Breakout Session Goals: The goal of these breakouts are to discern and deliberate on how to move forward in each of these topical areas, in the near term and in the long-term.

Charge to Breakout Participants: See links to each breakout session below.  Please refer to the 2023 MSM Mission statement in forming your remarks

Instructions to Notetakers:  Slides for this session that include the 'flash talk' slide before breakouts, a notes template for Round 1 & 2, and a template for report back is: Click here for all Breakout Session Notes!

Breakout Sessions (notes in links below)

1. Digital Twins: whole person, mental health (Click on the link for pre-Breakout Background Info)

MSM lead: Gary An; IMAG lead: Liz Ginexi

• How would the MSM Consortium play a role?
• How to integrate mechanism-based multi-scale modeling with machine learning/artificial intelligence to develop medical digital twins?
• How to foster collaborations that integrate underlying digital twin model development with developers of sensor/assay technologies needed to provide the data feedback to refine the virtual twin?
• Propose strategies for being able to capture inter-individual heterogeneity in a way that allows personalization of digital twins.
• Propose strategies for dealing with uncertainty and parameterization when integrating models/modules that cross multiple scales.
• Discuss how the concept of Multi-scale Mechanism-based Digital Twins can find synergies with the topics in the other Breakouts (Melding Mechanistic-ML models, Sociabehavioral and Social Determinants of Health, Quantum Computing and Translations/Incentivization)

2. New Mechanistic-ML Methods: PINN, transformer, XAI, Large Language Models

MSM lead: George Karniadakis; IMAG lead: Mauricio Rangel-Gomez, Ilana Goldberg, Julia Berzhanskaya

• What are the similarities and differences between current methods; e.g. PINN, transformer, XAI, Large Language models?
• What are the opportunities to improve these methods?
• How can these methods be applied to blood diagnostics an diseases as it pertains to the IMAG interst group AI and Machine Learning for Blood Diagnostics and Diseases | Interagency Modeling and Analysis Group (nih.gov)? 
• How can these methods be applied to neuroscience?
• How can these methods be used in the IMAG initiatives discussed in Session 2.1
• How do we quantify uncertainty in neural networks and specifically in scientific machine learning?
• Graph neural networks and causal inference.

3. Sociobehavioral and Social Determinants of Health (SDoH) Models: network, probabilistic, stochastic models

MSM lead: Elsje Pienaar, Kyoko Yoshida, Bruce Y. Lee; IMAG lead: Julia Berzhanskaya, Asif Rizwan

• What is the state of current modeling efforts - post-pandemic modeling?
• How can SDoH be used to develop models to address health inequities?

4. Quantum Computing and Other Technologies for Modeling: quantum sensors, neuromorphic chips. emerging compute capabilities

MSM lead: Suvranu De; IMAG lead: Orlando Lopez, Raj Gupta

• How can these technologies be used in the IMAG initiatives discussed in Session 2.1?

5. Translation and Incentivization: state of practice and future outlook

MSM lead: Feilim Mac Gabhann; IMAG lead: Elena Sizikova

• What are new and emerging MSM tools in your field, who develops them and how are these tools translated from research prototypes into practice?
• What are the domain-specific opportunities and hurdles in translation of MSM tools in your research domain? 
• How do we make credible and trustworthy MSM tools? Do different organizations (e.g. academia/industry/regulatory) have different approaches to or considerations about credibility?
• What technologies exist and what technologies are missing/needed for timely and effective tool dissemination?