The Diagnostic Imaging AI (artificial intelligence) focus page provides information, videos, podcasts, and the latest news about product developments, trial results, screening guidelines, and protocol guidance that touch on the development and use of AI across the healthcare continuum.
July 23rd 2024
For women with dense breasts, the combination of mammography and supplemental breast ultrasound had a 36.4 percent higher sensitivity rate for detecting breast cancer in comparison to the combination of mammography and adjunctive AI, according to a new study.
25th Annual International Lung Cancer Congress®
July 25-27, 2024
Register Now!
2023 ASCO Direct™ Highlights: Practice-Changing Data From the Leading Oncology Conference
View More
6th Annual Precision Medicine Symposium: An Illustrated Tumor Board
October 18-19, 2024
Register Now!
Community Practice Connections™: 24th Annual International Lung Cancer Congress®
View More
19th Annual New York Lung Cancers Symposium®
November 16, 2024
Register Now!
Medical Crossfire®: How Do the Experts Select and Sequence Therapies to Optimize Patient Outcomes and Quality of Life in Advanced Prostate Cancer?
View More
Medical Crossfire®: How Does Recent Evidence on PARP Inhibitors and Combinations Inform Treatment Planning for Prostate Cancer Now and In the Future?
View More
Lung Cancer Tumor Board®: Enhancing Multidisciplinary Communication to Optimize Immunotherapy in Stage I-III NSCLC
View More
Clinical Vignettes™: The Experts Explain How They Integrate PET Imaging into Metastatic HR+ Breast Cancer Care Settings
View More
School of Breast Oncology® Live Video Webcast: Clinical Updates from San Antonio
View More
Annual Hawaii Cancer Conference
January 25-26, 2025
Register Now!
21st Annual International Symposium on Melanoma and Other Cutaneous Malignancies®
February 8, 2025
View More
Community Practice Connections™: The 2nd Annual Hawaii Lung Cancers Conference®
View More
Clinical Case Vignette Series™: 41st Annual Miami Breast Cancer Conference®
View More
Medical Crossfire®: How Can Thoracic Teams Facilitate Optimized Care of Patients With Stage I-III EGFR Mutation-Positive NSCLC?
View More
Lung Cancer Tumor Board®: How Do Emerging Data for ICIs, BiTEs, ADCs, and Targeted Strategies Address Unmet Needs in the Therapeutic Continuum for SCLC?
View More
FDA Clears Pocket-Sized ECG System and AI Technology for Detection of Cardiac Conditions
June 27th 2024Using a reduced leadset and deep neural network algorithms trained on more than 175 million electrocardiograms, the KAI 12L technology reportedly detects up to 35 cardiac determinations, including acute myocardial infarction.
Adjunctive AI Leads to 16 Percent Increase in CT Sensitivity for Incidental Pulmonary Embolism
June 20th 2024Artificial intelligence facilitated a 96.2 percent sensitivity rate for incidental pulmonary embolism (IPE) on contrast-enhanced CT chest or abdomen exams, according to new prospective research involving over 4,300 patients.
Can Deep Learning Automate Amyloid Positivity Assessment on Brain PET Imaging?
June 14th 2024In validation testing with 205 18F florbetapir PET scans from 95 patients with Alzheimer’s disease, a deep learning model demonstrated a 93.2 percent accuracy rate and a 97 percent AUC for detecting amyloid-β positivity.
Study: Adjunctive AI Imaging Software Enhances Contouring of Prostate Cancer
June 13th 2024Artificial intelligence (AI) assisted contouring of prostate cancer demonstrated superior balanced accuracy than manual standard-of-care contouring and hemigland contouring with MRI, according to a new study.
SNMMI: AI May Enhance Detection and Risk Assessment for Multiple Cancers on Whole-Body PET/CT Scans
June 10th 2024Deep transfer learning may elevate the capability of whole-body PET/CT scans to diagnose multiple cancers, ranging from breast cancer and lung cancer to melanoma and prostate cancer, according to new research presented at the SNMMI conference.
Nanox Adds AI Applications to Teleradiology Platform for CT Second Opinions
Published: June 7th 2024 | Updated: June 7th 2024Facilitating additional consultation on chest and abdominal CT scans, the Second Opinions teleradiology platform now features FDA-cleared AI tools for cardiac, bone and liver assessments.
Can Mammography-Based AI Enhance the Detection of Contralateral Breast Cancer?
June 5th 2024Offering comparable sensitivity to radiologists for detecting contralateral breast cancer on mammography images, an emerging adjunctive AI software may also facilitate earlier diagnosis, according to study findings presented at the at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting.
Use of Mammography AI Leads to 12 Percent CDR Increase and 20 Percent Decrease in Recall Rate
June 4th 2024In a retrospective study involving nearly 119,000 women, researchers found that implementation of AI into mammography screening increased the positive predictive value by 11 percent, increased small cancer detection by 8.3 percent and reduced reading workload by approximately 33 percent.
Large CT Study Shows Benefits of AI in Predicting CV Risks in Patients Without Obstructive CAD
June 3rd 2024An AI algorithm that incorporates scoring of coronary inflammation based on coronary CT angiography (CCTA) may enhance long-term cardiovascular risk stratification beyond conventional risk factor and imaging assessments, even in patients without obstructive CAD.
CT-Based AI Model May Enhance Prediction of Lung Cancer Recurrence
May 30th 2024An AI model that includes extracted radiomic features from CT scans more than doubled the sensitivity rate for preoperative prediction of lung cancer recurrence in comparison to traditional TNM staging, according to study findings to be presented at the 2024 American Society of Clinical Oncology (ASCO) Annual Meeting in Chicago.
Qure.ai to Debut Multimodality AI Platform for Lung Cancer Imaging at ASCO 2024
May 29th 2024In addition to detecting missed lung nodules on X-rays, the AI-powered Qure.ai lung cancer continuum platform reportedly automates lung nodule measurement on CT scans and facilitates multimodality reporting.
Can Deep Learning Models Improve CT Differentiation of Small Solid Pulmonary Nodules?
May 29th 2024One deep learning model had a 72.4 percent accuracy rate for differentiating between benign and malignant solid pulmonary nodules on non-contrast CT while another deep learning model demonstrated an 87.1 percent AUC for differentiating benign and inflammatory findings.