Natural language processing (NLP), a branch of artificial intelligence that focuses on the interaction between computers and human language, has potential in advancing veterinary pathology through its ability to source knowledge efficiently from vast data sets, generate high-quality text rapidly, and enhance data searchability. This review explores the applications of NLP in veterinary pathology, emphasizing its potential role in diagnostics, training pathologists, and research. NLP might offer many advantages, such as accuracy, speed, and cost reduction, especially for routine tasks including text summarization and report generation. These benefits make NLP a promising technology for achieving precision, adding value, and driving innovation in health care. However, caution is warranted, as NLP models may introduce biases and errors due to the quality of the data they are trained on, have limitations in interpreting nuanced or context-specific information, and lead to private data leakage. Furthermore, the multifaceted nature of veterinary pathology data may require specifically trained and expert-validated algorithms for accurate interpretation. To ensure the credibility and validity of research findings, pathologists must critically evaluate and complement obtained outputs with human expertise and judgment. This article highlights the transformative potential of NLP in veterinary pathology, underscores the importance of integrating this technology into the field for enhanced diagnostic accuracy and research advancements, and gives real-life examples from pathologists for pathologists, which illustrate how NLP can be applied in veterinary pathology.
AdamopoulouEMoussiadesL.Chatbots: history, technology, and applications. Mach Learn Appl. 2020;2:100006. doi:10.1016/j.mlwa.2020.100006.
2.
Al-GaradiMAYangYCSarkerA.The role of natural language processing during the COVID-19 pandemic: health applications, opportunities, and challenges. Healthcare. 2022;10(11):2270. doi:10.3390/healthcare10112270.
3.
BakerNThompsonBFoxD.ChatGPT can write a paper in an hour—but there are downsides [published online ahead of print July 12, 2023]. Nature. doi:10.1038/d41586-023-02298-x.
4.
BankheadPLoughreyMBFernándezJA, et al. QuPath: open source software for digital pathology image analysis. Sci Rep. 2017;7(1):16878. doi:10.1038/s41598-017-17204-5.
5.
BieverC.ChatGPT broke the Turing test—the race is on for new ways to assess AI. Nature. 2023;619(7971):686–689. doi:10.1038/d41586-023-02361-7.
6.
BommasaniRHudsonDAAdeliE, et al. On the opportunities and risks of foundation models [Published online ahead of print July 12, 2022]. arXiv. doi:10.48550/arXiv.2108.07258.
ChenLZahariaMZouJ.How is ChatGPT’s behavior changing over time?Harv Data Sci Rev. 2024;6(2). doi:10.1162/99608f92.5317da47.
11.
ChowdhurySNathA.Trends in natural language processing : scope and challenges. Int J Sci Res Comput Sci Eng Inf Technol. 2021;7(6):393–401. doi:10.32628/CSEIT217698.
12.
ChuCP.ChatGPT in veterinary medicine: a practical guidance of generative artificial intelligence in clinics, education, and research. Front Vet Sci. 2024;11:1395934. doi:10.3389/fvets.2024.1395934.
CorcoranCMMittalVABeardenCE, et al. Language as a biomarker for psychosis: a natural language processing approach. Schizophr Res. 2020;226:158–166. doi:10.1016/j.schres.2020.04.032.
15.
CorradiMLuechtefeldTde HaanAM, et al. The application of natural language processing for the extraction of mechanistic information in toxicology. Front Toxicol. 2024;6:1393662. doi:10.3389/ftox.2024.1393662.
16.
CroxfordEGaoYPellegrinoN, et al. Current and future state of evaluation of large language models for medical summarization tasks. Npj Health Syst. 2025;2(1):1–13. doi:10.1038/s44401-024-00011-2.
DaveTAthaluriSASinghS.ChatGPT in medicine: an overview of its applications, advantages, limitations, future prospects, and ethical considerations. Front Artif Intell. 2023;6:1169595. doi:10.3389/frai.2023.1169595.
19.
DavenportTKalakotaR.The potential for artificial intelligence in healthcare. Future Healthc J. 2019;6(2):94–98. doi:10.7861/futurehosp.6-2-94.
20.
DavisAPWiegersJWiegersTC, et al. Public data sources to support systems toxicology applications. Curr Opin Toxicol. 2019;16:17–24. doi:10.1016/j.cotox.2019.03.002.
21.
De AngelisLBaglivoFArzilliG, et al. ChatGPT and the rise of large language models: the new AI-driven infodemic threat in public health. Front Public Health. 2023;11:1166120. doi:10.3389/fpubh.2023.1166120.
22.
DevlinJChangMWLeeK, et al. BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics; June2019; Minneapolis, MN: 4171–4186. doi:10.18653/v1/N19-1423.
23.
EloundouTManningSMishkinP, et al. GPTs are GPTs: an early look at the labor market impact potential of large language models. Published August 21, 2023. Accessed September 21, 2023. http://arxiv.org/abs/2303.10130.
24.
EstevaARobicquetARamsundarB, et al. A guide to deep learning in healthcare. Nat Med. 2019;25(1):24–29. doi:10.1038/s41591-018-0316-z.
FenzaGGalloMLoiaV, et al. Data set quality in machine learning: consistency measure based on group decision making. Appl Soft Comput. 2021;106:107366. doi:10.1016/j.asoc.2021.107366.
28.
FloridiLChiriattiM.GPT-3: its nature, scope, limits, and consequences. Minds Mach. 2020;30(4):681–694. doi:10.1007/s11023-020-09548-1.
FrançaRPBorges MonteiroACArthurR, et al. Chapter 3—an overview of deep learning in big data, image, and signal processing in the modern digital age. In: PiuriVRajSGenoveseASrivastavaR eds. Trends in Deep Learning Methodologies. Hybrid Computational Intelligence for Pattern Analysis. Cambridge, MA: Academic Press;2021:63–87. doi:10.1016/B978-0-12-822226-3.00003-9.
31.
GaoYXiongYGaoX, et al. Retrieval-augmented generation for large language models: a survey [Published online ahead of print March 27, 2024]. doi:10.48550/arXiv.2312.10997.
32.
GilsonASafranekCWHuangT, et al. How does ChatGPT perform on the United States medical licensing examination? the implications of large language models for medical education and knowledge assessment. JMIR Med Educ. 2023;9:e45312. doi:10.2196/45312.
33.
GiuffrèMKresevicSPuglieseN, et al. Optimizing large language models in digestive disease: strategies and challenges to improve clinical outcomes. Liver Int. 2024;44(9):2114–2124. doi:10.1111/liv.15974.
34.
GroteTBerensP.On the ethics of algorithmic decision-making in healthcare. J Med Ethics. 2020;46(3):205–211. doi:10.1136/medethics-2019-105586.
35.
HannaMGHannaMH.Current applications and challenges of artificial intelligence in pathology. Hum Pathol Rep. 2022;27:300596. doi:10.1016/j.hpr.2022.300596.
36.
HassanNSlightRMorganG, et al. Road map for clinicians to develop and evaluate AI predictive models to inform clinical decision-making. BMJ Health Care Inform. 2023;30(1):e100784. doi:10.1136/bmjhci-2023-100784.
37.
HasselgrenCOpreaTI.Artificial intelligence for drug discovery: are we there yet?Annu Rev Pharmacol Toxicol. 2024;64:527–550. doi:10.1146/annurev-pharmtox-040323-040828.
38.
HickmanSEWoitekRLeEPV, et al. Machine learning for workflow applications in screening mammography: systematic review and meta-analysis. Radiology. 2022;302(1):88–104. doi:10.1148/radiol.2021210391.
HovyEWangHWuH, et al. Pre-trained language models and their applications. Engineering. 2023;25:51–65. doi:10.1016/j.eng.2022.04.024.
41.
HoyMB.Alexa, siri, cortana, and more: an introduction to voice assistants. Med Ref Serv Q. 2018;37(1):81–88. doi:10.1080/02763869.2018.1404391.
42.
HurBHardefeldtLVerspoorK, et al. Overcoming challenges in extracting prescribing habits from veterinary clinics using big data and deep learning. Aust Vet J. 2022;100(5):220–222. doi:10.1111/avj.13145.
43.
HurBHardefeldtLVerspoorK, et al. Using natural language processing and VetCompass to understand antimicrobial usage patterns in Australia. Aust Vet J. 2019;97(8):298–300. doi:10.1111/avj.12836.
44.
IsolaPZhuJYZhouT, et al. Image-to-image translation with conditional adversarial networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR); July2017; Honolulu, HI: 5967–5976. doi:10.1109/CVPR.2017.632.
45.
JiangFJiangYZhiH, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2(4):230–243. doi:10.1136/svn-2017-000101.
46.
JumperJEvansRPritzelA, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–589. doi:10.1038/s41586-021-03819-2.
47.
KalyanKS.A survey of GPT-3 family large language models including ChatGPT and GPT-4. Nat Lang Process J. 2024;6:100048. doi:10.1016/j.nlp.2023.100048.
KiparAAleksandersenMBenazziC, et al. The ECVP/ESVP summer school in veterinary pathology: high-standard, structured training for young veterinary pathologists. J Vet Med Educ. 2007;34(4):485–491. doi:10.3138/jvme.34.4.485.
50.
KiparAAleksandersenMBenazziC, et al. The European College of Veterinary Pathologists (ECVP): the professional body for European veterinary pathologists. J Vet Med Educ. 2007;34(4):473–477. doi:10.3138/jvme.34.4.473.
51.
KorhonenAGlavašGVulićI, et al. SemEval-2020 task 2: predicting multilingual and cross-lingual (graded) lexical entailment. In: Proceedings of the Fourteenth Workshop on Semantic Evaluation; December2020; Barcelona. doi:10.18653/v1/2020.semeval-1.2.
52.
KuhailMAAlturkiNAlramlawiS, et al. Interacting with educational chatbots: a systematic review. Educ Inf Technol. 2023;28(1):973–1018. doi:10.1007/s10639-022-11177-3.
53.
LeeDKimJMoonWJ, et al. CollaGAN: collaborative GAN for missing image data imputation. In: 2019 IEEECVF Conf Comput Vis Pattern Recognit CVPR; June2019; Long Beach, CA: 2482–2491. doi:10.1109/CVPR.2019.00259.
54.
LeeJYoonWKimS, et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinforma Oxf Engl. 2020;36(4):1234–1240. doi:10.1093/bioinformatics/btz682.
55.
LewisPPerezEPiktusA, et al. Retrieval-augmented generation for knowledge-intensive NLP tasks. arXiv May22, 2020. Accessed September 12, 2024. https://arxiv.org/abs/2005.11401v4.
56.
LitjensGKooiTBejnordiBE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. doi:10.1016/j.media.2017.07.005.
57.
López-ÚbedaPDíaz-GalianoMCUreña-LópezLA, et al. Combining word embeddings to extract chemical and drug entities in biomedical literature. BMC Bioinformatics. 2021;22(suppl 1):599. doi:10.1186/s12859-021-04188-3.
58.
López-ÚbedaPMartín-NoguerolTAneiros-FernándezJ, et al. Natural language processing in pathology: current trends and future insights. Am J Pathol. 2022;192:1486–1495. doi:10.1016/j.ajpath.2022.07.012.
59.
LuJYangJBatraD, et al. Hierarchical question-image co-attention for visual question answering [Published online ahead of print January 19, 2017]. arXiv. doi:10.48550/arXiv.1606.00061.
60.
LuMYChenBWilliamsonDFK, et al. A multimodal generative AI copilot for human pathology. Nature. 2024;634: 466–473. doi:10.1038/s41586-024-07618-3.
61.
LuoNZhongXSuL, et al. Artificial intelligence-assisted dermatology diagnosis: from unimodal to multimodal. Comput Biol Med. 2023;165:107413. doi:10.1016/j.compbiomed.2023.107413.
62.
MiaoJThongprayoonCSuppadungsukS, et al. Integrating retrieval-augmented generation with large language models in nephrology: advancing practical applications. Med Kaunas Lith. 2024;60(3):445. doi:10.3390/medicina60030445.
63.
NagalakshmiRKhanSBKumarAV, et al. Enhancing drug discovery and patient care through advanced analytics with the power of NLP and machine learning in pharmaceutical data interpretation. SLAS Technol. 2025;31:100238. doi:10.1016/j.slast.2024.100238.
64.
NgSMasaroneSWatsonD, et al. The benefits and pitfalls of machine learning for biomarker discovery. Cell Tissue Res. 2023;394:17–31. doi:10.1007/s00441-023-03816-z.
65.
NyrupR.The limits of value transparency in machine learning. Philos Sci. 2022;89(5):1054–1064. doi:10.1017/psa.2022.61.
66.
OmarMUllanatVLodaM, et al. ChatGPT for digital pathology research. Lancet Digit Health. 2024;6(8):e595–e600. doi:10.1016/S2589-7500(24)00114-6.
OrelECigleneckiIThiabaudA, et al. An automated literature review tool (LiteRev) for streamlining and accelerating research using natural language processing and machine learning: descriptive performance evaluation study. J Med Internet Res. 2023;25:e39736. doi:10.2196/39736.
PankajakshanRBiswalSGovindarajuluY, et al. Mapping LLM security landscapes: a comprehensive stakeholder risk assessment proposal. Published March 20, 2024. Accessed October 16, 2024. http://arxiv.org/abs/2403.13309.
73.
PerkelJM.Six tips for better coding with ChatGPT. Nature. 2023;618(7964):422–423. doi:10.1038/d41586-023-01833-0.
74.
PhuongMHutterM.Formal algorithms for transformers [Published online ahead of print July 19, 2022]. arXiv. doi:10.48550/arXiv.2207.09238.
75.
PonsEBraunLMMHuninkMGM, et al. Natural language processing in radiology: a systematic review. Radiology. 2016;279(2):329–343. doi:10.1148/radiol.16142770.
PressmanSMBornaSGomez-CabelloCA, et al. Clinical and surgical applications of large language models: a systematic review. J Clin Med. 2024;13(11):3041. doi:10.3390/jcm13113041.
78.
RajkomarADeanJKohaneI.Machine learning in medicine. N Engl J Med. 2019;380(14):1347–1358. doi:10.1056/NEJMra1814259.
79.
RamspekCLJagerKJDekkerFW, et al. External validation of prognostic models: what, why, how, when and where?Clin Kidney J. 2021;14(1):49–58. doi:10.1093/ckj/sfaa188.
80.
RanaMBhushanM.Machine learning and deep learning approach for medical image analysis: diagnosis to detection. Multimed Tools Appl. 2023;82(17):26731–26769. doi:10.1007/s11042-022-14305-w.
ReddyRN.Universal language model fine-tuning for text classification. Int J Res Appl Sci Eng Technol. 2023;11:1–8. doi:10.22214/ijraset.2023.53887.
83.
ReynoldsLMcDonellK.Prompt programming for large language models: beyond the few-shot paradigm. Published February 15, 2021. Accessed September 11, 2024. http://arxiv.org/abs/2102.07350.
84.
Roman-NaranjoPParra-PerezAMLopez-EscamezJA.A systematic review on machine learning approaches in the diagnosis and prognosis of rare genetic diseases. J Biomed Inform. 2023;143:104429. doi:10.1016/j.jbi.2023.104429.
85.
RossPSpatesK.Considering the safety and quality of artificial intelligence in health care. Jt Comm J Qual Patient Saf. 2020;46(10):596–599. doi:10.1016/j.jcjq.2020.08.002.
86.
SachdevaNColemanBKangWC, et al. How to train data-efficient LLMs [Published online ahead of print February 15, 2024]. arXiv. doi:10.48550/arXiv.2402.09668.
87.
SandersonK.GPT-4 is here: what scientists think. Nature. 2023;615(7954):773–773. doi:10.1038/d41586-023-00816-5.
88.
SantagostinoSFAssenmacherCATarrantJC, et al. Mechanisms of regulated cell death: current perspectives. Vet Pathol. 2021;58(4):596–623. doi:10.1177/03009858211005537.
89.
SunZLinMZhuQ, et al. A scoping review on multimodal deep learning in biomedical images and texts. J Biomed Inform. 2023;146:104482. doi:10.1016/j.jbi.2023.104482.
90.
TerranovaNRenardDShahinMH, et al. Artificial intelligence for quantitative modeling in drug discovery and development: an innovation & quality (IQ) consortium perspective on use cases and best practices. Clin Pharmacol Ther. 2023;115:658–672. doi:10.1002/cpt.3053.
91.
TopolEJ.High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi:10.1038/s41591-018-0300-7.
92.
Van DerlaakJGrünbergKBoisclairJ, et al. A central repository of digital pathology slides to boost the development of artificial intelligence. Bigpicture. Accessed September 11, 2024. https://bigpicture.eu/.
93.
Van KempenEJPostMMannilM, et al. Performance of machine learning algorithms for glioma segmentation of brain MRI: a systematic literature review and meta-analysis. Eur Radiol. 2021;31(12):9638–9653. doi:10.1007/s00330-021-08035-0.
94.
Van KolfschootenHvan OirschotJ. The EU artificial intelligence act (2024): implications for healthcare. Health Policy. 2024;149:105152. doi:10.1016/j.healthpol.2024.105152.
95.
Van RoyenFSAsselbergsFWAlfonsoF, et al. Five critical quality criteria for artificial intelligence-based prediction models. Eur Heart J. 2023;44(46):4831–4834. doi:10.1093/eurheartj/ehad727.
WangCOngJWangC, et al. Potential for GPT technology to optimize future clinical decision-making using retrieval-augmented generation. Ann Biomed Eng. 2024;52(5):1115–1118. doi:10.1007/s10439-023-03327-6.
98.
WatariTTakagiSSakaguchiK, et al. Performance comparison of ChatGPT-4 and Japanese medical residents in the general medicine in-training examination: comparison study. JMIR Med Educ. 2023;9:e52202. doi:10.2196/52202.
99.
WeizenbaumJ.ELIZA—a computer program for the study of natural language communication between man and machine. Commun ACM. 1966;9(1):36–45. doi:10.1145/365153.365168.
100.
WengWHWagholikarKBMcCrayAT, et al. Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach. BMC Med Inform Decis Mak. 2017;17(1):155. doi:10.1186/s12911-017-0556-8.
WuLZhengZQiuZ, et al. A survey on large language models for recommendation. 2024;27:60. doi:10.48550/arXiv.2305.19860.
103.
XuMYoonSFuentesA, et al. A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognit. 2023;137:109347. doi:10.1016/j.patcog.2023.109347.
104.
YangGXuH.Complex-valued relative positional encodings for transformer. In: 3rd International Conference on Neural Networks, Information and Communication Engineering (NNICE);February2023; Guangzhou, China. doi:10.1109/nnice58320.2023.10105716.
ZhangCBengioSHardtM, et al. Understanding deep learning requires rethinking generalization. Published February 26, 2017. Accessed October 3, 2024. http://arxiv.org/abs/1611.03530.
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