Automated Analyzers Market, By Product Type (Biochemistry Analyzers, Immuno-based Analyzers, Hematology Analyzers, Others), By Sample Type (Blood, Urine, Serum, Plasma, Others), By End User (Hospitals & Clinics, Diagnostic Laboratories, Pharmaceutical and Biotechnology Companies, Others), By Geography (North America, Latin America, Europe, Asia Pacific, Middle East & Africa)

Published In : May 2024

Code : CMI6982

Pages :142

Formats :

Excel and PDF

Industry : Medical Devices

Market Challenge – Stringent Regulatory Framework

Stringent regulatory norms across various countries poses significant challenges for the growth of global automated analyzers market. Governments are increasingly tightening regulations to ensure patient safety and high accuracy of diagnostic test results. This leads to longer approval timelines for new products and technologies in this market. For instance, the U.S. Food and Drug Administration (FDA) subjects devices used for in-vitro diagnostic purposes to premarket approval to establish safety and effectiveness. Getting this approval involves clinical trials and extensive documentation, which can take 2-3 years. European In Vitro Diagnostic Medical Device Regulation (IVDR) 2017/746 mandates stricter regulatory measures concerning the design, production, and post-market monitoring of IVD devices, surpassing the requirements of previous directives. Compliance with these updated regulations has increased the cost and time invested by manufacturers.

Market Opportunity – Integration of Advanced Technologies Like AI and IoT

The integration of advanced technologies such as artificial intelligence and internet of things provides opportunities for the global automated analyzers market growth. These emerging technologies can drive automated operations and real-time monitoring capabilities within biochemistry analyzers. Artificial Intelligence (AI) powered algorithms and machine learning models can be leveraged to analyze complex biochemical test results more accurately. This can help clinicians arrive at diagnoses faster. The AI models can also be trained on vast amounts of historical test data to identify subtle patterns and correlations that are not apparent to humans. The incorporation of Internet of Things (IoT) sensors into biochemistry analyzers and test kits allows for round-the-clock remote monitoring of critical parameters.