How to read this roadmap
The phases are not sharp boundaries. Work in later phases often begins in earlier ones; some Phase I work continues through Phase III. The phasing reflects when capabilities reach maturity sufficient to enable the next layer of integration, not when work begins.
A funding agency reading this should treat phase milestones as gates for decision review, not as fixed delivery commitments. Each phase end is a natural point to assess what has worked, what has not, and how the next phase's priorities should shift.
The four phases at a glance
Each phase has its own detail page covering strategic objective, per-bucket priorities, integration milestones, funding allocation profile, dominant risks, and end-of-phase decision gate. The summaries below are the at-a-glance view; click through for the full treatment.
Demonstrate individual capabilities. Establish integration patterns. Build the infrastructure — datasets, standards, regulatory pathways — without which later phases cannot start.
Scale individual capabilities. Begin cross-bucket integration in earnest. Test whether the central scientific challenge (B3 causal disease modelling) will yield to current approaches.
Full AIHS prototypes for select indications. The bucket-integration question stops being theoretical. Regulatory and equity infrastructure mature alongside capability.
Either universal AIHS capability or sophisticated partial-system maturity. The branch depends on whether the named bottleneck advances yielded in earlier phases.
For the three explicit review moments where strategic direction gets reassessed, see the decision gates page.
Phase I — Foundation (Years 1–5)
The first phase focuses on demonstrating individual capabilities within each bucket and establishing the integration patterns that will matter later.
Bucket A priorities: Continuous multi-parameter biosensors with improved biocompatibility and bandwidth (A3). Foundation models for medical multimodal data integration (A4). In vivo single-cell imaging proofs of concept in animal models (A1).
Bucket B priorities: Scaled functional genomics for variant effect characterisation (B1). Federated learning infrastructure for medical AI (B5). Mechanistic modelling efforts on tractable organ systems — heart, liver (B4 preparation).
Bucket C priorities: Programmable cell therapies with logic-gate behaviour (C1 preparation). Improved delivery vehicles for currently inaccessible tissues (C2 preparation). Senolytic and rejuvenation therapy clinical maturation.
Integration priorities: Limited closed-loop systems for specific indications (continuous glucose with insulin delivery is the existing template). Safety architecture standards for autonomous medical decision systems (C6).
→ Full Phase I detail with funding allocation, risks, and decision gate
Phase II — Capability building (Years 5–15)
The second phase scales the individual capabilities and begins serious cross-bucket integration. Partial AIHS demonstrations for narrow indications become feasible by end of phase.
Bucket A: In vivo molecular mapping in non-human primates. Whole-body sensor networks with edge computing. Comprehensive multi-omic profiling at clinical timescales.
Bucket B: Patient-specific digital twins for common chronic diseases. Validated causal models for major disease classes. Continuous-learning clinical AI in deployed use. This is when the central scientific challenge (B3 — causal disease modelling) either yields meaningful progress or becomes the gating constraint on full AIHS.
Bucket C: Multi-target coordinated therapies for specific complex diseases. In vivo organ regeneration in animal models. Bounded reversible agent platforms in clinical trial.
Integration: Partial AIHS demonstrations for narrow indications (specific cancers, traumatic injury, regenerative medicine). Regulatory pathway development for closed-loop systems.
→ Full Phase II detail with funding allocation, risks, and decision gate
Phase III — Integration and scale (Years 15–30)
The third phase brings full AIHS prototypes for select clinical applications. By end of phase, AIHS systems are providing meaningful coverage for trauma, sepsis, select cancers, and regenerative medicine indications.
Bucket A: Whole-body real-time multi-omic monitoring in humans. Non-invasive single-cell readout at clinical scale.
Bucket B: Whole-patient digital twins for major disease categories. Comprehensive variant interpretation including combinatorial effects (conditional on B3 progress).
Bucket C: Coordinated multi-system therapy delivery. Architectural tissue reconstruction in clinical use for select tissues. Early neural reconnection capabilities for peripheral nerve injury.
Integration: Full AIHS prototypes for trauma, sepsis, and select cancer indications. Established regulatory frameworks for autonomous closed-loop care.
→ Full Phase III detail with funding allocation, risks, and decision gate
Phase IV — Maturity (Years 30+)
The fourth phase is conditional. Whether it arrives at the AIHS vision or stops short depends on whether breakthroughs occur in the named bottleneck problems.
If breakthroughs occur in causal disease modelling (B3), neural reconnection (C4), whole-body molecular mapping (A2), and architectural reconstruction (C3): general-purpose AIHS platforms become feasible. Neural reconnection including central nervous system applications. Accelerated healing capabilities for trauma and acute injury (conditional on C7).
If breakthroughs do not occur: mature systems are highly capable but limited to addressing conditions for which causal mechanisms are well-understood and intervention pathways are available. This is still a transformative clinical capability, just not the full AIHS vision. The fictional protomolecule-style omnicompetent substance remains fiction.
→ Full Phase IV detail with both scenarios and final gate
Decision gates
Three specific moments are natural for funding-agency review:
End of Phase II (year ~15): Has causal disease modelling reached the level needed for meaningful counterfactual reasoning in patient care? If no, AIHS capability will be permanently limited to mechanistically understood domains. Funding direction should pivot to maximising value within that scope rather than continuing to chase universal capability.
End of Phase III (year ~25): Has integration of the three buckets produced clinical value beyond what specialised systems alone would produce? If no, the future is specialised systems, not integrated platforms. Investment in integration infrastructure should be re-evaluated.
Mid-Phase IV (year ~40): Are the named bottleneck problems (B3, C4, A2, C3) showing breakthrough progress? If no, accept that AIHS will be a mature partial system rather than a complete one. This is still a successful outcome — most of the clinical value lives in the partial system. Recalibrate ambition.
→ Full decision gates page: evidence, decision-makers, what each gate is and isn't