ESCMID 2025 marked a shift from technology-led innovation to outcome-led development. The key question is no longer “Can it be done?” but “Does it solve the right problem, in the right way, at the right scale?”

Headline Trends

• Antimicrobial resistance (AMR) and antibiotics use – increasing access to and uptake of diagnostic testing is key for effective interventions
  AMR remains a major global health challenge. Antibiotic use continues to rise, especially in primary care, where most prescriptions are issued without diagnostic support. Addressing this requires a broader range of diagnostic systems. These must balance performance with affordability and usability, enabling appropriate treatment decisions in both hospital and community settings.
• The role of syndromic testing is still being debated
  While useful in hospital and acute care, syndromic panels have seen limited uptake in primary care due to cost and complexity. They support antimicrobial stewardship. But they must become more accessible and cost-effective to broaden adoption.
• Genetic sequencing may bring a step change in clinical utility in diagnostics
  Sequencing is moving closer to clinical use. It has the potential to reshape diagnostics. Developers must build workflows and systems that integrate sequencing seamlessly. They must interpret results clearly and deliver clinical value at scale.
• ML, AI, and automation are maturing
  AI tools are expanding from imaging to in-vitro diagnostics. They offer clinical augmentation value to tedious manual workflows, image interpretation, and data integration. The focus is shifting from innovation to implementation, embedding tools into workflows with trust, reproducibility, and regulatory alignment.

The annual congress of the European Society of Clinical Microbiology and Infectious Diseases (ESCMID) brings together clinicians, researchers, and industry leaders to explore the evolving landscape of diagnostics. This year’s event highlighted some key themes shaping the future of diagnostic impact and delivery.

AMR and Antibiotic Use: Access to Testing Is Critical

Antimicrobial resistance remains a central challenge. With an estimated 40–50 billion antibiotic doses taken daily and use projected to rise 50% by 2030, intervention is urgent. The vast majority of prescriptions occur in primary care. They are often without diagnostic support.

Improved access to diagnostics is key. Many current platforms focus on performance where demand already exists, but the biggest opportunity lies in reaching settings where no testing is currently available. Systems must support appropriate treatment decisions. They must balance speed, accuracy, and pathogen identification with usability, affordability, and integration into clinical workflows.

Syndromic Testing: Performance vs Cost

Multiplexed syndromic panels are established in hospitals and acute care, but uptake in primary care remains low. Their value in guiding antibiotic use is clear yet cost and complexity are barriers to broader use.
Developers must reduce system cost and complexity to reach more healthcare environments and fit into reimbursement frameworks. Technical capability alone is no longer enough. Integration, ease of use, and clinical decision support are now central to adoption.

Sequencing-based diagnostics are also beginning to compete with syndromic approaches, raising the bar for accessibility and performance in multiplexed testing.

Sequencing: Moving Toward Clinical Routine

Genetic sequencing is rapidly approaching routine use in clinical diagnostics. With falling costs and expanding platform availability, it holds the potential to reshape infectious disease diagnostics, particularly in syndromic or multiplexed contexts.
At ESCMID, multiple case studies demonstrated the use of same-day metagenomic sequencing for pathogen identification in respiratory and bloodstream infections. Amplicon-based approaches were also discussed, particularly for rapid variant detection and resistance gene profiling. Broader sequencing methods, including microbiome analysis, may also play a role in future clinical applications.

However, sequencing workflows are not yet plug-and-play. Upstream processes, such as sample collection, DNA extraction, host depletion, and library preparation, remain technically demanding. Downstream, interpretation, bioinformatics pipelines, and clinically actionable reporting are major hurdles. While sequencing speed and cost are improving, the challenge now lies in integrating these steps into streamlined, automated, and interpretable systems that deliver value at the point of care.

Developers must ask strategic questions. Should initial diagnostic applications focus on relatively simple, targeted sequencing? For example, amplicon sequencing for variant detection, or on broader, hypothesis-free metagenomic approaches? Will sequencing be decentralized or remain concentrated in specialized hubs? How can systems present complex data in ways that support decision-making by non-specialist clinicians? The opportunity is significant. But realizing it will require systems that balance performance, usability, and clinical relevance.

ML, AI, and Automation: From Hype to Implementation

Artificial intelligence has already made an impact in diagnostic imaging, including MRI, CT, and ultrasound. At ESCMID, attention turned to the broader use of AI and machine learning in in-vitro diagnostics, with exciting potential across three main areas:

• Image analysis: AI is enabling rapid interpretation of high-resolution optical data in fields like digital pathology, haematology, and spectral imaging. These tools can increase diagnostic accuracy while reducing the burden on human reviewers.
• Multi-marker data interpretation: AI models are increasingly used to integrate complex biomarker datasets, where the combined signal across markers yields diagnostic insight. Some platforms already use deterministic models; the shift toward machine learning promises greater flexibility and performance, particularly as training datasets grow.
• Workflow optimization and automation: AI is being applied to streamline laboratory operations, reducing hands-on time, standardizing results, and minimizing error rates. This is especially valuable in high-throughput or resource-constrained settings.

Several ESCMID sessions showed AI being used to support antimicrobial resistance prediction, improve taxonomic resolution, and aid diagnostic decision-making. Importantly, the field is moving from exploratory development to real-world deployment. The emphasis is now on clinical validation, regulatory clarity, reproducibility, and integration with existing systems.

For developers, success will depend on more than accuracy. Trust, interpretability, and usability are emerging as key differentiators. Tools that embed smoothly into clinical workflows, minimize training requirements, and deliver repeatable, high-confidence outputs will define the next wave of AI in diagnostics.

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It took 13 years and £2 billion to sequence the human genome back in 2003. Fast forward 15 years to today and next-generation sequencing (NGS) can do it for less than $1,000 in a matter of days. High-throughput sequencing technologies, computational power and data-mining techniques have opened up a whole new era in medical treatment – and our approach to product development.

Genetic differences in DNA allow scientists to determine how a patient will respond to certain drugs, enabling doctors to target their treatment. For example, the Sanger Institute recently discovered that the aggressive blood cancer acute myeloid leukaemia could be classified as 11 distinct disease groups, based on specific constellations of genetic mutations. This explains why some patients will be cured and others will not if they receive exactly the same treatment.

This ‘personalised’ approach promises to improve patient outcomes as the right treatment can be given from the start – time is not wasted by finding what works by trial and error – and unpleasant side effects can be minimised. Treatment is more efficient – and less money is wasted on ineffective drugs.

New targeted approaches based on genetic information are gaining particular attention from pharma companies, as they can dramatically reduce drug development costs and timescales. Whereas traditional drug discovery often leads to high failure rates in phase 2 or 3 trials, targeted treatment allows smaller trials and shorter regulatory review times because the drugs are safer and more effective.

Although the market size is smaller, lower side effects mean an increased price can be charged for the drug. An example of this is the Food and Drug Administration’s (FDA’s) approval in 2012 of a new cystic fibrosis (CF) therapy for patients with a rare genetic mutation (G551D mutation). This particular gene is responsible for only 4% of CF cases in the US – around 1,200 people.

The UK government has also recognised the value of the personalised treatment approach. The NHS is undertaking the ‘100,000 Genomes Project’ – 100,000 whole human genomes from 70,000 patients are being sequenced to identify potentially new diagnostics and drive the development of new drugs.

But the race is on. Genomics and biotechnology company 23andMe – which originally provided ancestry information from a saliva sample sent through the post (direct-to-consumer genetic testing) for £125 – has recently been approved by the FDA to provide risk information for 10 genetic diseases, such as Parkinson’s disease and late-onset Alzheimer’s disease. It is estimated that the company has accumulated valuable genetic information about more than two million people.

From this vast library of genomic information becoming available, new products will emerge that target the underlying cause specific to an individual patient. This will likely involve at least two medical products – a diagnostic test and the therapeutic product itself, working together as a so-called companion diagnostic (a diagnostic that is essential for the safe and effective use of a corresponding drug).

Pharma and device companies will need to collaborate more closely to ‘co-develop’ these products to ensure the drug is safe and effective, and the performance of the diagnostic is acceptable. And, since the barriers to drug development are significantly reduced with targeted treatment, smaller innovative companies will get involved. With its access to hugely valuable genomic data, 23andMe is one such company – which is presumably why it has just raised $250 million from private investors.

I predict that NGS and data analytics will be the powerful research tools that provide the understanding. But although NGS is starting to move out of the research lab and into the clinical environment, the data it provides is unnecessarily detailed for routine testing. It will be the lower-cost, more accessible diagnostic devices that will be used to test specific genetic sequences – leading to a proliferation of companion diagnostics.