Log10 for Healthcare
Log10 for Healthcare
Transforming Healthcare with AI Precision
Transforming Healthcare with AI Precision
Define and attain crucial LLM accuracy targets in healthcare with AI-powered LLMOps & Observability.
Define and attain crucial LLM accuracy targets in healthcare with AI-powered LLMOps & Observability.
Define and attain crucial LLM accuracy targets in healthcare with AI-powered LLMOps & Observability.
Define and attain crucial LLM accuracy targets in healthcare with AI-powered LLMOps & Observability.
LLMs are revolutionizing healthcare
Healthcare organizations are leveraging Large Language Models (LLMs) in key areas to enhance efficiency, decision-making, and innovation across clinical, administrative, and research domains.
Healthcare organizations are leveraging Large Language Models (LLMs) in key areas to enhance efficiency, decision-making, and innovation across clinical, administrative, and research domains.
Healthcare organizations are leveraging Large Language Models (LLMs) in key areas to enhance efficiency, decision-making, and innovation across clinical, administrative, and research domains.
01
Clinical Decision Support
LLMs provide real-time diagnostic suggestions and treatment recommendations based on patient data and medical literature.
01
Clinical Decision Support
LLMs provide real-time diagnostic suggestions and treatment recommendations based on patient data and medical literature.
01
Clinical Decision Support
LLMs provide real-time diagnostic suggestions and treatment recommendations based on patient data and medical literature.
01
Clinical Decision Support
LLMs provide real-time diagnostic suggestions and treatment recommendations based on patient data and medical literature.
02
Personalized Patient Care
LLMs analyze patient data to create tailored treatment plans and offer personalized health advice through chatbots for areas like preventative health, maternal health, mental health, eldercare, and longevity.
02
Personalized Patient Care
LLMs analyze patient data to create tailored treatment plans and offer personalized health advice through chatbots for areas like preventative health, maternal health, mental health, eldercare, and longevity.
02
Personalized Patient Care
LLMs analyze patient data to create tailored treatment plans and offer personalized health advice through chatbots for areas like preventative health, maternal health, mental health, eldercare, and longevity.
02
Personalized Patient Care
LLMs analyze patient data to create tailored treatment plans and offer personalized health advice through chatbots for areas like preventative health, maternal health, mental health, eldercare, and longevity.
03
Medical Research and Drug Discovery
LLMs synthesize scientific literature, clinical trial data, and biochemical information to accelerate research insights, hypothesis generation, and identification of potential drug candidates.
03
Medical Research and Drug Discovery
LLMs synthesize scientific literature, clinical trial data, and biochemical information to accelerate research insights, hypothesis generation, and identification of potential drug candidates.
03
Medical Research and Drug Discovery
LLMs synthesize scientific literature, clinical trial data, and biochemical information to accelerate research insights, hypothesis generation, and identification of potential drug candidates.
03
Medical Research and Drug Discovery
LLMs synthesize scientific literature, clinical trial data, and biochemical information to accelerate research insights, hypothesis generation, and identification of potential drug candidates.
04
Insurance Billing Codes
LLMs automate the mapping of medical notes to appropriate billing codes, enhancing accuracy and efficiency in healthcare billing and pre-authorizations.
04
Insurance Billing Codes
LLMs automate the mapping of medical notes to appropriate billing codes, enhancing accuracy and efficiency in healthcare billing and pre-authorizations.
04
Insurance Billing Codes
LLMs automate the mapping of medical notes to appropriate billing codes, enhancing accuracy and efficiency in healthcare billing and pre-authorizations.
04
Insurance Billing Codes
LLMs automate the mapping of medical notes to appropriate billing codes, enhancing accuracy and efficiency in healthcare billing and pre-authorizations.
05
Streamlining Administrative Tasks
LLMs automate scheduling, insurance processing, and medical transcription to reduce errors and enhance efficiency.
05
Streamlining Administrative Tasks
LLMs automate scheduling, insurance processing, and medical transcription to reduce errors and enhance efficiency.
05
Streamlining Administrative Tasks
LLMs automate scheduling, insurance processing, and medical transcription to reduce errors and enhance efficiency.
05
Streamlining Administrative Tasks
LLMs automate scheduling, insurance processing, and medical transcription to reduce errors and enhance efficiency.
06
Clinical Trials
LLMs streamline patient matching, recruitment, and optimize trial designs to accelerate medical research.
06
Clinical Trials
LLMs streamline patient matching, recruitment, and optimize trial designs to accelerate medical research.
06
Clinical Trials
LLMs streamline patient matching, recruitment, and optimize trial designs to accelerate medical research.
06
Clinical Trials
LLMs streamline patient matching, recruitment, and optimize trial designs to accelerate medical research.
Consequence
A woman in Australia delayed seeking treatment for breast cancer after receiving this advice from an AI health app, leading to a more advanced stage diagnosis.
The problem
The problem
LLM errors and hallucinations can lead to significant risks
LLMs can produce unreliable results that potentially harm patients and research integrity or disclose sensitive information leading to privacy violations. Human oversight guarantees accuracy and safety, but it’s impossible to manually evaluate thousands or millions of LLM completions each day.
LLMs can produce unreliable results that potentially harm patients and research integrity or disclose sensitive information leading to privacy violations. Human oversight guarantees accuracy and safety, but it’s impossible to manually evaluate thousands or millions of LLM completions each day.
LLMs can produce unreliable results that potentially harm patients and research integrity or disclose sensitive information leading to privacy violations. Human oversight guarantees accuracy and safety, but it’s impossible to manually evaluate thousands or millions of LLM completions each day.
Our solution
Our solution
Get human-accurate LLM oversight with customized models
Log10 AutoFeedback evaluation models detect errors and reduce risk by instantly reviewing LLM completions with the accuracy of a human. Inspired by interpretability research and using latent space technology, Log10 AutoFeedback offers accuracy comparable to fine-tuned models and surpasses LLM-as-a-judge, all while needing far fewer samples for faster, more efficient deployment.
90% less data needed; start with just 20-30 human samples
90% less data needed; start with just 20-30 human samples
90% less data needed; start with just 20-30 human samples
90% less data needed; start with just 20-30 human samples
Highly customizable for specific tasks and domains
Highly customizable for specific tasks and domains
Highly customizable for specific tasks and domains
Highly customizable for specific tasks and domains
Lightning-fast: <100ms latency
Lightning-fast: <100ms latency
Lightning-fast: <100ms latency
Lightning-fast: <100ms latency
Cheaper: leverages compact models vs. GPT-scale ones
Cheaper: leverages compact models vs. GPT-scale ones
Cheaper: leverages compact models vs. GPT-scale ones
Cheaper: leverages compact models vs. GPT-scale ones
Versatile: supports agentic & multimodal evals
Versatile: supports agentic & multimodal evals
Versatile: supports agentic & multimodal evals
Versatile: supports agentic & multimodal evals
Ideal for on-prem deployment
Ideal for on-prem deployment
Ideal for on-prem deployment
Ideal for on-prem deployment
Log10 AutoFeedback models are faster, cheaper, and require minimal data. Zero-shot prompting is quick but often lacks accuracy, while fine-tuned models are precise but demand significant resources. Log10 AutoFeedback combines the best of both, offering accuracy comparable to fine-tuned models and surpassing LLM-as-a-judge, all while needing far fewer samples for faster, more efficient deployment.
By labeling each and every LLM completion with human-accurate feedback, AutoFeedback powers observability, workflow, and automation features that enable developers to set and hit critical reliability goals for healthcare AI applications.
By labeling each and every LLM completion with human-accurate feedback, AutoFeedback powers observability, workflow, and automation features that enable developers to set and hit critical reliability goals for healthcare AI applications.
By labeling each and every LLM completion with human-accurate feedback, AutoFeedback powers observability, workflow, and automation features that enable developers to set and hit critical reliability goals for healthcare AI applications.
Monitoring & Alerting
Set quality threshold targets and understand exactly how your application is performing. Get alerts when quality falls below critical thresholds.
Ranking & Triaging
Errors are automatically prioritized and queued for resolution. Engineers debug and resolve issues using the Log10 LLMOps Observability Stack.
Self-Improving Applications
Datasets curated with feedback automatically tune prompts and models, continuously improving accuracy while in production.
Want to know more? We’ve got you covered.
Want to know more? We’ve got you covered.
Want to know more? We’ve got you covered.
Want to know more? We’ve got you covered.
Product
Developers
© Copyright 2024 Log10, Inc. All rights reserved.
Product
Developers
© Copyright 2024 Log10, Inc. All rights reserved.
Product
Developers
© Copyright 2024 Log10, Inc. All rights reserved.
Product
Developers
© Copyright 2024 Log10, Inc. All rights reserved.