08/19/2025
Final Call for Presentation Proposals – DCI Network Conference “Signal Through The Noise: What Works, What Lasts, and What Matters in Healthcare AI”
September 25–27, 2025 | Harvard Medical School Joseph B. Martin Conference Center, Boston, MA, USA
We invite researchers, clinicians, technologists, policymakers, patient advocates, and industry leaders to submit papers and presentations for Signal Through The Noise: What Works, What Lasts, and What Matters in Healthcare AI, a three-day hybrid conference hosted by the Division of Clinical Informatics at Beth Israel Deaconess Medical Center.
Submission for presentation proposals deadline is August 22, 2025 https://www.dcinetwork.org/aiconf25
Conference Goals
The conference will critically examine what delivers real value in healthcare AI—clinically, operationally, ethically, and societally. Participants will define frameworks for measuring return on investment, explore scalable models for validation, and co-create roadmaps for ethical and impactful deployment.
Keynote Speakers
• Raghav Mani, Director of Digital Health, NVIDIA – Agentic AI Architectures for Healthcare
• Dr. Leo Anthony Celi, MIT, Beth Israel Deaconess, Harvard Medical School – AI and Open Science: Alea Iacta
• Dr. John Brownstein, Harvard Medical School & Boston Children’s Hospital – Deploying AI at Scale in Clinical Care
Core Themes
• Defining Value & ROI in AI
• Ethical & Responsible AI
• Validation & Scalability
• Real-World Applications
• Regulation & Policy
• Collaborative Road Mapping
Conference Format
• Day 1 (Sept 25): Foundational methods, technical deep dives, real-world case studies
• Day 2 (Sept 26): Strategic panels on evaluation, regulation, and future directions
• Day 3 (Sept 27): Hands-on workshops co-creating roadmaps for AI deployment
The program includes contributed sessions, invited interactive panels, networking opportunities, and workshops that generate white papers, policy recommendations, and scientific articles.
Who Will Be There
Confirmed participants represent:
• Healthcare institutions (BIDMC, Boston Children’s, DFCI, Mount Sinai, American Hospital Dubai)
• Life sciences (Bristol Myers Squibb, Johnson & Johnson, Susan G. Komen Foundation)
• Tech industry (NVIDIA, Google Health)
• Academia & Non-profits (Harvard, MIT, Columbia, Yale, National Academy of Medicine, Coalition for Health AI)
• Patients & Advocates (OpenNotes, Massachusetts Rare Disease Council, Insight Panel Members)
Venue & Travel
• Harvard Medical School Joseph B. Martin Conference Center – 77 Avenue Louis Pasteur, Boston, MA 02115 https://www.dcinetwork.org/aiconf25
• Hotel discounts available https://www.dcinetwork.org/aiconf25
Call for Contributions
We welcome submissions describing:
• Innovative AI methods and applications in healthcare
• Frameworks for ethical evaluation, patient engagement, and bias mitigation
• Evidence of clinical or operational impact, including early signals of ROI
• Open-source tools, reproducibility, and regulatory alignment
• Case studies co-developed with patients, providers, or advocacy groups
Please include a clear problem statement, methods, outcomes, and patient involvement where applicable.
Sections and Word Limits
• Learning Objectives → max 300 characters each (measurable verbs).
• Abstract Summary → 200 words.
• Objectives, Methods, Results, Discussion → 500 words each.
• Conflicts of Interest → 200 words.
Conference Tracks
The conference is organized into five major session tracks, each reflecting a critical dimension of responsible, impactful healthcare AI.
1. Defining Value and ROI in Healthcare AI
• Exploring how “value” is defined across clinical, operational, societal, and patient perspectives
• Economic models and health outcomes frameworks for measuring AI’s return on investment (ROI)
• Case studies from hospitals, life sciences, and consumer health demonstrating tangible impact
2. Ethical and Responsible AI
• Ensuring fairness, transparency, and equity in AI deployment
• Mitigating bias in datasets and algorithms
• Patient and advocate voices in the design, evaluation, and governance of AI systems
• Building trust through open science and reproducible methods
3. Validation and Scalability
• From pilot projects to large-scale adoption: challenges and lessons learned
• Validation strategies, post-deployment monitoring, and model drift management
• Interoperability standards for scalable AI deployment
• Real-world evidence (RWE) frameworks and proxy signals for early evaluation
4. Real-World Applications Across Domains
• Clinical decision support (CDS): AI in diagnostics, treatment planning, and patient safety
• Healthcare operations: Workflow optimization, triage, and remote monitoring
• Life sciences and research: Digital twins, multi-omics integration, and accelerating clinical trials
• Consumer health: Patient-facing apps, wearables, chatbots, and literacy-adaptive tools
5. Regulatory and Policy Frameworks
• FDA, EMA, and SaMD pathways for AI regulation
• Emerging global governance frameworks and algorithm change protocols
• Coalition- and society-led approaches to AI codes of conduct
• Strategies for harmonizing policies across jurisdictions
6. Collaborative Innovation and Road Mapping
• Workshop-based co-design sessions across six focus areas:
1. AI Clinical Decision Support (CDS)
2. AI for Real-World Evidence & Life Sciences
3. AI for Consumers & Patient Engagement
4. AI Policy & Governance
5. AI for Clinical Trial Matching
6. AI Chatbots for Patients
Submit your paper by August 22, 2025
https://www.dcinetwork.org/aiconf25
