CLN Daily
Clinical mass spectrometry has become indispensable in laboratory medicine, yet many labs continue to face the same challenges, including labor-intensive workflows, complex data interpretation, and limited automation. During Wednesday’s session at ADLM 2026, “Off the beaten ion path: New directions for clinical mass spectrometry,” moderator Daniel Holmes, MD, FRCPC, will bring together speakers to showcase emerging technologies that address these barriers and expand the clinical impact of mass spectrometry.
The presenters will examine how artificial intelligence (AI), innovative sample preparation, workflow automation, and large language models (LLMs) could fundamentally change how laboratories perform and interpret mass spectrometry testing.
Brody Foy, DPhil, will begin the session by exploring the growing role of AI in pain- management testing. Toxicology reports often contain complex information that clinicians must interpret alongside medication histories and other clinical findings. AI-driven decision support could help bridge that gap by integrating laboratory results with prescription records and patient information to generate clinically meaningful interpretations.
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Rather than replacing laboratory professionals, AI can support their expertise by identifying clinically relevant patterns and promoting greater consistency in reporting, according to Foy. As these computational tools continue to mature, they may help laboratorians translate increasingly complex mass spectrometry data into clearer, more actionable clinical insights.
The second presentation will shift from data interpretation to specimen processing. Michael Chen, MD, MSc, FRCPC, will describe an innovative sample-preparation approach that uses MALDI-TOF (matrix-assisted laser desorption/ionization time-of-flight) to help reduce unnecessary urine cultures. By detecting bacterial signatures directly from clinical specimens following simplified sample preparation, MALDI-TOF may allow labs to rapidly identify negative specimens and avoid additional culture work for selected patients
In the final presentation, Holmes will focus on automation across the mass spectrometry workflow. He will highlight practical strategies laboratories can use to improve throughput while maintaining analytical quality — from automated sample preparation using high-density well plates to open-source software for instrument control and data processing.
Holmes will demonstrate how conversational AI and LLMs can assist with writing data analysis scripts, automating validation workflows, and generating code for laboratory applications. Without requiring extensive programming expertise, these tools allow laboratorians to use natural language prompts to streamline repetitive tasks and develop customized workflows.
Although AI and automation have generated considerable enthusiasm, the speakers will stress that human expertise remains essential. Laboratory professionals must continue to oversee method validation, verify analytical performance, and critically evaluate AI-generated outputs before incorporating them into clinical practice.
As the speakers will emphasize, the future of clinical mass spectrometry will be shaped not only by advances in instrumentation, but also by smarter, more integrated workflows that improve efficiency while maintaining the highest standards of analytical quality and patient care.