ABSTRACT
Endocrine radiology is a broad field that encompasses many subspecialties of radiology, ranging from neuroradiology to abdominal radiology. In recent years, rapid advances in information processing hardware and, in particular, deep learning methods have led to artificial intelligence (AI) research and applications, triggering rapid changes in radiology. In parallel, various scientific institutions and organizations around the world have published numerous guidelines and resources. In this context, it is emphasized that the focus should be on the power of AI to create new opportunities, describing it as a “game changer,” and ensuring its ethical use with patient benefit in mind. Radiology is at the forefront of AI in the health discipline. In addition to scientific research on AI in medical imaging, there are numerous Conformité Européenne and Food and Drug Administration approved AI products used in daily practice. These applications have the potential to transform diagnostic processes by increasing accuracy in image interpretation. There are also many areas in endocrine radiology where AI can be utilized. Within these areas, thyroid nodules and pituitary adenomas are currently at the forefront. Parathyroid adenomas, adrenal glands, and neuroendocrine tumors are other areas being studied. This article discusses the general status of AI in radiology, specific AI applications in endocrine system imaging, ethical and legal dimensions, and future projections.
Keywords:
Artificial intelligence, endocrine, medical imaging
References
1
Türk Radyoloji Derneği (TRD). Yapay zeka uygulamaları rehberi. TRD Görüntüleme Bilişimi Eğitim ve Bilimsel Araştırma Grubu; 2022.
2
Kotter E, D’Antonoli TA, Cuocolo R, Hierath M, Huisman M, Klontzas ME, et al. Guiding AI in radiology: ESR’s recommendations for effective implementation of the European AI act. Insights Imaging. 2025; 16: 33.
3
Najjar R. Redefining radiology: a review of artificial intelligence integration in medical imaging. Diagnostics (Basel). 2023; 13: 2760.
4
Zanardo M, Visser JJ, Colarieti A, Cuocolo R, Klontzas ME, Pinto Dos Santos D, et al. Impact of AI on radiology: a EuroAIM/EuSoMII 2024 survey among members of the European Society of Radiology. Insights Imaging. 2024; 15: 240.
5
Alis D, Tanyel T, Meltem E, Seker ME, Seker D, Karakaş HM, et al. Choosing the right artificial intelligence solutions for your radiology department: key factors to consider. Diagn Interv Radiol. 2024; 30: 357-65.
6
Geis JR, Brady AP, Wu CC, Spencer J, Ranschaert E, Jaremko JL, et al. Ethics of artificial intelligence in radiology: summary of the joint European and North American multisociety statement. Radiology. 2019; 293: 436-40.
7
Guerriero E, Ugga L, Cuocolo R. Artificial intelligence and pituitary adenomas: a review. Artif Intell Med Imaging. 2020; 1: 70-7.
8
Zeynalova A, Kocak B, Durmaz ES, Comunoglu N, Ozcan K, Ozcan G, et al. Preoperative evaluation of tumour consistency in pituitary macroadenomas: a machine learning-based histogram analysis on conventional T2-weighted MRI. Neuroradiology. 2019; 61: 767-74.
9
Lee DH, Park JE, Nam YK, Lee J, Kim S, Kim YH, et al. Deep learning-based thin-section MRI reconstruction improves tumour detection and delineation in pre- and post-treatment pituitary adenoma. Sci Rep. 2021; 11: 21302.
10
Choi US, Sung YW, Ogawa S. deepPGSegNet: MRI-based pituitary gland segmentation using deep learning. Front Endocrinol (Lausanne). 2024; 15: 1338743.
11
Zilka T, Benesova W. Radiomics of pituitary adenoma using computer vision: a review. Med Biol Eng Comput. 2024; 62: 3581-97.
12
Ashton J, Morrison S, Erkanli A, Wildman-Tobriner B. Assessment of the diagnostic performance of a commercially available artificial intelligence algorithm for risk stratification of thyroid nodules on ultrasound. Thyroid. 2024; 34: 1379-88.
13
Sorrenti S, Dolcetti V, Radzina M,et al. Artificial intelligence for thyroid nodule characterization: where are we standing? Cancers (Basel). 2022; 14: 3357.
14
Chen Y, Gao Z, He Y, Mai W, Li J, Zhou M, et al. An artificial intelligence model based on ACR TI-RADS characteristics for US diagnosis of thyroid nodules. Radiology. 2022; 303: 613-9.
15
Barinov L, Jairaj A, Middleton WD, D M, Beland, Kirsch J, et al. Improving the efficacy of ACR TI-RADS through deep learning-based descriptor augmentation. J Digit Imaging. 2023; 36: 2392-401.
16
Sharifi Y, Ashgzari MD, Shafiei S, Zakavi RS, Eslami S. Using deep learning for thyroid nodule risk stratification from ultrasound images. WFUMB Ultrasound Open. 2025; 3: 100082.
17
Tao Y, Yu Y, Wu T, Xu X, Dai Q, Kong H, et al. Deep learning for the diagnosis of suspicious thyroid nodules based on multimodal ultrasound images. Front Oncol. 2022; 12: 1012724.
18
Abdolali F, Shahroudnejad A, Amiri S, et al. A systematic review on the role of artificial intelligence in sonographic diagnosis of thyroid cancer: past, present and future. Front Biomed Technol. 2021; 7(4): 266-280.
19
Barat M, Gaillard M, Cottereau AS, Fishman EK, Assié G, Jouinot A, et al. Artificial intelligence in adrenal imaging: a critical review of current applications. Diagn Interv Imaging. 2023; 104: 37-42.
20
Ferro M, Tataru OS, Carrieri G, Busetto GM, Falagario UG, Maggi M, et al. Artificial intelligence and radiomics applications in adrenal lesions: a systematic review. Ther Adv Urol. 2025; 17: 17562872251352553.
21
Seow JH, Stella DL, Welman CJ, Somasundaram AJ, Gerstenmaier JF. Washed up: the end of an era for adrenal incidentaloma CT. Insights Imaging. 2025; 16: 136.
22
Qi S, Zuo Y, Chang R, Huang K, Liu J, Zhang Z. Using CT radiomic features based on machine learning models to subtype adrenal adenoma. BMC Cancer. 2023; 23: 111.
23
Altay C, Başara Akın I, Özgül AH, Adıyaman SC, Yener AS, Seçil M. Machine learning analysis of adrenal lesions: preliminary study evaluating texture analysis in the differentiation of adrenal lesions. Diagn Interv Radiol. 2023; 29: 234-43.
24
Li C, Fu Y, Yi X, Guan X, Liu L, Chen BT. Application of radiomics in adrenal incidentaloma: a literature review. Discov Oncol. 2022; 13: 112.
25
Pantelis AG, Panagopoulou PA, Lapatsanis DP. Artificial intelligence and machine learning in the diagnosis and management of gastroenteropancreatic neuroendocrine neoplasms-a scoping review. Diagnostics (Basel). 2022; 12: 874.
26
Lopez-Ramirez F, Yasrab M, Tixier F, Kawamoto S, Fishman EK, Chu LC. The role of AI in the evaluation of neuroendocrine tumors: current state of the art. Semin Nucl Med. 2025; 55: 345-57.
27
Balma M, Laudicella R, Gallio E, Gusella S, Lorenzon L, Peano S, et al. Applications of artificial intelligence and radiomics in molecular hybrid imaging and theragnostics for neuro-endocrine neoplasms (NENs). Life (Basel). 2023; 13: 1647.
28
Yi N, Mo S, Zhang Y, Jiang Q, Wang Y, Huang C, et al. An endoscopic ultrasound-based interpretable deep learning model and nomogram for distinguishing pancreatic neuroendocrine tumors from pancreatic cancer. Sci Rep. 2025; 15: 3383.
29
Merola E, Fanciulli G, Pes GM, Dore MP. Artificial intelligence for prognosis of gastro-entero-pancreatic neuroendocrine neoplasms. Cancers (Basel). 2025; 17: 1981.
30
Zhang T, Zhang Y, Liu X, Xu H, Chen C, Zhou X, et al. Application of radiomics analysis based on CT combined with machine learning in diagnostic of pancreatic neuroendocrine tumors patient’s pathological grades. Front Oncol. 2021; 10: 521831.
31
European Commission. Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence (AI Act). Brussels; 2024.
32
Biden JR. Executive order on the safe, secure, and srustworthy development and use of artificial intelligence. Exec Order No. Washington (DC): 30 Oct 2023. Federal Register. 2023;24283.
