Technological and scientific innovations are transforming healthcare delivery. The integration of quantum computing, AI, and diverse health data sources from MedTech devices, wearables, and genomics, enables precise diagnostics and the development of life-extending therapies. Real-time population health profiles, ethically constructed from this data, facilitate the identification of disease drivers and the creation of advanced, personalised treatments, building on earlier breakthroughs in gene therapies and immunotherapy. These advancements, along with innovations in pharmacogenomics, nanotechnology, and implantable devices, have significantly increased survival rates for some diseases.

The world in 2030 
 

• Genomic advancements: Rapid genomic data analysis enables accurate diagnoses, personalised treatment plans, and enhanced survival rates, contributing to improved population health outcomes.
• Multi-omics and microbiome therapies: Technologies like proteomics and metabolomics, alongside microbiome-based therapies, provide a deeper understanding of human biology and offer innovative treatment options for various diseases.
• Targeted therapies and diagnostics: Developments in vaccine technology, drug delivery systems, and liquid biopsy assays allow for more precise diagnoses and more targeted and cost-efficient healthcare interventions.
• Neurotechnology and personalised mental health: Quantum computing, brain-computer interfaces, and customised mental health treatment plans based on genetics and biomarkers are revolutionising neurological and psychological care.
  

Overcoming cross-cutting constraints
 

There are four cross-cutting constraints that could affect the prediction (not having the right skills and talent, funding models, approach to regulation, and data governance in place). The prediction can be realised by turning the constraints into enablers, for example by:

• attracting talent with expertise in clinical pharmacology, computational biology, AI, and regulatory compliance as well as clinicians who possess strong digital skills and a deep understanding of multi-omics and AI-driven treatments.
• adopting outcomes-based funding models that incentivise innovation and equitable access to healthcare; and establishing platform-based business models to streamline data sharing and collaboration within the healthcare ecosystem.
• enabling regulatory bodies to balance innovation with consumer protection, incorporating real-world evidence into decision-making; and embedding robust cybersecurity measures to ensure data integrity and patient privacy.

Evidence in 2024  
 

• Gene editing and cell and gene therapies (CGTs) are advancing rapidly: The UK's approval of Casgevy for sickle cell disease and β-thalassemia highlights the potential of CRISPR gene editing, while the growing market for CGTs from US$5.3bn in 2022 to $19.9bn in 2027 signals a shift towards personalised advanced medicine, despite high costs prompting innovative business models.
• Advances in neurological treatments: Driven by 23 novel therapies for the treatment of agitation and disease-modifying therapies, the global Alzheimer’s disease market is projected to grow by 20% annually to reach US$13.7bn by 2030. Additionally, Aarhus University have found that it is possible to predict the risk of developing psychiatric disorders using genetic analysis, paving the way for better prevention and treatment.
  

How AI/GenAI might impact treatment and diagnostic paradigms  
 

• GenAI can analyse diverse datasets, including genomics, clinical history, and social determinants of health, to provide deeper insights that can revolutionise healthcare delivery.
• GenAI enables personalised treatment plans by integrating polygenic risk scores with behavioural insights, extending care beyond traditional settings through virtual coaching and remote monitoring. 
• GenAI can accelerate the discovery of new treatments, optimise medication dosages, predict adverse drug reactions, and enhance supply chain management, ultimately improving patient outcomes and healthcare efficiency.
• GenAI can enhance medical imaging analysis, automate radiology reporting, and facilitate the creation of customised educational materials in multiple languages and literacy levels.