Across many laboratories speed, accuracy, and data integrity, advanced technologies are transforming workflows and supporting critical medical decisions.
Laboratories face mounting pressure to manage growing data volumes, optimise workflow efficiency, and uphold stringent standards of integrity and accuracy. This is where artificial intelligence (AI) can assist, helping research professionals reshape healthcare and science for the better.
This is the view of Dr. Dror Kolodkin-Gal, founder of image integrity software Proofig AI. Kolodkin-Gal tells Digital Journal how AI is transforming laboratory operations across Europe.
Healthcare remains a top priority in Europe, as highlighted by the EU4Health Programme, which aims to bolster health systems and enhance crisis preparedness across the continent. According to the European Federation of Clinical Chemistry and Laboratory Medicine, nearly three quarters of current global medical decisions are dependent on laboratory test results.
Automating routine laboratory tasks
According to Kolodkin-Gal: “Traditional laboratory workflows often rely on a trial-and-error approach, where researchers repeat processes and experiments multiple times to achieve consistent results. This method, while effective, is highly time-consuming and labour-intensive, limiting the pace of scientific discovery. AI-driven automation offers a powerful solution, allowing laboratories to perform intricate tasks continuously and precisely, unhindered by human fatigue.”
Examples of how this can be achieved, include: “Robotic systems enhanced with AI execute experiments with a level of consistency that manual methods simply cannot match, minimising human error and variability. Working around the clock, these systems accelerate research timelines and facilitate safer handling of hazardous materials, effectively reducing safety risks. Moreover, AI algorithms optimise workflows by identifying bottlenecks and suggesting adjustments, resulting in faster and more efficient experimentation processes.”
Revolutionising data analysis in research
When considering research, Kolodkin-Gal observes: “The power of AI extends beyond automation – it is pivotal in managing and analysing the vast datasets generated by modern experiments. AI algorithms can detect patterns, revealing correlations or anomalies that may otherwise go unnoticed, and can suggest promising research directions. In diagnosis studies, AI can analyse medical images and patient data to improve the identification of diseases, significantly reducing the time required to reach accurate findings.”
Drawing on an example, Kolodkin-Gal cites: “A recent study in the UK found that AI systems used for breast cancer diagnosis led to a reduction in false positives and false negatives by 5.7 per cent and 9.4 per cent, respectively . In South Korea, researchers discovered that AI demonstrated a greater sensitivity in detecting breast cancer masses, achieving a rate of 90 per cent compared to 78 per cent for radiologists . Furthermore, AI outperformed radiologists in identifying early-stage breast cancer, with a detection rate of 91 per cent versus 74 per cent for traditional methods.”
Advanced image analysis
Image analysis has made considerable advances in helping with patient diagnosis. Kolodkin-Gal identifies: “This technology allows for the identification of subtle patterns and anomalies in images that may be imperceptible to the human eye, significantly enhancing research outcomes in fields such as biomedical sciences. Errors or misrepresentations in image investigations can compromise entire studies, leading to manuscripts retractions and undermining public trust in science.”
As an example, Kolodkin-Gal states: “To tackle these challenges, AI-driven image proofing tools are being integrated into research and publication workflows. These tools automatically scan images to detect duplications, manipulations, and alterations.”
Advantages
Summing up the contribution of AI, Kolodkin-Gal finds: “ By automating routine tasks, enhancing data analysis and safeguarding image integrity, AI supports the overarching goal of strengthening Europe’s healthcare systems. Embracing AI innovations helps laboratories across Europe position themselves at the forefront of healthcare and research, driving advancements that will support better patient outcomes and safeguard public health for years to come.”
