://doi.org/10.1007/s13631-016-0117-7
27. Ofori-Asenso R. A closer look at the World Health Organization’s prescribing indicators. J Pharmacol Pharmacother 2016;7:51-4. DOI: https://doi.org/10.4103/0976-500X.179352
28. Almodóvar A, Ronda E, Flores R, et al. Appropriateness of radiological diagnostic tests in otolaryngology. Insights Imaging 2022;13:126. DOI: https://doi.org/10.1186/s13244-022-01263-y
29. Kamolratanapiboon K, Tantanate C. Inappropriate use of D-dimer and impact on test characteristics for deep vein thrombosis exclusion. Scand J Clin Lab Invest 2019;79:431-6. DOI: https://doi.org/10.1080/00365513.2019.1658214
30. Camerotto A, Truppo V, Pozzato A, et al. Ermete: a decision support system for innovative management of knowledge and prescription in laboratory medicine. Am J Clin Exp Med 2017;5:115-22. DOI: https://doi.org/10.11648/j.ajcem.20170504.13
31. Camerotto A, Mazzetto A, Farella A, et al. The reversal of the decision-making pyramid in medicine in the AI-driven era: a working hypothesis. Riv Ital Med Lab 2025;21:255-61. DOI: https://doi.org/10.23736/S1825-859X.25.00287-7
32. Camerotto A, Formenton F, Ramazzina E, et al. L’inappropriatezza nella richiesta di esami di laboratorio per difetto di conoscenza: errore individuale o errore di sistema? Biochim Clin 2007;31:209-19.
33. Devreese KMJ, Bertolaccini ML, Branch DW, et al. An update on laboratory detection and interpretation of antiphospholipid antibodies for diagnosis of antiphospholipid syndrome. J Thromb Haemost 2025;23:731-44. DOI: https://doi.org/10.1016/j.jtha.2024.10.022
34. Regione Veneto. Catalogo veneto prescrivibile [Internet]. Available from: https://salute.regione.veneto.it/web/fser/catalogo-veneto-prescrivibile
35. Agenzia Italiana del Farmaco. Liste dei farmaci [Internet]. Roma: AIFA; 2024. Available from: https://www.aifa.gov.it/liste-dei-farmaci
36. Rete Classificazioni [Internet]. Available from: https://www.reteclassificazioni.it
37. Osservatorio Malattie Rare [Internet]. Available from: https://www.osservatoriomalattierare.it
38. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 on artificial intelligence. Off J Eur Union 2024;L1689.
39. ISO/IEC 42001:2023. Artificial intelligence management system: objectives and requirements. Cybersecurity360 2024.
40. Ruamviboonsuk P, Arjkongharn N, Vongsa N, et al. Discriminative, generative artificial intelligence and foundation models in retina imaging. Taiwan J Ophthalmol 2024;14:473-85. DOI: https://doi.org/10.4103/tjo.TJO-D-24-00064
41. Barone G. Machine learning e intelligenza artificiale. Milano: Feltrinelli; 2021.
Vaibhav V. Supervised learning with Python. New York: Apress; 2020.
How to Cite
1.
Mazzetto A et al. The small numbers of medicine and the big numbers of artificial intelligence: a curated-data approach to appropriateness in laboratory medicine and coagulation. Bleeding Thromb Vasc Biol [Internet]. 2026 Apr. 15 [cited 2026 Apr. 16];5(2). Available from: https://www.btvb.org/btvb/article/view/460
Copyright (c) 2026 The Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
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