David Hachuel wants pictures of your poop -- for science. The computer scientist-turned-entrepreneur is working to build the world's largest database of human stool photos -- up to 100,000 in all. The images will be used to teach an artificial intelligence to tell the difference between stool that's consistent with good health and stool that could be evidence of gastrointestinal ailments like irritable bowel syndrome or Crohn's disease. The color, shape and consistency of stool hold important clues that help doctors make diagnoses. Hachuel thinks the photos can form the basis of an app that nonphysicians can use to obtain such information on their own.

BOSTON & SAN FRANCISCO--(BUSINESS WIRE)--Pear Therapeutics, Inc., the leader in Prescription Digital Therapeutics (PDTs), announced today that it has entered into agreements with multiple technology innovators, including Firsthand Technology, Inc., leading researchers from the Karolinska Institute in Sweden, Cincinnati Children's Hospital Medical Center, Winterlight Labs, Inc., and NeuroLex Laboratories, Inc. These new agreements continue to bolster Pear's PDT platform, by adding to its library of digital biomarkers, machine learning algorithms, and digital therapeutics. Pear's investment in these cutting-edge technologies further supports its strategy to create the broadest and deepest toolset for the development of PDTs that redefine standard of care in a range of therapeutic areas. With access to these new technologies, Pear is positioned to develop PDTs in new disease areas, while leveraging machine learning to personalize and improve its existing PDTs. "We are excited to announce these agreements, which expand the leading PDT platform," said Corey McCann, M.D., Ph.D., President and CEO of Pear.

Training Optimus Prime, M.D.: Generating Medical Certification Items by Fine-Tuning OpenAI's gpt2 Transformer Model Matthias von Davier August 21st, 2019 Abstract Objective: Showcasing Artificial Intelligence, in particular deep neural networks, for language modeling aimed at automated generation of medical education test items. Materials and Methods: OpenAI's gpt2 transformer language model was retrained using PubMed's open access text mining database. The retraining was done using toolkits based on tensorflow-gpu available on GitHub, using a workstation equipped with two GPUs. Results: In comparison to a study that used character based recurrent neural networks trained on open access items, the retrained transformer architecture allows generating higher quality text that can be used as draft input for medical education assessment material. In addition, prompted text generation can be used for production of distractors suitable for multiple choice items used in certification exams. Discussion: The current state of neural network based language models can be used to develop tools in supprt of authoring medical education exams using retrained models on the basis of corpora consisting of general medical text collections. Conclusion: Future experiments with more recent transformer models (such as Grover, TransformerXL) using existing medical certification exam item pools is expected to further improve results and facilitate the development of assessment materials. Objective The aim of this article is to provide evidence on the current state of automated item generation (AIG) using deep neural networks (DNNs). Based on earlier work, a first paper that tackled this issue used character-based Address for correspondence: mvondavier@nbme.org: Time flies in the domain of DNNs used for language modeling, indeed: The day this paper was submitted, on August 13th, 2019, to internal review, NVIDIA published yet another, larger language model of the transformer used in this paper. The MegratronLM (apart from taking a bite out of the pun in this article's title) is currently the largest language model based on the transformer architecture [3]. This latest neural network language model has 8 billions of parameters, which is incomprehensible compared to the type of neural networks we used only two decades ago. At that time, in winter semester 1999-2000, I taught classes about artificial Neural Networks (NNs, e.g. Back then, Artificial Intelligence (AI) already entered what was referred to as AI winter, as most network sizes were limited to rather small architectures unless supercomputers were employed.

Purpose - Functional bowel diseases, including irritable bowel syndrome, chronic constipation, and chronic diarrhea, are some of the most common diseases seen in clinical practice. Many patients describe a range of triggers for altered bowel consistency and symptoms. However, characterization of the relationship between symptom triggers using bowel diaries is hampered by poor compliance and lack of objective stool consistency measurements. We sought to develop a stool detection and tracking system using computer vision and deep convolutional neural networks (CNN) that could be used by patients, providers, and researchers in the assessment of chronic gastrointestinal (GI) disease.

Modern medicine is based on the concept of disease. Each disease has its own unique and specific pathophysiology – meaning that each disease has a biological fault that defines that disease and only that disease. Functional disorders (e.g., irritable bowel syndrome, chronic fatigue syndrome, fibromyalgia syndrome) are problematic in that no specific pathophysiology has been discovered, though the search goes on. There are a number of biological abnormalities associated with functional disorders, but they are often shared between the different functional disorders, they are not always found, and they do not uniquely define any particular functional disorder. Additionally, patients with functional disorders are polysymptomatic and the symptoms of one disorder tend to overlap to some degree with those of another disorder, leading the description of spectrum disorders.