Cambridge Healthtech’s 26th Annual
Bio-IT World Conference & Expo
Bio-IT World: Connecting the Life Sciences Ecosystem to Advance Precision Medicine
May 18-20, 2027
SPEAKER PROPOSALS NOW BEING ACCEPTED
Are you ready to present at the 26th Annual Bio-IT World Conference & Expo, taking place May 18-20, 2027, in Boston, MA? We’re excited to receive your proposals!
For more than 25 years, Bio-IT World has brought together the global life sciences ecosystem at the intersection of science and technology. Researchers, scientists, informaticians, data and AI leaders, R&D executives, technology innovators, and industry partners come together to exchange ideas, share real-world implementations, and explore the technologies and strategies transforming biomedical research and pharmaceutical R&D.
The 2027 program will span 11 conference tracks, focused symposia, workshops, training seminars, and networking opportunities across the life sciences R&D ecosystem. Coverage will include digital and data infrastructure, informatics and bioinformatics, advanced analytics, generative and agentic AI, AI-driven discovery and precision medicine, intelligent laboratory systems and robotics, R&D digital transformation, and emerging technologies shaping the future of research.
We are seeking compelling scientific, technical, and strategic presentations that go beyond what is possible to demonstrate what is working and deliver measurable value. Priority will be given to innovative research, real-world implementations, lessons learned, and case studies that show how science and technology are solving complex R&D challenges, improving collaboration and workflows, accelerating discovery and development, increasing efficiency and productivity, strengthening decision-making, and delivering measurable scientific, business, or patient impact.
Join the Bio-IT World community in Boston to share your work, connect with peers from across the global life sciences ecosystem, and help shape the next generation of data-, technology-, and AI-enabled R&D.
Coverage will include, but is not limited to:
SYMPOSIA (taking place Tuesday, May 18)
S1: Generative AI Tools
Move from Proof-of-Concept to Production in Regulated Environments
The Generative AI Tools symposium explores how life sciences organizations are building and operationalizing GenAI applications, agents, platforms, and workflows in complex, regulated environments. Topics include system architecture, workflow and product design, agent orchestration, enterprise platforms, research infrastructure, integration, validation, security, compliance, and human oversight. Through production deployments and practical case studies, attendees will learn how to move beyond prototypes, integrate GenAI into existing systems and workflows, automate time-intensive processes, establish trust and auditability, and build scalable, production-ready AI solutions across R&D, clinical, regulatory, and operational environments.
S2: AI for Biologics
From Models to Discovery: Closing the Loop between AI, Biology, and Experimentation
AI is rapidly changing how biologics are discovered, moving beyond prediction toward the generation, optimization, and design of novel therapeutic molecules. The field is moving toward integrated AI-driven discovery systems that connect multimodal foundation models, generative protein and antibody design, and agentic workflows with experimental biology. The symposium will explore what this next generation of AI-enabled discovery will bring, from designing molecules across multiple therapeutic properties, to using active learning and experimental data to continuously guide the next design cycle. The program will showcase emerging approaches, with a focus on real-world progress, challenges, and opportunities. As AI and experimental science become increasingly connected through a continuous design-build-test-learn loop, the attendees will learn about how these advances can accelerate biologics discovery and bring greater precision to the design of next-generation therapeutics.
S3: Knowledge Graphs
Connect Fragmented Data for Unified Views and Powerful Discoveries
The Knowledge Graphs symposium explores how life sciences organizations are transforming fragmented data and evidence into connected, contextualized knowledge that supports scientific discovery and AI-driven reasoning. Topics include biomedical knowledge graph construction, semantic integration, ontologies, provenance, GraphRAG, graph machine learning, multimodal data, and scalable knowledge infrastructure. Through real-world implementations, attendees will learn how these approaches connect complex relationships across data and evidence, improve discoverability and traceability, enable more trustworthy AI, accelerate hypothesis generation and scientific decision-making, and translate connected knowledge into actionable insights across discovery, development, and precision medicine.
TRACKS (taking place Wednesday, May 19 – Thursday, May 20, 2027)
T1: Data Platforms & Storage Infrastructure
Optimize Data Platforms for Scale, Speed, Performance, and Cost Efficiency
The rapid growth of complex scientific data, coupled with AI, advanced analytics, imaging, and omics, is placing new demands on the infrastructure that stores, moves, and delivers it. Modern data platforms must provide fast, scalable access to distributed data while balancing performance, security, and cost efficiency across pharma and biotech R&D.
The Data Platforms & Storage Infrastructure track explores how organizations are modernizing the foundational data layer for R&D. Through case studies and best practices, the track examines scalable data platforms and architectures, high-performance and tiered storage, data orchestration and movement, interoperability, distributed and hybrid environments, data lifecycle and cost optimization strategies, and infrastructure designed to support increasingly data- and compute-intensive science.
T2: Data Management
Build Connected, Scalable Data Foundations for Discovery and AI
The Data Management track explores how life sciences organizations are transforming fragmented scientific and clinical data into connected, trusted, and reusable assets that scale across R&D. Topics include common data models, data products and data mesh, metadata and ontologies, governance and stewardship, integration, and AI-ready data strategies. Through real-world implementations, attendees will learn how modern data approaches improve collaboration and reuse, streamline integration and workflows, strengthen quality and traceability, accelerate analysis and decision-making, and create scalable foundations for scientific discovery and AI.
T3: Software Applications & Services
Powering AI-Driven Discovery through Next-Generation Software
The Software, Applications & Services track explores the technologies and platforms transforming how life sciences organizations conduct research, analyze data, and bring new discoveries to life. In 2027, the focus moves beyond digital transformation toward AI-native, connected, and increasingly autonomous R&D environments. Sessions will examine how agentic AI and AI-powered scientific software are moving from experimentation into real-world workflows—from intelligent research assistants and autonomous data analysis—to agents that orchestrate complex discovery workflows. We’ll also explore the latest advances in AI-powered scientific applications, self-driving labs, digital twins, FAIR and AI-ready data, knowledge graphs, and autonomous workflows. Designed for developers, software engineers, computational scientists, and IT leaders, this track showcases real-world implementations, emerging architectures, and practical strategies for building the software infrastructure required for AI-driven discovery and the next generation of life sciences R&D.
T4: Cloud for AI/ML & Modern Data Science
Cloud Infrastructure and Strategies for AI-Driven R&D
As AI/ML, advanced analytics, and data-intensive science place new demands on pharmaceutical and biotech R&D, cloud infrastructure must deliver the compute power, scalability, flexibility, and security these workloads require. Organizations must also determine how best to use public, private, hybrid, and multi-cloud environments while balancing cost, performance, security, and regulatory requirements. The Cloud for AI/ML & Modern Data Science track explores how life sciences organizations are designing and optimizing cloud environments for modern R&D. Through case studies and best practices, the track examines cloud strategies for AI/ML and scientific computing, GPU and HPC workloads, hybrid and multi-cloud architectures, secure and sovereign cloud, workload orchestration, cost optimization, and emerging cloud technologies.
T5: Generative & Agentic AI
Transform Scientific Workflows, Accelerate Decisions, and Scale AI across R&D
The Generative & Agentic AI track explores how life sciences organizations are applying GenAI and AI agents to transform scientific work across R&D. Topics include AI agents and copilots, foundation models, scientific reasoning, agent orchestration, workflow automation, validation, and human oversight. Through real-world implementations, attendees will learn how these approaches expand scientific capabilities, automate complex tasks, accelerate evidence generation and decision-making, increase researcher productivity, and transform how scientists interact with data, tools, and each other across discovery, development, and clinical research.
T6: AI for Drug Discovery & Development
Accelerate Therapeutic Discovery, Design, Validation, and Development with AI
The AI for Drug Discovery & Development track explores how AI is transforming the discovery and advancement of new therapeutics. Topics include target and biomarker discovery, foundation models, molecular and therapeutic design, predictive modeling, medicinal chemistry, agentic workflows, and AI-integrated experimentation. Through real-world applications, attendees will learn how these approaches expand biological and chemical exploration, prioritize promising targets and candidates, connect computational predictions with experimental validation, accelerate design-make-test-learn cycles, improve R&D decision-making, and advance promising therapies more efficiently from discovery toward development.
T7: AI for Oncology, Precision Medicine & Health
Transform Multimodal Data into Clinical Impact with Trusted AI
The AI for Oncology, Precision Medicine & Health track explores how AI is translating complex patient and biological data into more precise insights, decisions, and care. Topics include biomarker discovery, multimodal and foundation models, AI-enabled diagnostics, patient stratification, digital twins, clinical prediction, experimental validation, and trustworthy clinical AI. Through real-world applications, attendees will learn how these approaches improve disease understanding and early detection, identify clinically relevant biomarkers, predict patient risk and treatment response, personalize interventions, strengthen clinical decision-making, and move AI-driven insights toward measurable patient impact.
T8: Data Science & Analytics Technologies
Scale Data Science in Life Sciences from Algorithms to Outcomes
The Data Science & Analytics Technologies track explores how advanced computational, statistical, and analytical methods are turning complex biomedical data into predictive insight and scientific impact. Topics include predictive and mechanistic modeling, network and systems medicine, federated learning, reverse translation, uncertainty quantification, and scalable analytical workflows. Through real-world applications, attendees will learn how these approaches reveal complex biological relationships, strengthen predictions across populations and datasets, connect clinical evidence back to discovery, improve scientific decision-making, and translate rigorous data science into reproducible, actionable outcomes across life sciences R&D.
T9: Bioinformatics
Transforming Data into Biological Intelligence
While 2026 has focused on operationalizing bioinformatics and multimodal data, making complex workflows scalable, reproducible, and fit for real-world research and clinical environments, the field is now moving beyond processing and integrating biological data toward extracting deeper biological meaning from it. As genomics, single-cell, spatial, proteomics, imaging, and clinical datasets continue to converge, the challenge is becoming one of representation, context, and interpretation. How can we connect information across biological scales and modalities, distinguish meaningful signals from noise, and build computational systems that can reason across increasingly complex biological landscapes? Advances in foundation models, multimodal AI, pangenomes, knowledge representation, agentic workflows, and other emerging computational approaches are creating new possibilities, while also raising fundamental questions around benchmarking, generalizability, reproducibility, biological validation, and trust. The 2027 program will explore this transition from data and computational workflows toward biological intelligence, examining how emerging technologies can move beyond prediction and analysis to generate deeper biological insight and enable more impactful discovery and scientific decision-making.
T10: AI-Powered Robotics & Intelligent Lab Automation
Unite Physical and Digital to Build the Intelligent Laboratory of the Future
The AI-Powered Robotics & Intelligent Lab Automation track explores how life sciences organizations are connecting robotics, automation, data, and AI to create more intelligent and increasingly autonomous laboratories. Topics include connected and modular labs, robotic orchestration, automated data capture, AI-ready experimentation, integrated DMTA workflows, and closed-loop optimization. Through real-world implementations, attendees will learn how these approaches accelerate experimental cycles, increase throughput and instrument utilization, improve reproducibility and data quality, reduce manual effort, enable more flexible and scalable laboratory operations, and connect computational intelligence with physical experimentation to advance scientific discovery.
T11: Pharmaceutical R&D Informatics
Lead Digital Transformation and Build the Future of R&D
The Pharmaceutical R&D Informatics track brings together senior pharma and biotech leaders shaping the digital future of R&D. Through executive perspectives and enterprise case studies, sessions explore digital and AI strategy, informatics ecosystems, operating models, technology investment, organizational change, governance, and the modernization of scientific workflows. Attendees will gain insights into how organizations prioritize and scale innovation, simplify complex technology landscapes, balance speed with scientific and regulatory requirements, strengthen adoption, improve operational efficiency, and translate digital transformation into measurable scientific and business value.
INVESTOR CONFERENCE (taking place Tuesday, May 18, 2027)
Bio-IT World Venture, Innovation & Partnering
Connect Capital and Science to Shape the Future
As part of the 26th Annual Bio-IT World Conference & Expo, the 4th Annual Bio-IT World Venture, Innovation & Partnering Conference (May 18, 2027) delivers an executive-level platform for investors, corporate leaders, and entrepreneurs driving the next wave of biotech and precision medicine. This boutique event convenes C-suite leaders from venture capital, private equity, corporate venture arms, growth-stage companies, and pharma. The 2027 program zeroes in on AI-driven discovery, platform- vs. product-investment models, evolving M&A and partnership strategies, IPO and private liquidity pathways, and regulatory shifts shaping capital deployment. Sessions are designed to provide investors with clear insights into market dynamics, risk-adjusted returns, and scalable innovation strategies. Through candid discussions, fireside chats, and curated panels, participants will explore how capital is being allocated, where innovation pipelines are headed, and how to identify long-term winners. Alongside investor-focused dialogue, attendees gain access to 150+ exhibits, 11 scientific tracks, and global partners from 30+ countries to broaden their market perspective.
The deadline for priority consideration is October 13, 2026.
All proposals are subject to review by session chairpersons and/or the Scientific Advisory Committee to ensure the overall quality of the conference program. Additionally, as per Cambridge Healthtech’s policy, a select number of vendors and consultants who provide products and services will be offered opportunities for podium presentation slots based on a variety of Corporate Sponsorships.
Opportunities for Participation:
SUBMIT YOUR PROPOSAL