Artificial intelligence promises to transform medicine, but the path from algorithm to patient bedside is far more complex than headlines suggest.
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Artificial intelligence is no longer just a future idea in medicine; it is already being used. It helps radiologists detect cancers, supports doctors in predicting patient conditions, and assists researchers in developing new drugs faster. However, even with this progress, healthcare is still one of the slowest fields to adopt AI on a large scale.
The main reason is not a lack of technology or funding. Instead, healthcare faces a complex mix of challenges, including ethical concerns, strict regulations, organisational issues, and technical limitations that need careful handling.
This article looks at the key challenges of using AI in healthcare today. It is based on recent research, clinical experience, and regulatory updates, aiming to help patients, healthcare professionals, and policymakers better understand what is required to safely and effectively bring AI into everyday clinical practice.
At its foundation, AI learns from data and in healthcare, that data is among the most sensitive information a person can share. Training effective AI models requires access to large volumes of medical records, diagnostic images, and genomic profiles. The challenge is that using such data requires explicit patient consent, robust anonymisation, and strict governance frameworks.
Even when data is anonymised, patients must still agree to its processing, and research consistently shows that many patients, particularly older individuals, are reluctant to share their records with AI developers. Compounding this is the data minimisation principle: AI can only access the information strictly necessary for a specific task, limiting the breadth of datasets available for training.
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About Me – Dr. Rajnandini Dubey
Hello, I’m Dr. Rajnandini Dubey, a Physiotherapist with a Master’s degree in Sports Physiotherapy and currently working as an Assistant Professor in the Department of Physiotherapy. Along with my academic career, I have been working as a professional academic and medical writer for the past 3–4 years, contributing to research papers, postgraduate thesis, PhD dissertations, and healthcare websites.
Through The Health Journals, my aim is simple: to provide reliable, evidence-based, and authentic health information that readers can truly trust. In today’s digital world, health information is widely available, but unfortunately, not all of it is accurate or scientifically supported. That is why every piece of content shared here is written with a strong focus on research evidence, clinical knowledge, and academic integrity.
My experience in physiotherapy education, research writing, and healthcare content development allows me to analyze scientific literature and present it in a way that is clear, practical, and useful for students, healthcare professionals, and the general public.
I strongly believe that health education should be based on facts, not misinformation. This platform is dedicated to sharing credible updates, research-based insights, and scientifically validated health knowledge so that readers can make informed decisions about their health and learning.If you are looking for authentic health information backed by scientific understanding, you are in the right place.
Regulatory obligations such as the EU's GDPR set a very high bar for data handling, and when a hospital shares records with AI developers, this is permissible only under legally binding data processing agreements. The practical implication: building a compliant AI pipeline in healthcare takes considerably longer than in other sectors.
AI systems learn from past data. This means they can pick up existing patterns but also the biases present in that data. In healthcare, where decisions can directly affect patient outcomes, this is not just a technical issue; it is a serious patient safety concern.
A well-known example from the United States shows how this can happen. A medical risk prediction algorithm was found to recommend extra care more often for white patients than for Black patients, even when Black patients were more unwell. This happened because the system used past healthcare spending as a measure of risk, which reflected unequal access to care rather than actual medical need.
This issue becomes even more important on a global level. AI tools trained mainly on data from Europe or North America may not work as effectively for patients in regions like South Asia or sub-Saharan Africa. Differences in genetics, population characteristics, and access to healthcare can affect how well these systems perform.
As a result, there is both a technical challenge making sure AI works across different populations and an ethical responsibility to ensure that AI does not increase existing health inequalities
One of the biggest challenges in healthcare AI is the lack of a unified global regulatory system. Different countries follow different rules, which makes implementation complex and difficult to manage.
For example, the European Union has introduced the AI Act, which classifies most healthcare AI tools as “high risk.” This means strict requirements for transparency, human supervision, and continuous monitoring after deployment. In contrast, the United States has developed its own approach through the FDA, which has already approved hundreds of AI-based medical devices. At the same time, countries like Japan, Singapore, and South Korea are creating their own regulatory frameworks.
For companies working across multiple countries, this creates a major challenge. An AI system that meets the standards in one country may not meet the requirements in another. As a result, organisations must design flexible compliance strategies to meet different regulations at the same time.
This increases both complexity and cost, especially for smaller companies trying to innovate in the healthcare AI space.
Many hospitals around the world still use older systems that were not built to handle the kind of data AI needs. Electronic health records from different providers often do not connect well with each other. In addition, much of the data is stored in unstructured formats such as doctor’s notes, PDFs, or voice recordings, which AI systems cannot easily use without extra processing.
One of the most common challenges is interoperability the ability of different systems to work together. Data is often stored in separate systems, follows different coding standards, or is locked within proprietary software. Because of this, even a well-designed AI model may struggle to fit into everyday clinical workflows.
Smaller hospitals and organisations face even greater difficulties. Limited budgets and weaker IT infrastructure make it harder to upgrade existing systems or adopt new technologies.
For this reason, experts emphasise the importance of improving data quality and standardisation before introducing AI. In simple terms, the foundation must be strong before advanced tools can be effectively used.
When an AI system makes a mistake that harms a patient, an important question arises: who is responsible? Is it the developer who created the algorithm, the hospital that implemented it, or the clinician who followed its recommendation? These are critical questions, and in many countries, the answers are still unclear.
Most legal systems do not yet have clear rules about responsibility when AI is used in healthcare. This uncertainty makes hospitals and healthcare organisations cautious. Even if an AI tool has the potential to improve patient outcomes, they may hesitate to use it in high-risk situations because the legal consequences of an error are not well defined.
Because of this, healthcare organisations need strong internal governance before using AI in clinical practice. This includes forming multidisciplinary teams with legal experts, clinicians, ethicists, and IT professionals. These teams help define roles, responsibilities, and accountability clearly before any AI system is used in patient care.
Implementing AI in healthcare is not only a technical process; it is also a human one. As clinicians, we rely heavily on our clinical judgment, developed over years of training and experience. It is therefore natural to be cautious about using AI systems, especially when their recommendations are not clearly explained. This caution is not a limitation it is an essential part of safe clinical practice.
Another challenge is the limited number of professionals who understand both healthcare and artificial intelligence. Bridging this gap requires time, training, and collaboration between clinical and technical teams. In addition, successful implementation depends on strong leadership, clear planning, and effective change management, which can be difficult in healthcare systems that are traditionally risk-averse.
In practice, a gradual approach is most effective. Starting with small, well-defined applications, particularly in lower-risk settings, allows teams to evaluate performance and build confidence. Involving multidisciplinary teams from the beginning ensures better integration into clinical workflows. Trust in AI is not built instantly it develops over time through consistent, safe, and reliable use in clinical practice.
In clinical practice, when a physician recommends a treatment, the reasoning can be explained. It is based on patient symptoms, investigation findings, and established clinical guidelines. However, many advanced AI systems, especially deep learning models, do not provide such explanations. They generate results without clearly showing how those conclusions were reached.
This lack of transparency, often called the “black-box” problem, is a major barrier to using AI in healthcare. Clinicians need to understand and verify any recommendation before applying it to patient care. Without clear reasoning, it becomes difficult to trust or critically evaluate AI outputs.
Because of this, regulatory bodies are increasingly emphasising the need for interpretable AI systems. Developers are expected to explain not only the result but also the reasoning behind it. Although research in Explainable AI is progressing, it is still not fully developed for many complex clinical situations.
Overall, the use of AI in healthcare continues to be limited by challenges related to interpretability, bias, and unclear legal frameworks. Addressing these issues will require ongoing collaboration between clinicians, data scientists, and regulatory authorities.
All claims in this article are supported by peer-reviewed research, systematic reviews, and expert commentary from the sources listed below.
— Dr. Rajnandini Dubey
MPT (Sports Physiotherapy)
Assistant Professor | Physiotherapist | Academic & Medical Writer
... • 4 min read
