Role of Artificial Intelligence in Diagnostics
Medgate Today, July 2022: The diagnostic industry continues to stride ahead with AI technology
Diagnostics is the first step in the healthcare delivery chain, as 70% of the treatment decisions are based on lab results. The market for diagnostics devices is growing to meet the increasing demand from hospitals and laboratories. The onus is on the manufacturer to provide a holistic solution that integrates automation with analytics, training, updates, and troubleshooting. With a rise in the adoption of artificial intelligence to improve turnover and optimize efficiency, pathology laboratories are transforming into predictive rather than reactive environments.
The Role of AI
Many laboratory processes continue to be performed manually or are partially digitized. Efforts are being taken to improve the preanalytic, analytic, and post-analytic processes of the clinical laboratories with AI support.
According to a recent report, examples of applications of Artificial Intelligence (AI) to COVID-19 are already reported such as AI-enabled outbreak tracking apps, chatbots for diagnostics, AI-powered analysis of scientific publications and triage using natural language processing for screening potential patients and prognosis prediction tools, using radiology CT scans to manage system capacities, etc.
Also available are multiple examples of the role played by AI in the management of chronic diseases such as cancer and cardiovascular disorders. AI tools aid in augmenting the accuracy of clinical decisions and improving patient care.
Further, studies reveal an AI-supported system that predicts patient waiting time in the phlebotomy unit and organizes the entire blood collection process or the auto-verification of test results.
Revolutionizing the way in which pathology is viewed and defined
The integration of AI with the conventional modes of diagnosis opens up a plethora of opportunities:
Enhancing lab workflow efficiency: AI-based diagnostic devices offer a shorter TAT compared to the traditional microscopic testing methods, thereby allowing for more samples to be tested. Pathological investigations like microscopy for infectious diseases such as malaria, differential counts, etc. depend on image analysis. AI can aid in analyzing these images to help the pathologist give a faster and more accurate diagnosis.
Predictive maintenance: For laboratories, one of the biggest concerns is downtime. Planning for upgrading via cloud-based lab management systems and sophisticated AI software is a good way to future-proof products and reduce the cost of maintenance.
Inventory management: Remote monitoring is used to evaluate the usage and consumption of reagents for each test and their expiry to allow efficient management of lab inventory. Further, by analyzing actual usage data, a company can provide better customer service by pre-scheduling the delivery of consumables.
24x7 remote access: With IoT and AI software, the service team can receive real-time reports. Technicians can remotely access the instrument and get information on the performance and repair history which can help them save time.
Digital slides/remote view:
Digital pathology is one of the biggest advantages of AI as it helps alleviate the many barriers between patients and clinicians and clinicians and pathologists. The ability to share digital microscopic images enhances research and collaborative diagnosis.
Increases productivity of lab personnel: AI gives the lab experts space to focus on assessing rare and complex cases that require a high level of competency and skill.
Adoption of AI by manufacturers of medical devices
AI is being leveraged by the biggest manufacturers in the MedTech industry to provide better customer service to labs and in turn enhance clinical outcomes.
Transasia Bio-Medicals Ltd., India’s leading IVD Company, has already integrated its fully automated clinical chemistry line of analyzers with IoT sensors and remote diagnosis technology, to provide an altogether different level of service to its customers and partner with them in improving lab efficiencies and benefitting the patients at large.
Integration of AI is helping Transasia provide analyzers that are efficient, powerful, and user-friendly. As an example, the integration of AI with digital bright-field microscopy in Laura XL, a fully automated urine chemistry and sediment analyzer, provides clear, high-quality images mimicking visual microscopy. These systems are enabled to recognize thousands of known sediment elements. This reduces repetitions while improving the accuracy and reliability of sediment particle detection and differentiation. Additionally, when an atypical cell appears on the screen, the pathologist can check the accuracy of the automatic identification and further evaluate to confirm the sub-types.