Artificial intelligence is rapidly reshaping the healthcare sector, offering new capabilities in diagnosis, treatment planning and patient management. However, a critical issue threatens to undermine its transformative promise: the narrow scope of the data used to train these systems. Many AI models draw predominantly on healthcare data from the United States and Europe, inadvertently reinforcing regional biases and limiting the range of care options available to patients worldwide. To harness AI’s full potential, especially in the context of precision medicine and emerging therapies, healthcare must broaden its data foundation to include global perspectives and non-traditional approaches.
The Limits of Local Data in a Global Healthcare Landscape
While AI technology itself is advancing quickly, its success in healthcare is fundamentally constrained by the quality and diversity of its underlying data. In many current applications, AI is fed with information derived from specific national healthcare systems, especially from the U.S. This narrow training base means AI systems tend to replicate the clinical practices, preferences and regulatory priorities of those regions. For example, U.S.-based datasets often favour pharmacological and surgical interventions, marginalising approaches such as herbal medicine, lifestyle-based prevention strategies or integrative care, which may be more prevalent and effective in other parts of the world.
The result is a skewed clinical lens that overlooks viable alternatives simply because they fall outside the established protocols of a single healthcare system. Such limitations are particularly detrimental in a globalised medical environment where patients increasingly seek information and treatment across borders. For AI to become a genuinely helpful decision-support tool, it must be trained on datasets that encompass a wide variety of patient profiles, treatment methods and cultural contexts. Otherwise, its outputs will remain functionally narrow and may even exacerbate existing healthcare disparities.
Preparing Infrastructure for Inclusive AI Integration
The effectiveness of AI in healthcare depends not only on the quality of its algorithms but also on the availability and interoperability of data. Fragmented records, outdated system interfaces and poorly connected data silos hinder AI’s ability to deliver accurate, comprehensive insights. In this context, integration platforms that support secure and real-time connections between electronic health records (EHRs) and external applications are becoming essential.
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Platforms built on RESTful APIs, for example, play a crucial role in unlocking data that would otherwise remain trapped in isolated systems. By facilitating the seamless exchange of information between EHRs and specialist software tools, these solutions allow AI models to work with more complete patient profiles and contribute more meaningfully to clinical decisions. This infrastructure upgrade is a prerequisite for scaling AI across healthcare settings, especially as more advanced use cases—like personalised medicine and predictive analytics—become standard practice.
Importantly, integration must also include data from genomic and lifestyle sources, which are not typically housed within traditional EHR systems. Bringing together genetic, clinical and real-world evidence enables a richer context for AI to interpret health conditions and recommend treatments. This not only improves individual outcomes but also positions healthcare systems to benefit from broader trends in population health and disease prevention.
Broadening the Scope: Precision Medicine and Alternative Therapies
As medicine becomes more personalised, AI must evolve to account for individual variability in biology, environment and lifestyle. Genomics provides a key foundation for this shift, offering insights into how a person’s genetic makeup influences their response to specific treatments. However, genomic data alone is insufficient. Its power is only realised when combined with detailed clinical records, environmental data and treatment outcomes from diverse populations.
Training AI on such integrated datasets can help move healthcare away from reactive approaches and toward truly predictive and preventive care. For instance, rather than prescribing medication and waiting to see if it works, clinicians could use AI tools informed by genomics to recommend treatments with a higher likelihood of success from the outset. This not only saves time and cost but also reduces the risk of adverse effects.
Equally important is the need for AI to consider non-mainstream therapies that may not be approved or reimbursed in one country but are widely used elsewhere. These could include traditional medicines, dietary interventions or novel therapies still under investigation. Including them in AI training allows for more comprehensive treatment suggestions and enables informed discussions between patients and clinicians. It also acknowledges the reality that effective healthcare is not confined to one regulatory environment.
By embracing a broader data strategy, AI becomes a tool not just for efficiency but for equity. It offers patients a wider view of what is possible and helps clinicians deliver care that is more aligned with the patient’s values, cultural context and medical history. This approach empowers decision-making and reinforces trust in the health system—both of which are essential for sustained adoption of AI in clinical practice.
AI has already begun to demonstrate its transformative potential in healthcare, but to fulfil this promise, it must move beyond its current limitations. Narrow training data, fragmented system integration and a reluctance to consider non-traditional therapies all hinder AI's usefulness and equity. To build AI systems that support better outcomes for all patients—not just those within certain borders—healthcare must adopt a more inclusive, globally informed and interoperable data approach. Only by doing so can AI become a truly valuable partner in the pursuit of personalised, effective and compassionate care.
Source: Healthcare IT News
Image Credit: iStock
