Where intelligence meets experience – HaiLTH – is a Software-as-a-Service company that leverages data to advance medical diagnostics. By bringing together world-class radiologists, data scientists, and researchers, we collect and analyze clinical data, pioneering medical products and service that empowers healthcare professionals to diagnose anomalies sooner and with unparalleled accuracy.
Unmatched capabilities of expert radiologists alongside the use of advanced technology, including machine learning and deep learning algorithms to improve workflow, efficiency, and quality at scale.
With our global reach, leading innovations, and deep insights, we aim to achieve the following:
Establish a parallel eco-system for teleradiology processes in reading nodal point
Add value to both productivity and output in terms of accuracy and reading speed
Ensure access to a global live database about related case studies for enhanced reporting
Provide instantaneous support during an emergency in reading any image of a critical patient
HaiLTH is a team of highly experienced multidisciplinary experts in artificial intelligence, medicine, marketing, and research.
HaiLTH provides high-quality software-as-a-service to radiologists with a commitment to meet and, when possible, exceed applicable regulatory, statutory, and quality requirements to improve patient care.
We aim to continuously improve products and services as more advanced technologies become available through our Quality Management System framework. Our mission is to develop products to help healthcare professionals measure, detect, track, and triage anomalies in radiological images to diagnose anomalies faster, more accurately, and more safely.
HaiLTH strives to maintain the effectiveness of products through regular reviews of in-house models, using benchmarking datasets and radiology services for continued suitability, improvement, and to implement Quality Policy throughout the organization. This helps ensure that all HaiLTH Team Member understand and fulfill the commitments made in support of sustaining aspired standards of care.
High efficiency is among the most important arguments in favor of automation. With automated processes implemented, analyses can be easily carried out in significantly less time and with a high volume of specimens. Resources can be used more efficiently and error-free test results are likely to be available faster.
Using traditional machine deep learning algorithms, we are in the process of automating diagnosis systems for some specific use cases. This technology is not a replacement for radiologists but it enables radiologists to be more efficient in ensuring that errors don’t occur while reading the images.
HaiLTH will benefit from AI, ML models and content-based image retrieval (CBIR). This will help identify labeled images that appear visually similar to a patient’s case, reducing the time between a patient visiting a clinic and receiving feedback to get the condition treated.
HaiLTH aims to leverage machine learning to offer consumers customized treatment plans. The algorithm uses information from a huge database which reportedly includes case studies and customer reviews.
The massive growth of computational power has led to a significant increase in the amount and granularity of stored digital medical data. HaiLTH seeks to use AI to analyze the large volume of this data to deliver more meaningful and actionable insights to transform how healthcare is delivered.
By applying to this problem ML-based image analysis, HaiLTH’s solution strive to cut down both the time needed for MRI examinations but also ensures the quality and quality of data provided.
Besides diagnosis, medical imaging techniques like PET scanning are gradually becoming imperative to evaluate patients’ response to treatments like cancer. Early response and evaluation are essential, for treatments like chemo or radiation therapies.