DeepLabel — Clinical Data Labelling

Description

DeepLabel is a specialized platform providing clinical data annotation, reinforcement learning from human feedback (RLHF), and synthetic training data tailored for healthcare AI applications. The service is distinguished by its use of clinicians from the UK and US, who are meticulously trained by BiteLabs to ensure that the data labelling process meets the highest standards of clinical relevance and accuracy.

The platform addresses common issues in clinical AI, such as the failure of AI models when trained on generic data. By employing a large pool of over 30,000 clinicians trained in annotation and RLHF, DeepLabel ensures that the data used in AI models is both accurate and clinically relevant. This approach is particularly beneficial for clients in the healthcare sector, where precision and reliability are paramount.

DeepLabel's process is streamlined into four key steps: scoping the project with an initial call, conducting a pilot review, calibrating with only qualified clinicians, and delivering the final product according to client timelines. This methodical approach ensures that each project is tailored to the specific needs of the client, providing a customized and efficient solution.

The platform has been proven in the field, with over 219,618 clinical encounters analyzed. It has been deployed within NHS 111 and has provided valuable insights for organizations like Red Robin and Rad AI. DeepLabel offers fixed-price projects across three main services: clinical data annotation, RLHF and preference evaluation, and clinical training data generation. These services cover a wide range of data types, including EHR, clinical NLP, and imaging data.

DeepLabel is ideal for healthcare organizations looking to enhance their AI capabilities with high-quality, clinically validated data. Its focus on clinician-led data labelling and its proven track record make it a reliable partner for any healthcare AI project.

DeepLabel's Core Features

• Clinical data annotation

• RLHF and preference evaluation

• Synthetic training data generation

• Clinician-led data labelling

• Fixed-price projects

• Custom label sets

• Pilot review process

• Clinician calibration

How to use DeepLabel?

1. 1

   Scope: Initial 30-min call

2. 2

   Pilot: Review before scale

3. 3

   Calibrate: Qualified clinicians only

4. 4

   Deliver: According to timelines

DeepLabel's Use Cases

• Clinical Data Annotation
• RLHF Evaluation
• Synthetic Data Generation
• Healthcare AI Enhancement
• Custom AI Projects

FAQ from DeepLabel

• What is DeepLabel's primary service?

  DeepLabel specializes in clinical data annotation, RLHF, and synthetic training data for healthcare AI.

• Who performs the data labelling at DeepLabel?

  Data labelling is performed by UK and US clinicians trained by BiteLabs.

• How does DeepLabel ensure data quality?

  DeepLabel uses a pilot review and clinician calibration process to ensure data quality.

• What types of data does DeepLabel handle?

  DeepLabel handles EHR, clinical NLP, imaging data, and more.

• Is DeepLabel's service proven in the field?

  Yes, DeepLabel has analyzed over 219,618 clinical encounters and is deployed within NHS 111.

• What is the pricing model for DeepLabel?

  DeepLabel offers fixed-price projects.

• What is RLHF in the context of DeepLabel?

  RLHF stands for reinforcement learning from human feedback, used to enhance AI models.

• Can DeepLabel handle custom label sets?

  Yes, DeepLabel can label data to your custom label set.