Recent advancements in artificial intelligence (AI) are showing significant promise in the field of radiology, with various studies and applications highlighting the technology's potential to improve diagnostic accuracy and efficiency. AI competitions have mobilized a global community to tackle real-world medical challenges, leading to developments such as the use of weakly supervised deep learning for large-scale screening of Barrett’s esophagus in Nature Communications and the creation of a visual-language foundation model for computational pathology in Nature Medicine. Notably, the performance of ChatGPT on Brazilian Radiology and Diagnostic Imaging and Mammography Board Examinations, as reported by @haritrivedimd, @judywawira, and @emoryradiology, has demonstrated the capabilities of AI in understanding and interpreting medical imaging. Additionally, studies have assessed the impact of deep learning reconstruction methods on reducing MRI scan times and increasing throughput in clinical settings, as well as the role of AI in enhancing lung cancer screening across U.S- and Japan-based populations. An AI system has also been shown to improve the performance of general radiologists and breast imaging specialists. The AI application 'Mia' from NHS has been credited with identifying cancer cells missed by human radiologists, underscoring the potential of AI to revolutionize healthcare. Despite concerns about AI replacing radiologists, the technology is increasingly seen as a tool to augment human expertise, with its applications extending beyond diagnostics to include telemedicine, clinical trials, and patient care management. The healthcare AI market is expected to grow significantly, with a projected value of $208 billion, reflecting the expanding role of AI in the medical field.
"A second study evaluated a deep neural network performance compared with 15 radiologists for diagnosis of heart abnormalities (hypertrophy or dilation of the left ventricle) from chest X-rays. The A.I. accuracy exceeded all of the radiologists."
A study in @NatureMedicine examining the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks finds that conventional experience-based factors fail to reliably predict the impact of AI assistance. https://t.co/wucqxn6OlF
Deep learning #reconstruction methods were associated with reductions in scan and room times in a clinical setting https://t.co/HK5jxKoCid @AmishDoshiMD @mountsinainyc #MRI #DL #radiology https://t.co/sC5krNi4IW
❣️Did you know AI's heartbeat is growing stronger in healthcare every day? Beyond robot-assisted surgeries and early diagnostics, AI's influence spans telemedicine, clinical trials, and patient care management. AI healthcare market value is anticipated to skyrocket to $208…
Predictions of #AI replacing radiologists have not aged well #GenAI via @misha_saul https://t.co/99yGPuE52V
Commentary on an important advance in applying weakly supervised learning for medical imaging https://t.co/sxUaFXMiGv @Kareem_A_Wahid @MDAndersonNews #DeepLearning #AI #ML https://t.co/g1p5Ik9T9O
Follow the @Radiology_AI blog -- The Vasty Deep! https://t.co/abJpdqDRZw #ML #MachineLearning #DeepLearning https://t.co/boieASmav1
hmm is ai finally coming for the radiologists? https://t.co/LuD7dLCWBU
AI to revolutionize healthcare AI Mia from NHS is already finding cancer cells that professional human radiologists had missed. we need to understand that AI will do more good for humanity than bad. https://t.co/EB1xk1Q6sl
#AI system improved performance of general radiologists & breast imaging specialists https://t.co/9Jt57tmwhu @UWRadiology @uwsph @DanaFarber #mammo #DeepLearning #MachineLearning https://t.co/XisfC0sNDj
Impact of AI on #LungCancer screening on U.S- and Japan-based settings and populations https://t.co/FeZhWUpaLf @GoogleHealth #DeepLearning #ChestRad #OncoRad https://t.co/sWIxArvB52
Study assessed deep learning reconstruction methods to reduce #MRI scan times and increase throughput in a real clinical setting https://t.co/HK5jxKoCid @AmishDoshiMD @mountsinainyc #radiology #AI #ML https://t.co/xOwuepECJt
AI saves lives today, and will save even more tomorrow. https://t.co/StSymSpNXK
"The #LLM Will See You Now": Performance of #ChatGPT on the Brazilian Radiology and Diagnostic Imaging and Mammography Board Examinations https://t.co/VoWger1zQC @haritrivedimd @judywawira @emoryradiology #GPT4 #DeepLearning #AI https://t.co/hOLheLvWHT
A visual-language foundation model for computational pathology | Nature Medicine https://t.co/HdOF9To5LH #deeplearning
Enabling large-scale screening of Barrett’s esophagus using weakly supervised deep learning in histopathology | Nature Communications https://t.co/B0LgMmHoEI #deeplearning https://t.co/Yq09YUvF3j
#AI competitions have engaged a global community to effectively address real-world medical problems https://t.co/gUwsdHHBiD @lmprevedello @MonganMD @mattlungrenMD #MachineLearning #competition #ML https://t.co/nzaeLC4HRt