Volume 26 Number 3

That’s an excellent and timely question — the field of human wound healing research is evolving rapidly, with new priorities shaped by advances in regenerative medicine, bioengineering, and systems biology

Here’s a concise summary of current and future priorities that researchers and funding agencies (like NIH, Horizon Europe, and Wellcome Trust) are emphasising for the next decade:

1. Regenerative and stem cell-based therapies

Goal: Move beyond simple wound closure toward true tissue regeneration (restoring skin appendages, pigmentation, and function).

Priorities:

• Optimising stem cell delivery (mesenchymal, epidermal, or induced pluripotent stem cells).
• Understanding stem cell–immune system interactions in wound beds.
• Developing off-the-shelf allogeneic cell therapies with predictable safety profiles.

2. Bioengineered skin substitutes and smart biomaterials

Goal: Create skin replacements that mimic the structure and biology of native skin.

Priorities:

• Next-generation hydrogels, nanofiber scaffolds, and bioprinted constructs.
• 3D bioprinting and organoid-based skin equivalents with vascular and neural integration.
• Smart dressings with sensing, drug release, and self-healing capabilities.

3. Neuroimmune and inflammatory regulation

Goal: Control the chronic inflammation that impairs healing, especially in diabetic and aged patients.

Priorities:

• Characterising immune cell phenotypes (macrophage polarisation, T-cell responses).
• Targeting neuropeptides and cytokines to modulate inflammation without delaying regeneration.
• Mapping nerve–immune–fibroblast signaling in the wound niche.

4. Chronic and diabetic wound management

Goal: Prevent non-healing wounds and limb amputations.

Priorities:

• Biomarkers for predicting healing trajectory.
• Metabolic and microbiome modulation (gut–skin axis, local probiotics).
• Precision medicine approaches using AI-based wound assessment and digital twins.

5. Genetic and epigenetic regulation of healing

Goal: Understand why some individuals heal scarlessly (e.g., fetal wounds) while others scar excessively.

Priorities:

• Identifying epigenetic switches that control fibroblast activation and scarring.
• Single-cell omics to profile cell states during healing.
• Gene editing (e.g., CRISPR-based modulation) to restore regenerative pathways.

6. Personalised and systems-level approaches

Goal: Integrate biology, data, and environment for individualised treatment.

Priorities:

• AI-driven prediction models for wound progression and treatment response.
• Incorporating social determinants (nutrition, stress, comorbidities).
• Using systems biology to map the entire wound healing network.

7. Translational and clinical implementation

Goal: Bridge the gap between discovery and therapy.

Priorities:

• Standardised clinical trial endpoints for wound healing efficacy.
• Real-world evidence platforms for device and biomaterial testing.
• Cost-effective therapies for low-resource settings.

Asked to focus on AI in wound healing, as ChatGPT suggested,

ChatGPT said:

Excellent — focusing on AI in wound healing opens one of the most transformative frontiers in the field. Artificial intelligence is shifting wound care from reactive treatment toward predictive, personalised, and precision-guided healing.
Here’s a structured overview of future priorities for AI-driven wound healing research in humans:

1. AI-powered wound assessment and monitoring

Current status: AI is already being used to analyse wound images (via smartphone apps or clinic-based imaging) to estimate size, depth, tissue type, and healing rate.

Future priorities:

• Multimodal image analysis: Combine RGB, hyperspectral, thermal, and 3D imaging for more accurate tissue characterisation.
• Standardised datasets: Build large, labeled, and diverse wound image databases across skin tones, wound types, and lighting conditions.
• Edge AI integration: Deploy real-time image analysis on portable or smartphone-based systems, especially for telemedicine or low-resource settings.
• Regulatory-grade validation: Establish frameworks (FDA/EMA) for validating AI wound classifiers as medical devices.

2. Predictive analytics for healing trajectories

Goal: Predict who will heal, when, and how.

Priorities:

• Machine learning models that integrate:
  •   Clinical data (age, diabetes status, comorbidities)
  •   Biochemical markers (cytokines, proteomics)
  •   Lifestyle and environmental factors
• Digital twin models: Virtual replicas of individual patients’ wounds that simulate healing under different treatments.
• Adaptive learning: Systems that continuously learn from new patient data to improve prediction accuracy.

3. AI in personalised treatment optimisation

Goal: Use AI to choose the right intervention for the right wound at the right time.

Priorities:

• Reinforcement learning systems that optimise wound care pathways dynamically (e.g., debridement timing, dressing choice, antibiotic use).
• Decision-support tools for clinicians that integrate AI outputs with clinical guidelines.
• Drug/device personalisation: Predict patient-specific responses to stem cell, growth factor, or biomaterial therapies.

4. Integrative “omics” and systems biology

Goal: Discover new healing biomarkers and therapeutic targets.

Priorities:

• AI-driven analysis of single-cell and spatial transcriptomics to map wound microenvironments.
• Multi-omics integration (genomics, proteomics, metabolomics) to define healing signatures.
• Causal AI models to distinguish correlation from mechanism — identifying what drives healing vs. what just correlates with it.

5. Smart wound dressings and Internet of Things (IoT)

Goal: Real-time wound monitoring with AI interpretation.

Priorities:

• Sensor-embedded dressings measuring pH, oxygen, moisture, and temperature.
• AI algorithms that interpret sensor data to alert for infection, ischemia, or delayed healing.
• Closed-loop systems that automatically release drugs or adjust therapy based on AI feedback.

6. Ethical, bias, and data governance frameworks

Goal: Ensure trustworthy and equitable AI in wound care.

Priorities:

• Bias mitigation in datasets — particularly across skin tones and global populations.
• Transparent algorithms (explainable AI) that clinicians can interpret and trust.
• Privacy-preserving learning (federated AI) to share data securely across hospitals and regions.

7. Clinical translation and regulatory pathways

Goal: Move AI from research prototypes to bedside use.

Priorities:

• Prospective clinical trials validating AI performance in real-world settings.
• Interoperability standards so AI tools integrate with electronic health records (EHRs).
• Implementation science to study how AI adoption impacts workflow, cost, and outcomes.

Vision for 2030+

“Wound healing ecosystems” — where AI continuously integrates patient data, imaging, and sensor feedback to create adaptive, individualised healing plans that evolve in real time.