Posts Tagged ‘Machine Learning’
Advancing Clinical Practice: The Potential of Multimodal Technology in Modern Medicine.
Link: https://doi.org/10.3390/jcm13206246 Authors: Artsi, Yaara; Sorin, Vera; Glicksberg, Benjamin S; Nadkarni, Girish N; Klang, Eyal Abstract: Multimodal technology is poised to revolutionize clinical practice by integrating artificial intelligence with traditional diagnostic modalities. This evolution traces its roots from Hippocrates’ humoral theory to the use of sophisticated AI-driven platforms that synthesize data across multiple sensory channels.…
Read MoreA foundation model for clinician-centered drug repurposing.
Link: https://doi.org/10.1038/s41591-024-03233-x Authors: Huang, Kexin; Chandak, Payal; Wang, Qianwen; Havaldar, Shreyas; Vaid, Akhil; Leskovec, Jure; Nadkarni, Girish N; Glicksberg, Benjamin S; Gehlenborg, Nils; Zitnik, Marinka Abstract: Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs for new diseases. The clinical utility of drug-repurposing artificial…
Read MoreUnveiling the Future of Postoperative Outcomes Prediction: The Role of Machine Learning and Trust in Healthcare.
Link: https://doi.org/10.1007/s10916-024-02106-7 Authors: Hofer, Ira S; Wax, David B Abstract:
Read MoreAdvancing rheumatology with natural language processing: insights and prospects from a systematic review.
Link: https://doi.org/10.1093/rap/rkae120 Authors: Omar, Mahmud; Naffaa, Mohammad E; Glicksberg, Benjamin S; Reuveni, Hagar; Nadkarni, Girish N; Klang, Eyal Abstract: Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and…
Read MoreA Novel Digital Twin Strategy to Examine the Implications of Randomized Clinical Trials for Real-World Populations.
Link: https://doi.org/10.1101/2024.03.25.24304868 Authors: Thangaraj, Phyllis M; Shankar, Sumukh Vasisht; Huang, Sicong; Nadkarni, Girish N; Mortazavi, Bobak J; Oikonomou, Evangelos K; Khera, Rohan Abstract: Randomized clinical trials (RCTs) are essential to guide medical practice; however, their generalizability to a given population is often uncertain. We developed a statistically informed Generative Adversarial Network (GAN) model, RCT-Twin-GAN, that…
Read MoreDerivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension.
Link: https://doi.org/10.1038/s41746-024-01227-0 Authors: Gulamali, Faris; Jayaraman, Pushkala; Sawant, Ashwin S; Desman, Jacob; Fox, Benjamin; Chang, Annette; Soong, Brian Y; Arivazagan, Naveen; Reynolds, Alexandra S; Duong, Son Q; Vaid, Akhil; Kovatch, Patricia; Freeman, Robert; Hofer, Ira S; Sakhuja, Ankit; Dangayach, Neha S; Reich, David S; Charney, Alexander W; Nadkarni, Girish N Abstract: Increased intracranial pressure (ICP)…
Read MoreAssessing GPT-4 multimodal performance in radiological image analysis.
Link: https://doi.org/10.1007/s00330-024-11035-5 Authors: Brin, Dana; Sorin, Vera; Barash, Yiftach; Konen, Eli; Glicksberg, Benjamin S; Nadkarni, Girish N; Klang, Eyal Abstract: This study aims to assess the performance of a multimodal artificial intelligence (AI) model capable of analyzing both images and textual data (GPT-4V), in interpreting radiological images. It focuses on a range of modalities, anatomical…
Read MoreMultimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population.
Link: https://doi.org/10.1038/s41746-024-01218-1 Authors: Jiang, Joy; Thi Vy, Ha My; Charney, Alexander; Kovatch, Patricia; Reddy, Vivek; Jayaraman, Pushkala; Do, Ron; Khera, Rohan; Chugh, Sumeet; Bhatt, Deepak L; Vaid, Akhil; Lampert, Joshua; Nadkarni, Girish Nitin Abstract: Congenital long QT syndrome (LQTS) diagnosis is complicated by limited genetic testing at scale, low prevalence, and normal QT corrected interval…
Read MoreDistinguishing neonatal culture-negative sepsis from rule-out sepsis with artificial intelligence-derived graphs.
Link: https://doi.org/10.1038/s41390-024-03458-z Authors: Holmes, Emma; Kauffman, Justin; Juliano, Courtney; Duchon, Jennifer; Nadkarni, Girish N Abstract: Novel artificial intelligence methods can aide in identification of cases of conditions using only unstructured electronic health record data. This graph-based method compares comprehensive electronic health records among neonates using temporal data. This provides a scalable solution to distinguish culture…
Read MoreAdvancing radiology practice and research: harnessing the potential of large language models amidst imperfections.
Link: https://doi.org/10.1093/bjro/tzae022 Authors: Klang, Eyal; Alper, Lee; Sorin, Vera; Barash, Yiftach; Nadkarni, Girish N; Zimlichman, Eyal Abstract: Large language models (LLMs) are transforming the field of natural language processing (NLP). These models offer opportunities for radiologists to make a meaningful impact in their field. NLP is a part of artificial intelligence (AI) that uses computer…
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