AI, robotics & blockchain: The next big investment wave in healthcare

Biotech and pharmaceutical R&D: Still high risk, but with new tools

Drug development has always carried high uncertainty. Most experimental treatments never make it past clinical trials, and even those that do often face delays or regulatory setbacks.

To address these challenges, biotech firms are increasingly leveraging artificial intelligence (AI) and machine learning to analyse molecular structures, predict protein folding, and identify potential drug targets. In clinical trials, AI aids in designing protocols, selecting suitable patient populations, and forecasting outcomes, thereby improving efficiency and reducing costs.

Pharmaceutical companies are also adopting digital infrastructure to manage large-scale data from genomics, electronic health records, and wearable devices. This enables more precise targeting of therapies, as well as better measurement of treatment outcomes.

Despite these advances, the industry continues to grapple with long development timelines, regulatory complexities, and intense competition. Still, investors are watching biotech firms that combine strong scientific research with advanced analytics and adaptive trial models.

Barriers to healthcare tech adoption: Regulation, ethics, and institutional inertia

The pace of healthcare innovation is accelerating, but its adoption is not keeping up. Even when technologies prove effective in trials, implementation often stalls once they reach hospitals or public systems. Several systemic barriers stand in the way:

Regulatory uncertainty

Healthcare is one of the most tightly regulated sectors in the world, with oversight from bodies like the FDA, EMA, and national health agencies. While this protects patients, it also slows innovation. Many digital health tools and AI-based solutions don’t fit neatly into existing regulatory categories, creating ambiguity around approval pathways. As a result, companies often face long delays in getting to market, particularly when their products evolve faster than the regulatory frameworks that govern them.

Ethical concerns around AI

As AI becomes more integrated into healthcare decision-making, questions of trust and accountability are becoming more urgent. Bias in training data, lack of transparency in algorithms, and unclear lines of responsibility raise ethical risks. Clinicians may hesitate to rely on AI outputs, while patients may resist care driven by opaque systems.

Resistance to new technology

Hospitals and health systems often rely on legacy software and established workflows that are difficult to update. Integrating new tools can be time-consuming and costly. Procurement cycles are slow, retraining staff takes time, and technical integration isn’t always seamless. In many cases, institutional habits are the greatest obstacles.

Lack of system-level readiness

Even the most promising healthcare technologies can’t scale if the broader ecosystem isn’t equipped to support them. Many markets face budget constraints, fragmented digital infrastructure, and misaligned incentives between payers, providers, and policymakers. These structural limitations—outside any single institution’s control—can make widespread adoption impossible, regardless of product quality or clinical need.

Policy and public funding as investment catalysts

Healthtech adoption often depends on more than innovation alone. Public policy and funding play a decisive role in turning promising technologies into scalable solutions.

Governments in the US, UK, and EU are backing digital transformation through large-scale programmes:

• United States. Executive orders aim to accelerate the development of AI infrastructure, which may indirectly benefit healthcare technologies.
• United Kingdom. The NHS Long Term Plan prioritises virtual care and data-driven systems. Recent initiatives include the launch of the Cancer 360 app, designed to streamline cancer diagnosis and care by consolidating patient data into a single dashboard for medical teams.
• European Union. The EU4Health program, with a budget of EUR 5.3 billion, invests in cross-border digital health capabilities, aiming to strengthen healthcare systems and improve overall health outcomes across member states.

These efforts create more stable demand and reduce investment risk by clarifying regulatory paths and encouraging adoption. Many also promote public–private partnerships, allowing companies to scale faster through institutional contracts.

Reimbursement models are evolving, too. More countries now support payments for virtual care, AI-supported diagnostics, and remote monitoring. This is helping turn pilot projects into long-term revenue streams.

For investors, tracking public funding and regulatory alignment is crucial. Policy shapes where and when adoption takes hold.

AI, robotics, and blockchain are reshaping healthcare

Healthcare is moving beyond hospitals and pharmaceuticals into a tech-driven sector. AI is improving diagnostics and accelerating drug discovery; robotics is advancing surgery and hospital logistics; and blockchain is strengthening data security and payment transparency.

This technological transformation is influencing investment flows, with digital health tools becoming integral components of healthcare infrastructure. While the sector growth will depend on policy alignment, clinical integration, and system readiness, we see a clear trajectory: these technologies are redefining how care is delivered, measured, and scaled.