Artificial Intelligence: What Is It and How Can It Expand the Ultrasound Potential in the Future?

During the past century, our ability to perform complex calculations massively increased, due to the availability of powerful processors, and diffuse, ubiquitious presence of personal computers for home and professional applications. Many physicians are worried by the application of Artificial Intelligence (AI) in medicine, envisioning an Asimov science-fiction scenario. But is this real? What the AI use in medicine and, particularly, ultrasonography actually entails?

Publication History

Article published online:
04 August 2020

• References

• 1 Beam AL, Kohane IS. Big Data and Machine Learning in Health Care. JAMA 2018; 319: 1317-1318
• 2 Cheirdaris DG. Artificial Neural Networks in Computer-Aided Drug Design: An Overview of Recent Advances. Adv Exp Med Biol 2020; 1194: 115-125
• 3 Deng Y, Xu X, Qiu Y. et al. A multimodal deep learning framework for predicting drug-drug interaction events. Bioinformatics 2020;
• 4 Gutierrez G. Artificial Intelligence in the Intensive Care Unit. Crit Care 2020; 24: 101
• 5 Akkus Z, Cai J, Boonrod A. et al. A Survey of Deep-Learning Applications in Ultrasound: Artificial Intelligence-Powered Ultrasound for Improving Clinical Workflow. J Am Coll Radiol 2019; 16: 1318-1328
• 6 Hinton G. Deep Learning-A Technology With the Potential to Transform Health Care. JAMA 2018; 320: 1101-1102
• 7 Ting DSW, Cheung CY, Lim G. et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA 2017; 318: 2211-2223
• 8 Elliott Range DD, Dov D, Kovalsky SZ. et al. Application of a machine learning algorithm to predict malignancy in thyroid cytopathology. Cancer Cytopathol 2020; 128: 287-295
• 9 Tumino D, Grani G, Di Stefano M. et al. Nodular Thyroid Disease in the Era of Precision Medicine. Front Endocrinol (Lausanne) 2019; 10: 907
• 10 Cabitza F, Rasoini R, Gensini GF. Unintended Consequences of Machine Learning in Medicine. JAMA 2017; 318: 517-518
• 11 Ko SY, Lee JH, Yoon JH. et al. Deep convolutional neural network for the diagnosis of thyroid nodules on ultrasound. Head Neck 2019; 41: 885-891
• 12 Fresilli D, Grani G, De Pascali ML. et al. Computer-aided diagnostic system for thyroid nodule sonographic evaluation outperforms the specificity of less experienced examiners. J Ultrasound 2020; 23: 169-174
• 13 Di Segni M, de Soccio V, Cantisani V. et al. Automated Classification of Focal Breast Lesions According to S-detect: Validation and Role as a Clinical and Teaching Tool. J Ultrasound 2018; 21: 105-118
• 14 Jeong EY, Kim HL, Ha EJ. et al. Computer-aided diagnosis system for thyroid nodules on ultrasonography: diagnostic performance and reproducibility based on the experience level of operators. Eur Radiol 2019; 29: 1978-1985
• 15 Săftoiu A, Gilja OH, Sidhu PS. et al. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Elastography in Non-Hepatic Applications: Update 2018. Ultraschall in Med 2019; 40: 425-453
• 16 Sidhu PS, Cantisani V, Dietrich CF. et al. The EFSUMB Guidelines and Recommendations for the Clinical Practice of Contrast-Enhanced Ultrasound (CEUS) in Non-Hepatic Applications: Update 2017 (Short Version). Ultraschall in Med 2018; 39: 154-180
• 17 Dietrich CF, Lorentzen T, Appelbaum L. et al. EFSUMB Guidelines on Interventional Ultrasound (INVUS), Part III – Abdominal Treatment Procedures (Short Version). Ultraschall in Med 2016; 37: 27-45
• 18 Dietrich CF, Lorentzen T, Appelbaum L. et al. EFSUMB Guidelines on Interventional Ultrasound (INVUS), Part III – Abdominal Treatment Procedures (Long Version). Ultraschall in Med 2016; 37: E1-E32