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With a focus on automated diagnosis and connected health, RAD365 Artificial Intelligence is our effort to leverage tools in this burgeoning field to make the processes more efficient and results 100 per cent accurate eliminating any human misses/errors.

Our Team

Our core team has an average experience of more than 20 years working in the US and India. We work in close collaboration with our senior radiologists, doctors and technologists, who have decades of combined experience in their respective areas.

Core Strengths

Our team members across domains have Ph.D., Master’s and Bachelor’s degrees in technology and medicine. Our core Healthcare AI focused team has extensive experience in areas of traditional Machine Learning as well as Deep Learning technologies and different frameworks for re-using publicly available models (Transfer Learning). With deep experience in their respective domain and underlying technology stacks, the team can deliver best-in-class products and services that meet clients’ business objectives.

Our Expertise

  • Domain expertise in Radiology and Healthcare as a whole
  • Artificial Intelligence and Machine Learning
  • Internet of Things
  • Block chain Technology
  • Big Data Hadoop – Spark eco-system

Our Methodology

We use deep learning for radiology image analytics in addition to traditional machine learning for different feature engineering methodologies. Research experience of our radiologists enables us to incorporate important features in the workflow to create advanced models with great quality.

Quality Assurance

Availability of in-house data help us validate our model extensively resulting in the high-quality health care model. We validate our model using available benchmarking datasets and also use our in-house tele-radiology services to routinely validate our products.

WE LEVERAGE

AI Tools for Health Solutions

  • Innovative
  • Precious
  • Efficient

Our Initiatives
Using traditional Machine Learning and Deep Learning technologies, we are in the process of arriving at automated diagnosis systems for some specific use cases. This technology is not a replacement for radiologists. Instead it allows radiologists to be more efficient in ensuring that they do not miss any kind of pathology in the images.
Currently we are working on the following solutions:

Automation in Diagnosis
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.

Our Solutions

Development of Deep Learning algorithms for detection and segmentation of radiology images of various modalities.
  • Identification of niche opportunities
  • Research (secondary - transfer learning) and algorithm development using Deep Learning and Feature Engineering.
  • Trial runs

Benefits

  • Parallel eco-system for tele-radiology process in the Reading nodal point
  • Productivity & output in terms of accuracy, reading speed, value based reporting will be the key beneficial aspects
  • Access to global live database about related possible case studies for comparison purpose for better reporting
  • Highly effective support during emergency situations in reading any image of a critical patient
  • Enhances the efficiency in terms of spotting pathology in the images

Internet of Health Things (IoHT)

One by one, medical systems are becoming more interconnected specifically, increasing efforts are put into enhancing examination and diagnosis processes to enable precise and well-timed decisions. This is key to ensuring appropriate delivery of care and predictable patient outcome.
Among others, the Internet of Health Things (IoHT) comes into play in the form of smart medical devices and wearables, with the ability to collect varied patient-generated health data, also offering preliminary diagnosis options.
In radiology, the goal of the IoHT is to support health specialists by bringing in comfortable and safe work conditions and help them manage patient flow.
Our solutions in this domain include the following:
  • Developing deep learning algorithms for detection and segmentation of radiology images
  • Managing devices and implementing machine learning and AI algorithms to create alerts for emergencies
  • Leveraging connected health to integrate the following nodes:
    • Service layer
    • Device Management
    • Last Mile Care

Benefits

  • Monitoring the 10 Vital signs of a human 24x7 at a minimal reduced cost
  • Effective pro-active monitoring of health indicators
  • Leveraging preventive analytics, e.g. Preventing an impending heart-attack
  • Instantaneous updating of health condition 24x7 to a patient’s care giver & family

Centre of Excellence (CoE)

With 12+ years of experience in healthcare and with a strong team of healthcare professionals, RAD365 – a pioneer in research, resource alignment, and radiology best practices – has ventured into developing LMS-oriented towards MES/CME coverage and Healthcare Artificial Intelligence.
We leverage the experience to put forward digital training asset service-solution for managing complex curricula and developing training materials and evaluation tools for stakeholders. Our main goal is to extend a holistic e-learning experience while imparting & sharing a deep technology expertise and functional knowledge in Healthcare domain.
CME programs are also developed by practicing clinical and healthcare experts/academicians and are designed to address the pressing needs of the medical fraternity that helps enhance the communication with one’s consumers. Our professionals have an exemplary reputation for implementing successful CMEs, conferences, and consensus meets with leading enterprises.
We are committed to delivering educational resources that are independent, credible, and scientifically correct. As a part of this initiative, we are also looking forward to collaborating with a few leading academic institutions, medical centers, and universities to introduce the latest medical education along with healthcare AI to healthcare professionals.
Other solutions include but are not limited to the following:
  • Developing POCs OCs on new and emerging technologies and applications for Pharmaceuticals, Consumer Health & Biotech Industries
  • Re-skilling of IT professionals in the area of AI and ML in Healthcare
  • Developing LMS oriented towards Medical Education Services – CME clubbed with AI
  • Providing Training and Internship opportunities for students
  • Content creation – blogs / tutorials / kernels / notebooks etc.