1
|
Robleto E, Habashi A, Kaplan MAB, Riley RL, Zhang C, Bianchi L, Shehadeh LA. Medical students' perceptions of an artificial intelligence (AI) assisted diagnosing program. MEDICAL TEACHER 2024; 46:1180-1186. [PMID: 38306667 DOI: 10.1080/0142159x.2024.2305369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 01/10/2024] [Indexed: 02/04/2024]
Abstract
As artificial intelligence (AI) assisted diagnosing systems become accessible and user-friendly, evaluating how first-year medical students perceive such systems holds substantial importance in medical education. This study aimed to assess medical students' perceptions of an AI-assisted diagnostic tool known as 'Glass AI.' Data was collected from first year medical students enrolled in a 1.5-week Cell Physiology pre-clerkship unit. Students voluntarily participated in an activity that involved implementation of Glass AI to solve a clinical case. A questionnaire was designed using 3 domains: 1) immediate experience with Glass AI, 2) potential for Glass AI utilization in medical education, and 3) student deliberations of AI-assisted diagnostic systems for future healthcare environments. 73/202 (36.10%) of students completed the survey. 96% of the participants noted that Glass AI increased confidence in the diagnosis, 43% thought Glass AI lacked sufficient explanation, and 68% expressed risk concerns for the physician workforce. Students expressed future positive outlooks involving AI-assisted diagnosing systems in healthcare, provided strict regulations, are set to protect patient privacy and safety, address legal liability, remove system biases, and improve quality of patient care. In conclusion, first year medical students are aware that AI will play a role in their careers as students and future physicians.
Collapse
Affiliation(s)
- Emely Robleto
- Department of Medicine, Division of Cardiology, University of Miami Miller School of Medicine, Miami, FL, USA
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Ali Habashi
- Department of Cinematic Arts, School of Communication, University of Miami, Miami, FL, USA
| | - Mary-Ann Benites Kaplan
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Richard L Riley
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Microbiology and Immunology, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Chi Zhang
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Laura Bianchi
- Department of Physiology and Biophysics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Lina A Shehadeh
- Department of Medicine, Division of Cardiology, University of Miami Miller School of Medicine, Miami, FL, USA
- Interdisciplinary Stem Cell Institute, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Medical Education, University of Miami Miller School of Medicine, Miami, FL, USA
| |
Collapse
|
2
|
Grani G, Sponziello M, Filetti S, Durante C. Thyroid nodules: diagnosis and management. Nat Rev Endocrinol 2024:10.1038/s41574-024-01025-4. [PMID: 39152228 DOI: 10.1038/s41574-024-01025-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/22/2024] [Indexed: 08/19/2024]
Abstract
Thyroid nodules, with a prevalence of almost 25% in the general population, are a common occurrence. Their prevalence varies considerably depending on demographics such as age and sex as well as the presence of risk factors. This article provides a comprehensive overview of the prevalence, risk stratification and current management strategies for thyroid nodules, with a particular focus on changes in diagnostic and therapeutic protocols that have occurred over the past 10 years. Several sonography-based stratification systems (such as Thyroid Imaging Reporting and Data Systems (TIRADS)) might help to predict the malignancy risk of nodules, potentially eliminating the need for biopsy in many instances. However, large or suspicious nodules necessitate cytological evaluation following fine-needle aspiration biopsy for accurate classification. In the case of cytology yielding indeterminate results, additional tools, such as molecular testing, can assist in guiding the management plan. Surgery is no longer the only treatment for symptomatic or malignant nodules: active surveillance or local ablative treatments might be beneficial for appropriately selected patients. To enhance clinician-patient interactions and discussions about diagnostic options, shared decision-making tools have been developed. A personalized, risk-based protocol promotes high-quality care while minimizing costs and unnecessary testing.
Collapse
Affiliation(s)
- Giorgio Grani
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Marialuisa Sponziello
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Sebastiano Filetti
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy
| | - Cosimo Durante
- Department of Translational and Precision Medicine, Sapienza University of Rome, Rome, Italy.
| |
Collapse
|
3
|
Lee SE, Kim HJ, Jung HK, Jung JH, Jeon JH, Lee JH, Hong H, Lee EJ, Kim D, Kwak JY. Improving the diagnostic performance of inexperienced readers for thyroid nodules through digital self-learning and artificial intelligence assistance. Front Endocrinol (Lausanne) 2024; 15:1372397. [PMID: 39015174 PMCID: PMC11249553 DOI: 10.3389/fendo.2024.1372397] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 06/12/2024] [Indexed: 07/18/2024] Open
Abstract
Background Data-driven digital learning could improve the diagnostic performance of novice students for thyroid nodules. Objective To evaluate the efficacy of digital self-learning and artificial intelligence-based computer-assisted diagnosis (AI-CAD) for inexperienced readers to diagnose thyroid nodules. Methods Between February and August 2023, a total of 26 readers (less than 1 year of experience in thyroid US from various departments) from 6 hospitals participated in this study. Readers completed an online learning session comprising 3,000 thyroid nodules annotated as benign or malignant independently. They were asked to assess a test set consisting of 120 thyroid nodules with known surgical pathology before and after a learning session. Then, they referred to AI-CAD and made their final decisions on the thyroid nodules. Diagnostic performances before and after self-training and with AI-CAD assistance were evaluated and compared between radiology residents and readers from different specialties. Results AUC (area under the receiver operating characteristic curve) improved after the self-learning session, and it improved further after radiologists referred to AI-CAD (0.679 vs 0.713 vs 0.758, p<0.05). Although the 18 radiology residents showed improved AUC (0.7 to 0.743, p=0.016) and accuracy (69.9% to 74.2%, p=0.013) after self-learning, the readers from other departments did not. With AI-CAD assistance, sensitivity (radiology 70.3% to 74.9%, others 67.9% to 82.3%, all p<0.05) and accuracy (radiology 74.2% to 77.1%, others 64.4% to 72.8%, all p <0.05) improved in all readers. Conclusion While AI-CAD assistance helps improve the diagnostic performance of all inexperienced readers for thyroid nodules, self-learning was only effective for radiology residents with more background knowledge of ultrasonography. Clinical Impact Online self-learning, along with AI-CAD assistance, can effectively enhance the diagnostic performance of radiology residents in thyroid cancer.
Collapse
Affiliation(s)
- Si Eun Lee
- Department of Radiology, Yongin Severance Hospital, College of Medicine, Yonsei University, Yongin-si, Republic of Korea
| | - Hye Jung Kim
- Department of Radiology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
| | - Hae Kyoung Jung
- Department of Radiology, CHA University Bundang Medical Center, Seongnam-si, Republic of Korea
| | - Jing Hyang Jung
- Department of Surgery, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
| | - Jae-Han Jeon
- Department of Endocrinology, Kyungpook National University Chilgok Hospital, Daegu, Republic of Korea
| | - Jin Hee Lee
- Department of Radiology, Keimyung University Dongsan Hospital, Daegu, Republic of Korea
| | - Hanpyo Hong
- Department of Radiology, Yongin Severance Hospital, College of Medicine, Yonsei University, Yongin-si, Republic of Korea
| | - Eun Jung Lee
- Department of Computational Science and Engineering, Yonsei University, Seoul, Republic of Korea
| | - Daham Kim
- Department of Endocrinology, College of Medicine, Yonsei University, Seoul, Republic of Korea
| | - Jin Young Kwak
- Department of Radiology, College of Medicine, Yonsei University, Seoul, Republic of Korea
| |
Collapse
|
4
|
Liu M, Pan N. Quantitative ultrasound imaging parameters in patients with cancerous thyroid nodules: development of a diagnostic model. Am J Transl Res 2024; 16:2645-2653. [PMID: 39006293 PMCID: PMC11236663 DOI: 10.62347/wedg9279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/24/2024] [Indexed: 07/16/2024]
Abstract
OBJECTIVE This study aimed to develop a diagnostic model utilizing quantitative ultrasound parameters to accurately differentiate benign from malignant thyroid nodules. METHODS A retrospective analysis of 194 patients with thyroid nodules, encompassing 65 malignant and 129 benign cases, was performed. Clinical data, ultrasound characteristics, and hemodynamic indicators were compared. Receiver operating characteristic (ROC) curves and logistic regression analysis identified independent diagnostic markers. RESULTS No significant differences in clinical data were observed between the groups (P>0.05). Malignant nodules, however, were more likely to exhibit solid composition, hypoechoicity, irregular shapes, calcifications, central blood flow, and unclear margins (P<0.05). Hemodynamic parameters showed that malignant nodules had lower end-diastolic volume (EDV) but higher peak systolic velocity (PSV), resistive index (RI), and vascularization flow index (VFI) (P<0.001). Independent diagnostic factors identified included calcification, margin definition, RI, and VFI. A risk prediction model was formulated, demonstrating significantly lower scores for benign nodules (P<0.0001), achieving an ROC area of 0.964. CONCLUSION Color Doppler ultrasound effectively distinguishes malignant from benign thyroid nodules. The diagnostic model emphasizes the importance of calcification, margin clarity, RI, and VFI as critical elements, enhancing the accuracy of thyroid nodule characterization and facilitating informed clinical decisions.
Collapse
Affiliation(s)
- Mingyang Liu
- Department of Ultrasound, Xingtai People's Hospital No. 16 Hongxing Street, Xingtai 054500, Hebei, China
| | - Na Pan
- Department of Hematology, Xingtai People's Hospital No. 16 Hongxing Street, Xingtai 054500, Hebei, China
| |
Collapse
|
5
|
McMahon GT. The Risks and Challenges of Artificial Intelligence in Endocrinology. J Clin Endocrinol Metab 2024; 109:e1468-e1471. [PMID: 38471009 DOI: 10.1210/clinem/dgae017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Indexed: 03/14/2024]
Abstract
Artificial intelligence (AI) holds the promise of addressing many of the numerous challenges healthcare faces, which include a growing burden of illness, an increase in chronic health conditions and disabilities due to aging and epidemiological changes, higher demand for health services, overworked and burned-out clinicians, greater societal expectations, and rising health expenditures. While technological advancements in processing power, memory, storage, and the abundance of data have empowered computers to handle increasingly complex tasks with remarkable success, AI introduces a variety of meaningful risks and challenges. Among these are issues related to accuracy and reliability, bias and equity, errors and accountability, transparency, misuse, and privacy of data. As AI systems continue to rapidly integrate into healthcare settings, it is crucial to recognize the inherent risks they bring. These risks demand careful consideration to ensure the responsible and safe deployment of AI in healthcare.
Collapse
Affiliation(s)
- Graham T McMahon
- Accreditation Council for Continuing Medical Education, Chicago, IL 60611, USA
- Department of Medical Education and Division of Endocrinology, Metabolism and Molecular Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA
| |
Collapse
|
6
|
Zhang X, Jia C, Sun M, Ma Z. The application value of deep learning-based nomograms in benign-malignant discrimination of TI-RADS category 4 thyroid nodules. Sci Rep 2024; 14:7878. [PMID: 38570589 PMCID: PMC10991510 DOI: 10.1038/s41598-024-58668-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 04/02/2024] [Indexed: 04/05/2024] Open
Abstract
Thyroid nodules are a common occurrence, and although most are non-cancerous, some can be malignant. The American College of Radiology has developed the Thyroid Imaging Reporting and Data System (TI-RADS) to standardize the interpretation and reporting of thyroid ultrasound results. Within TI-RADS, a category 4 designation signifies a thyroid nodule with an intermediate level of suspicion for malignancy. Accurate classification of these nodules is crucial for proper management, as it can potentially reduce unnecessary surgeries and improve patient outcomes. This study utilized deep learning techniques to effectively classify TI-RADS category 4 thyroid nodules as either benign or malignant. A total of 500 patients were included in the study and randomly divided into a training group (350 patients) and a test group (150 patients). The YOLOv3 model was constructed and evaluated using various metrics, achieving an 84% accuracy in the classification of TI-RADS category 4 thyroid nodules. Based on the predictions of the model, along with clinical and ultrasound data, a nomogram was developed. The performance of the nomogram was superior in both the training and testing groups. Furthermore, the calibration curve demonstrated good agreement between predicted probabilities and actual outcomes. Decision curve analysis further confirmed that the nomogram provided greater net benefits. Ultimately, the YOLOv3 model and nomogram successfully improved the accuracy of distinguishing between benign and malignant TI-RADS category 4 thyroid nodules, which is crucial for proper management and improved patient outcomes.
Collapse
Affiliation(s)
- Xinru Zhang
- Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Jinan, 250014, China
| | - Cheng Jia
- Department of Radiology, The Second Affiliated Hospital of Shandong First Medical University, Taian, 271000, China
| | - Meng Sun
- Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Jinan, 250014, China
| | - Zhe Ma
- Department of Medical Ultrasound, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Medicine and Health Key Laboratory of Abdominal Medical Imaging, Jinan, 250014, China.
| |
Collapse
|
7
|
Crouzeix G, Caron P. Key data from the 2023 European Thyroid Association annual meeting: Management of thyroid nodules. ANNALES D'ENDOCRINOLOGIE 2024; 85:152-154. [PMID: 38311540 DOI: 10.1016/j.ando.2024.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Accepted: 01/08/2024] [Indexed: 02/06/2024]
Affiliation(s)
- Geneviève Crouzeix
- Department of Endocrinology and Metabolic Diseases, Vascular Unit, Brest University Hospital, Brest, France
| | - Philippe Caron
- Department of Endocrinology and Metabolic Diseases, CHU Larrey, 24, chemin de Pouvourville, TSA 30030, 31059 Toulouse cedex, France.
| |
Collapse
|