Best AI tools for< Clinical Research Associate >

AiCure provides a patient-centric eClinical trial management platform that enhances drug development through improved medication adherence rates, more powerful analysis and prediction of treatment response using digital biomarkers, and reduced clinical tech burden. AiCure's solutions support traditional, decentralized, or hybrid trials and offer flexibility to meet the needs of various research designs.

Intelligencia AI is a leading provider of AI-powered solutions for the pharmaceutical industry. Our suite of solutions helps de-risk and enhance clinical development and decision-making. We use a combination of data, AI, and machine learning to provide insights into the probability of success for drugs across multiple therapeutic areas. Our solutions are used by many of the top global pharmaceutical companies to improve their R&D productivity and make more informed decisions.

Beacon Biosignals provides an EEG neurobiomarker platform that is designed to accelerate clinical trials and enable new treatments for patients with neurological and psychiatric diseases. Their platform is powered by machine learning and a world-class clinico-EEG database, which allows them to analyze existing EEG data for insights into mechanisms, PK/PD, and patient stratification. This information can be used to guide further development efforts, optimize clinical trials, and enhance understanding of treatment efficacy.

BioXcel Therapeutics, Inc. is a clinical-stage biopharmaceutical company developing transformative medicines in neuroscience and immuno-oncology utilizing artificial intelligence, or AI, techniques. The company's proprietary AI platform is used to identify, re-innovate, and develop potential new therapies. BioXcel Therapeutics has a pipeline of product candidates in various stages of development, including BXCL501 for agitation in dementia, BXCL701 for cocaine use disorder, and BXCL801 for acute suicidal ideation and behavior in patients with major depressive disorder.

DrugCard is an AI-enabled Data Intelligence platform designed to streamline drug safety routines for pharmacovigilance processes. It offers solutions for local literature screening, catering to CROs, MAHs, and freelancers in the pharmaceutical industry. With support for multiple languages and regions, DrugCard ensures continuous, transparent, and scalable drug safety processes, saving time and improving efficiency. The platform leverages AI technology to enhance pharmacovigilance practices, providing accurate and holistic screening of medical journals to meet regulatory requirements.

Insitro is a drug discovery and development company that uses machine learning and data to identify and develop new medicines. The company's platform integrates in vitro cellular data produced in its labs with human clinical data to help redefine disease. Insitro's pipeline includes wholly-owned and partnered therapeutic programs in metabolism, oncology, and neuroscience.

SOMA is a Research Automation Platform that accelerates medical innovation by providing up to 100x speedup through process automation. The platform analyzes medical research articles, extracts important concepts, and identifies causal and associative relationships between them. It organizes this information into a specialized database forming a knowledge graph. Researchers can retrieve causal chains, access specific research articles, and perform tasks like concept analysis, drug repurposing, and target discovery. SOMA enhances literature review efficiency by finding relevant articles based on causal chains and keywords specified by the user. It empowers researchers to focus on their research by saving up to 95% of the time spent on pre-processing documents. The platform offers freemium access with extended functionality for 14 days and advanced features available through subscription.

Protocol Pal is a website designed to help users with building protocols. It is a user-friendly platform created by Basam Alasaly, Oceanexplains, and Tkruer. The website aims to simplify the process of creating protocols for various purposes. Users can easily navigate through the platform to generate structured protocols efficiently. Protocol Pal is a valuable tool for researchers, scientists, and professionals who require well-defined protocols for their work.

IXICO is a precision analytics company specializing in intelligent insights in neuroscience. They offer a range of services for drug development analytics, imaging operations, and post-marketing consultancy. With a focus on technology and innovation, IXICO provides expertise in imaging biomarkers, radiological reads, volumetric MRI, PET & SPECT, and advanced MRI. Their TrialTracker platform and Assessa tool utilize innovation and AI for disease modeling and analysis. IXICO supports biopharmaceutical companies in CNS clinical research with cutting-edge neuroimaging techniques and AI technology.

Oatmeal Health is an AI-enabled cancer screening application that aims to empower health equality by providing early detection services to underserved Medicare/Medicaid FQHC populations. The application combines primary care support, radiology risk assessments, and advanced therapeutic/clinical trial access for early diagnosis. Oatmeal Health partners with local providers to eliminate cancer care disparities and enhance patient outcomes through innovative technology and clinical solutions.

NovaResp cMAP™ is an AI-powered platform software designed to improve adherence to Continuous Positive Airway Pressure (CPAP) therapy for sleep apnea patients. It utilizes artificial intelligence and machine learning to predict and prevent apnea episodes during therapy, delivering personalized treatment at more comfortable air pressure levels. The application aims to enhance patient quality of life, increase sales for device manufacturers, and reduce labor costs for DMEs. NovaResp cMAP™ is compatible with all major PAP machines and is a revolutionary solution in the treatment of Obstructive Sleep Apnea (OSA).

neurons.bio is an AI application that offers a unique collection of over 100 AI agents designed for drug development, medicine, and life science research. These agents perform specific tasks efficiently, retrieve data from various sources, and provide insights to accelerate research processes. The platform aims to revolutionize drug discovery and development by integrating cutting-edge LLM technology with domain-specific agents, reducing research costs and time to clinic.

Insight is an AI-powered medical research tool that serves as a research assistant for generating scientific summaries, hypotheses, experimental designs, and target identification. It empowers scientists to navigate literature, formulate hypotheses, and design experiments by utilizing peer-reviewed databases to provide reliable outputs. With integrated features like NIH PubMed access, NIH Reporter insights, and MYGENE & MYVARIANT deep dives, Insight streamlines the research process and accelerates discoveries in the medical field.

The National Cancer Institute's Center for Biomedical Informatics and Information Technology (CBIIT) provides a comprehensive suite of tools, resources, and training to support cancer data science research. These resources include data repositories, analytical tools, data standards, and training materials. CBIIT also develops and maintains the NCI Thesaurus, a comprehensive vocabulary of cancer-related terms, and the Cancer Data Standards Registry and Repository (caDSR), a repository of cancer data standards. CBIIT's mission is to accelerate the pace of cancer research by providing researchers with the tools and resources they need to access, analyze, and share cancer data.

JMIR AI is a new peer-reviewed journal focused on research and applications for the health artificial intelligence (AI) community. It includes contemporary developments as well as historical examples, with an emphasis on sound methodological evaluations of AI techniques and authoritative analyses. It is intended to be the main source of reliable information for health informatics professionals to learn about how AI techniques can be applied and evaluated.

JADBio is an automated machine learning (AutoML) platform designed to accelerate biomarker discovery and drug development processes. It offers a no-code solution that automates the discovery of biomarkers and interprets their role based on research needs. JADBio can parse multi-omics data, including genomics, transcriptome, metagenome, proteome, metabolome, phenotype/clinical data, and images, enabling users to efficiently discover valuable insights. The platform is purpose-built for various conditions such as cancer, immune, endocrine, metabolic system, chronic diseases, aging, infectious diseases, and mental health, offering solutions for early biomarker discovery, drug repurposing, lead identification, compound optimization, trial monitoring, and response to treatment. JADBio is trusted by partners in precision health & medicine and is continuously evolving to disrupt drug discovery times and costs at all stages.

Health Imaging is an AI-powered platform that focuses on providing cutting-edge solutions in medical imaging and healthcare management. The platform offers a wide range of features and tools that leverage artificial intelligence to enhance diagnostic accuracy, streamline workflows, and improve patient care. From advanced imaging technologies to AI-based training solutions, Health Imaging is at the forefront of innovation in the healthcare industry.

Freenome is a healthcare company that uses artificial intelligence and multiomics technology to detect cancer in its earliest stages through a simple blood draw. The company's mission is to make early cancer detection more accessible and affordable, and to improve the chances of successful treatment.

The website is a comprehensive platform providing medical news, articles, and resources covering a wide range of health topics such as COVID-19, artificial intelligence in healthcare, diseases, treatments, and medical advancements. It offers insights from experts, interviews, white papers, and newsletters in the fields of medicine and life sciences. Users can access information on various health categories, research findings, safety summaries, and trending stories in the medical and life sciences domains.

Cercle is an AI platform that advances healthcare for women by transforming healthcare data into real-time, high-quality insights. The platform caters to women's healthcare providers, payors, and pharma companies, helping them run more efficient businesses and provide personalized care. Cercle's Biomedical Graph unlocks insights at unprecedented speed and accuracy, optimizing patient care and improving outcomes in areas such as assisted reproduction and fertility processes.

This repository contains a collection of papers and resources related to Large Language Models (LLMs), including their applications in various domains such as text generation, translation, question answering, and dialogue systems. The repository also includes discussions on the ethical and societal implications of LLMs. **Description** This repository is a collection of papers and resources related to Large Language Models (LLMs). LLMs are a type of artificial intelligence (AI) that can understand and generate human-like text. They have a wide range of applications, including text generation, translation, question answering, and dialogue systems. **For Jobs** - **Content Writer** - **Copywriter** - **Editor** - **Journalist** - **Marketer** **AI Keywords** - **Large Language Models** - **Natural Language Processing** - **Machine Learning** - **Artificial Intelligence** - **Deep Learning** **For Tasks** - **Generate text** - **Translate text** - **Answer questions** - **Engage in dialogue** - **Summarize text**

2025-AI-College-Jobs is a repository containing a comprehensive list of AI/ML & Data Science jobs suitable for college students seeking internships or new graduate positions. The repository is regularly updated with positions posted within the last 120 days, featuring opportunities from various companies in the USA and internationally. The list includes positions in areas such as research scientist internships, quantitative research analyst roles, and other data science-related positions. The repository aims to provide a valuable resource for students looking to kickstart their careers in the field of artificial intelligence and machine learning.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Taiyi (太一) is a bilingual large language model fine-tuned for diverse biomedical tasks. It aims to facilitate communication between healthcare professionals and patients, provide medical information, and assist in diagnosis, biomedical knowledge discovery, drug development, and personalized healthcare solutions. The model is based on the Qwen-7B-base model and has been fine-tuned using rich bilingual instruction data. It covers tasks such as question answering, biomedical dialogue, medical report generation, biomedical information extraction, machine translation, title generation, text classification, and text semantic similarity. The project also provides standardized data formats, model training details, model inference guidelines, and overall performance metrics across various BioNLP tasks.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

The repository 'LLM-for-Healthcare' provides a comprehensive survey of large language models (LLMs) for healthcare, covering data, technology, applications, and accountability and ethics. It includes information on various LLM models, training data, evaluation methods, and computation costs. The repository also discusses tasks such as NER, text classification, question answering, dialogue systems, and generation of medical reports from images in the healthcare domain.

llm_benchmarks is a collection of benchmarks and datasets for evaluating Large Language Models (LLMs). It includes various tasks and datasets to assess LLMs' knowledge, reasoning, language understanding, and conversational abilities. The repository aims to provide comprehensive evaluation resources for LLMs across different domains and applications, such as education, healthcare, content moderation, coding, and conversational AI. Researchers and developers can leverage these benchmarks to test and improve the performance of LLMs in various real-world scenarios.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

BasicLingua is a Python library that provides functionalities for linguistic tasks such as tokenization, stemming, lemmatization, and many others. It is based on the Gemini Language Model, which has demonstrated promising results in dealing with text data. BasicLingua can be used as an API or through a web demo. It is available under the MIT license and can be used in various projects.

Awesome Mamba is a curated collection of groundbreaking research papers and articles on Mamba Architecture, a pioneering framework in deep learning known for its selective state spaces and efficiency in processing complex data structures. The repository offers a comprehensive exploration of Mamba architecture through categorized research papers covering various domains like visual recognition, speech processing, remote sensing, video processing, activity recognition, image enhancement, medical imaging, reinforcement learning, natural language processing, 3D recognition, multi-modal understanding, time series analysis, graph neural networks, point cloud analysis, and tabular data handling.

This repository serves as a practical guide for Medical Large Language Models (Medical LLMs) and provides resources, surveys, and tools for building, fine-tuning, and utilizing LLMs in the medical domain. It covers a wide range of topics including pre-training, fine-tuning, downstream biomedical tasks, clinical applications, challenges, future directions, and more. The repository aims to provide insights into the opportunities and challenges of LLMs in medicine and serve as a practical resource for constructing effective medical LLMs.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

This repository includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

The LLMEvaluation repository is a comprehensive compendium of evaluation methods for Large Language Models (LLMs) and LLM-based systems. It aims to assist academics and industry professionals in creating effective evaluation suites tailored to their specific needs by reviewing industry practices for assessing LLMs and their applications. The repository covers a wide range of evaluation techniques, benchmarks, and studies related to LLMs, including areas such as embeddings, question answering, multi-turn dialogues, reasoning, multi-lingual tasks, ethical AI, biases, safe AI, code generation, summarization, software performance, agent LLM architectures, long text generation, graph understanding, and various unclassified tasks. It also includes evaluations for LLM systems in conversational systems, copilots, search and recommendation engines, task utility, and verticals like healthcare, law, science, financial, and others. The repository provides a wealth of resources for evaluating and understanding the capabilities of LLMs in different domains.

This repository does not provide a tool, but rather a collection of resources and strategies for academics in the field of artificial intelligence who are feeling depressed or overwhelmed by the rapid advancements in the field. The resources include articles, blog posts, and other materials that offer advice on how to cope with the challenges of working in a fast-paced and competitive environment.

KG-RAG (Knowledge Graph-based Retrieval Augmented Generation) is a task agnostic framework that combines the explicit knowledge of a Knowledge Graph (KG) with the implicit knowledge of a Large Language Model (LLM). KG-RAG extracts "prompt-aware context" from a KG, which is defined as the minimal context sufficient enough to respond to the user prompt. This framework empowers a general-purpose LLM by incorporating an optimized domain-specific 'prompt-aware context' from a biomedical KG. KG-RAG is specifically designed for running prompts related to Diseases.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

Comprehensive Guide in Clinical Trials & Regulations

Expert in healthcare data analysis, coding, and clinical trials.

私と製薬ネタで遊びましょう【注意】私のアウトプットはフィクションです。実在の人物や団体などとは関係ありません。

Guide to healthcare services in India.

Medical engineer specializing in HL7/FHIR data conversion.

Scientific expert in evaluating articles using ROBINS-I and Cochrane tools

A Medical Research Assistant Chatbot

Medical Device Expert on MDR 2017/745, IVDR 2017/746 and related MDCG guidance

Approachable expert on FDA medical device regulation. Offering direct download links for related regulation and guidance documents from FDA sites.

Clinical trial assistant answering questions about clinical trials.

Expert in Medical Communications

Expert in disease cures, medical research, and data analysis.

An expert in Drug Delivery Systems Industry, providing in-depth, accurate insights.

As the Pharma Talent Coach, I specialize in career coaching for the pharmaceutical and biotechnology industries. I offer resume analysis and interview coaching. By Glen Osiemo

Informative guide on medical devices and healthcare technology.

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