Medical Technology

@ManaMSF94 10 months ago

Happy to share our latest publication, a collaboration between #MachineLearning Tools/Research and Education subcommittees of @SIIM_Tweets led by @ShahriarFaghani ! Our study focuses on reproducibility of DL models for medicallmaging #TheJDI http://rdcu.be/dgajs

@theomitsa 11 months ago

https://arxiv.org/abs/2306.09968 ClinicalGPT: Large Language Models Finetuned with Diverse Medical Data #MachineLearning, #DataScience, #ChatGPT @Khulood_Almani @enilev @EstelaMandela @KevinClarity @Shi4Tech @bimedotcom @RLDI_Lamy @Analytics_659 @JagersbergKnut

@moorejh 11 months ago

A flexible symbolic regression method for constructing interpretable clinical prediction models - Nature Digital Medicine https://www.nature.com/articles/s41746-023-00833-8 #machinelearning #bioinformatics #geneticprogramming #symbolicregression

@Marktechpost 1 year ago

A Research Group from Stanford Studied the Possible Fine-Tuning Techniques to Generalize Latent Diffusion Models for Medical Imaging Domains Quick Read: https://www.marktechpost.com/2023/03/30/a-research-group-from-stanford-studied-the-possible-fine-tuning-techniques-to-generalize-latent-diffusion-models-for-medical-imaging-domains/ Paper: https://arxiv.org/pdf/2210.04133.pdf #ArtificialIntelligence #MachineLearning

@medical_xpress 1 year ago

#Machinelearning models rank predictive risks for Alzheimer's disease @OhioState @SciReports https://doi.org/gr2qp7 https://medicalxpress.com/news/2023-03-machine-alzheimer-disease.html

@NVIDIAHealth 1 year ago

Discover how by relying on data-driven insights, including #MachineLearning models and AI-powered medical devices, #smarthospital can enhance the work of healthcare professionals and hospital management, resulting in a better and faster care. https://nvda.ws/3V1OUuO

@freddytn 1 year ago

In #machinelearning, synthetic data can offer real performance improvements Models trained on synthetic data ➡️ eliminate privacy, copyright, & ethical concerns from using real data @MIT_CSAIL @MITEECS @MIT_SCC @MITIBMLab #health #healthcare #medicine https://news.mit.edu/2022/synthetic-data-ai-improvements-1103

@Path_AI 1 year ago

This week @Google introduced a suite of tools designed to make medical images more interoperable. We are encouraged to see that helping develop #artificialintelligence and #machinelearning models around them will be a key element of the initiative. http://tinyurl.com/mvn84bt3

@freddytn 1 year ago

Shifting #MachineLearning for #healthcare from development to deployment & from models to data By A Zhang, L Xing, J Zou & J Wu Data-centric view of #innovation + challenges #health #medicine #datascience #artificialintelligence @natBME @NaturePortfolio https://www.nature.com/articles/s41551-022-00898-y

@H2020MyPal 1 year ago

An interesting study recently published in @PalliativeMedJ looks at the use of #MachineLearning models to detect social distress, spiritual pain as well as severe symptoms in end-of-life #cancer patients using data from medical records. Read more here: https://journals.sagepub.com/doi/full/10.1177/02692163221105595

@SwissCognitive 2 years ago

Pharmaceutical companies are using #ArtificialIntelligence to streamline the process of discovering new #medicines. #MachineLearning models doing it in minutes what might take humans months to achieve manually🦾 cc @joana_ut @ageitgey @mvollmer1 #AI http://ow.ly/Vp0b50ISl2k

@ricardovinuesa 2 years ago

In my latest piece published in @YuanCommunity, I discuss the importance of #Interpretability in #DeepLearning models, particularly in the context of #Medical applications. We need to open the #BlackBox! More info: https://www.nature.com/articles/s42256-021-00414-y #SDGs #MachineLearning #AI #Interpretable https://twitter.com/YuanCommunity/status/1495952256533168128

@odsc 2 years ago

Results published recently in Nature Medicine demonstrate that federated learning builds powerful AI models that generalize across healthcare institutions. #DataScience #FederatedLearning #Healthcare https://hubs.li/H0XF-170

@nelsonSpinto 2 years ago

My surprised face reading this article: Great work by @kdpsinghlab et al. Lesson learned: #DataScience models in medicine are going nowhere without thorough evaluation and post-implementation monitoring. https://twitter.com/jamainternalmed/status/1407005406514319361

@PinakiLaskar 2 years ago

Why are #machinelearning models trained to make medical decisions that perform at nearly the same level as human experts not in clinical use? https://www.linkedin.com/posts/pinakilaskar_machinelearning-algorithms-deeplearning-activity-6806429189512728576-8Dhl #BigData #Analytics #DataScience #Python #RStats #JavaScript #ReactJS #Serverless #Linux #Coding #100DaysofCode

@andrewdfeldman 3 years ago

Cerebras Systems today announced our collaboration with @AstraZeneca using Cerebras CS-1 to advance drug discovery – specifically training large-scale NLP models to enable rapid medical literature search on PubMed #AI #NLP #pharmatech #pharma #DL #datascience https://twitter.com/CerebrasSystems/status/1386710936891936772

@FelipeKitamura 3 years ago

1/20ish Do you train #MachineLearning models on #medicalimaging #data? These are a few tips to help prepare your data. A #tweetorial for #radres, #rads and #DataScientists. Inspired by a recent paper in @radiology_rsna https://pubs.rsna.org/doi/pdf/10.1148/radiol.2020192224

@AmineKorchiMD 4 years ago

✍️Automatic annotation of medical images is the holy grail of #AI in #Radiology. This paper describes #MachineLearning models to generate annotations for review & appeared to expedite annotation with high sensitivity at the expense of specificity. https://link.springer.com/article/10.1007/s10278-019-00299-9?wt_mc=alerts.TOCjournals&utm_source=toc&utm_medium=email&utm_campaign=toc_10278_33_2

@GSCollins 4 years ago

Reporting quality of studies using #MachineLearning models for medical diagnosis by @epi_afro "All studies in this review failed to use reporting guidelines, and a large proportion of them lacked adequate detail..." https://tinyurl.com/s37a59w

@nasmoutiphd 4 years ago

Google developed Coral: a portable neural network accelerator. It will make it possible to use models locally without the need for cloud. Sending data to the cloud is a security risk for sensitive data (eg medical records). #MachineLearning #DataScience https://www.theverge.com/platform/amp/2020/1/14/21065141/google-coral-ai-edge-computing-products-applications-cloud?fbclid=IwAR1xGpoS5u-6qqSKDTBCztUlz5Os8KbFVg3aVTdHEXM1nQmEzvkvubv5BmU

@ewenharrison 4 years ago

Black box models are not acceptable for high stakes decisions in medicine. Models must be interpretable, not just explainable (explanations are wrong by definition). Here’s your great long-read for today by @CynthiaRudin https://arxiv.org/abs/1811.10154 #rstats #DataScience