AI, Robotics & Precision Diagnostics in SLAS Tech 32

SLAS (Society for Laboratory Automation and Screening)

Oak Brook, IL – Volume 32 of SLAS Technology, includes one review, one tech brief, six original research articles, one protocol, one literature highlight and several Special Issue (SI) features.

Review

Tech Brief

Original Research

This study presents the µ-Split, a high-precision microfluidic flow splitter that outperforms commercial alternatives by enabling even flow division via a high-resistance inlet design, simplifying multi-inlet perfusion while reducing cost and system complexity.

Protocol

  • Automation of protein crystallization scaleup via Opentrons-2 liquid handling

    This protocol demonstrates an automated, cost-effective protein crystallization workflow using the Opentrons-2 liquid handler, showing improved reliability and reproducibility for both hen egg white lysozyme and Campylobacter jejuni periplasmic protein crystallization compared to manual methods while providing open-access protocols for broader adoption.

Lit Highlights

Special Issues

  • AI-Driven Predictive Modeling for Disease Prevention and Early Detection

    This SI highlights how AI and machine learning revolutionize disease prevention and early detection by analyzing multimodal data (genetic, lifestyle, and environmental) to uncover hidden patterns, enabling proactive and personalized medicine. While promising, challenges such as data quality, privacy and standardization must be addressed to fully realize AI's potential in transforming healthcare from a reactive to a preventive approach.

  • High-throughput mass spectrometry in drug discovery

    This SI features innovative research on high-throughput mass spectrometry technologies that overcome traditional LC-MS bottlenecks, enabling ultrafast, label-free screening for hit identification, covalent drug discovery and compound library validation.

  • Robotics in Laboratory Automation

    This SI contains research on innovative robotics solutions for laboratory automation, including novel applications and automation frameworks.

  • Bio-inspired computing and Machine learning analytics for a future-oriented mental well-being

    The SI proposes bio-inspired computing and machine learning analytics for mental well-being in the field of life sciences innovation. Featured research reinforces the goal of revolutionizing the delivery of biological services through a medical assistive environment and facilitating the independent living of patients.

  • Innovative Applications of NLP and LLMs in Life Sciences

    Presented in this SI is research that marks a pivotal transformation in the life sciences: the transition of natural language processing and large language models from theoretical promise to real-world application.

This issue of SLAS Technology is available at https://www.slas-technology.org/issue/S2472-6303(25)X0003-0

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