Spotlight

Introducing Mendel's New Brand Focus: Supercharging Clinical Data Workflows in Healthcare

Introducing Mendel's New Brand Focus: Supercharging Clinical Data Workflows in Healthcare

Mendel has evolved its brand to “Supercharge Your Clinical Data Workflows,” a shift that reflects our commitment to delivering AI solutions that genuinely enhance clinical data management. In healthcare, where talent shortages demand efficient and reliable tech, our Hypercube solution and neuro-symbolic AI bring unmatched cost-efficiency, speed, and accuracy to workflows. This shift emphasizes our focus on alleviating healthcare’s talent strain with tech that builds trust—eliminating errors and reducing the risk of hallucinations. Discover how Mendel’s transformative approach can optimize your workflows with validated solutions trusted by leaders in the industry.

Research paper

Faithfulness Hallucination Detection in Healthcare AI: Ensuring Reliable Medical Summaries

Faithfulness Hallucination Detection in Healthcare AI: Ensuring Reliable Medical Summaries

Research Paper

Revolutionizing Patient Cohort Identification with AI – Insights from Mendel’s ACR Benchmark

Revolutionizing Patient Cohort Identification with AI – Insights from Mendel’s ACR Benchmark

Introducing ACR: A New Benchmark for Patient Cohort Retrieval This study introduces Automatic Cohort Retrieval (ACR), a novel task for efficiently identifying patient groups from large-scale medical data. Comparing AI-powered approaches, including large language models and neuro-symbolic systems, the research reveals promising advancements in automating cohort selection for clinical trials and studies. The findings highlight the potential of AI to revolutionize healthcare data analysis, while emphasizing the need for continued improvements in accuracy, efficiency, and reliability.

Whitepaper

Introduction to Hypercube’s Ontology and Reasoning Engine

Introduction to Hypercube’s Ontology and Reasoning Engine

Large Language Models (LLMs) hold the potential to transform healthcare by generating clinical insights and supporting decision-making. However, LLMs face challenges such as hallucinations, lack of explainability, and limited reasoning capabilities, which restrict their effectiveness in clinical settings. Mendel's Hypercube platform addresses these limitations by integrating LLMs with structured clinical ontologies, enhancing both inference and decision-making. Unlike standard ontologies focused mainly on documentation, Mendel’s generative ontology prioritizes scalable reasoning through reductionism and emergentism, enabling more accurate clinical reasoning and streamlined data integration.

Updates

Mendel Unveils Groundbreaking Neuro-Symbolic AI System Outperforming GPT-4 for Automatic Cohort Retreival in New Study

Mendel Unveils Groundbreaking Neuro-Symbolic AI System Outperforming GPT-4 for Automatic Cohort Retreival in New Study

“Our latest research at Mendel marks a significant milestone in the field of AI in general, and healthcare in particular,” said Wael Salloum, Cofounder and Chief Science Officer at Mendel. “We are the leader in clinical reasoning by coupling LLMs with our hypergraph reasoning, enhancing both the effectiveness and efficiency of patient cohort retrieval.

Updates

Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI Assisted vs Standard Methods in 3 Oncology Trials

Improving Clinical Trial Participant Prescreening With Artificial Intelligence (AI): A Comparison of the Results of AI Assisted vs Standard Methods in 3 Oncology Trials

Delays in clinical trial enrollment and difficulties enrolling representative samples continue to vex sponsors, sites, and patient populations. Here we investigated use of an artificial intelligence-powered technology, Mendel.ai, as a means of overcoming bottlenecks and potential biases associated with standard patient prescreening processes in an oncology setting.

Updates

Coupling Symbolic Reasoning with Language Modeling for Efficient Longitudinal Understanding of Unstructured Electronic Medical Records

Coupling Symbolic Reasoning with Language Modeling for Efficient Longitudinal Understanding of Unstructured Electronic Medical Records

The application of Artificial Intelligence (AI) in healthcare has been revolutionary, especially with the recent advancements in transformer-based Large Language Models (LLMs). However, the task of understanding unstructured electronic medical records remains a challenge given the nature of the records (e.g., disorganization, inconsistency, and redundancy) and the inability of LLMs to derive reasoning paradigms that allow for comprehensive understanding of medical variables. In this work, we examine the power of coupling symbolic reasoning with language modeling toward improved understanding of unstructured clinical texts. We show that such a combination improves the extraction of several medical variables from unstructured records. In addition, we show that the state-of-the-art commercially-free LLMs enjoy retrieval capabilities comparable to those provided by their commercial counterparts. Finally, we elaborate on the need for LLM steering through the application of symbolic reasoning as the exclusive use of LLMs results in the lowest performance.

Case study

How a diagnostic company was able to build a clinico-genomic database in a week

How a diagnostic company was able to build a clinico-genomic database in a week

The customer, a key player in the genomics space, had a strategic initiative to build a clinic genomic database to support their life sciences customers.

Case study

How One Organization Changed The Way Patients are Identified for Clinical Trials with AI

How One Organization Changed The Way Patients are Identified for Clinical Trials with AI

One clinical trial organization was using manual chart review and was looking to reduce the time it takes to find eligible patients.

From the AI Team

How to Approach De-Identification

How to Approach De-Identification

Organizations that use patient data for internal or external research need to take steps to prevent the exposure of PHI to those who are not authorized to view it. They do this by redacting specific categories of identifiers from every patient document. Once the identifiers are masked, the risk profile of these datasets is significantly reduced. But how do you ensure that redaction engines are working to the highest accuracy?

Team Retreat

Mendel Retreat: Adventures and Team Building in Cairo

Mendel Retreat: Adventures and Team Building in Cairo

The Mendel team is still buzzing from our week-long retreat in Cairo. The theme of the retreat was “coming together” and it was the first time the American and other remote employees were united with their Egyptian counterparts. Although there were many adventures–missing flights, seeing the pyramids, haggling at Khan el-Khalili–the highlight of the trip was collaborating together, as one global organization. 

Competence via comprehension

AI for healthcare needs clinical reasoning skills

AI for healthcare needs clinical reasoning skills

Artificial intelligence (AI) is playing an increasingly important role in the healthcare industry. But to fully leverage the potential of AI, it must be equipped with clinical reasoning skills - the ability to truly comprehend clinical data, or in other words, to read it as a doctor would. When it comes to data processing tools, only a tool capable of clinical reasoning can effectively process unstructured clinical data.