AI models, particularly deep learning systems, can process vast amounts of data and identify patterns beyond human capacity. Yet, their complexity often means that even their developers struggle to explain exactly why a model produced a particular output. In sectors like finance or retail, such opacity might cause inconvenience or financial risk. In healthcare, it can undermine clinical decision-making, erode patient confidence, and potentially expose organisations to ethical and legal challenges. Clinicians are trained to justify their decisions based on clinical evidence, guidelines, and reasoning. If they are expected to trust AI tools, they need a clear understanding of how and why these systems reach their conclusions, especially when AI outputs contradict traditional clinical instincts.
Explainability serves multiple purposes in healthcare. First, it facilitates clinical validation. Healthcare professionals need to verify that an AI system’s recommendations are consistent with medical knowledge and best practice. Second, it supports regulatory compliance. Bodies such as the Medicines and Healthcare products Regulatory Agency (MHRA) and equivalent European regulators increasingly demand transparency in AI-driven medical devices. Third, explainability enhances accountability. When outcomes are poor, stakeholders must be able to determine whether failures stemmed from human error, flawed AI design, or data biases. Lastly, and importantly, explainability builds patient trust. If patients can understand how AI influences their care, they are more likely to engage positively with digital health solutions.
Building explainable AI involves both technical and human-centred strategies. From a technical perspective, developers use a variety of methods to make models interpretable. These include model simplification, where simpler models like decision trees or logistic regression are favoured over opaque deep neural networks, particularly when interpretability is a primary concern. Another approach is post hoc explanation, where complex models are accompanied by additional tools that provide insight into their behaviour without changing the model itself. Methods such as LIME (Local Interpretable Model-Agnostic Explanations) or SHAP (SHapley Additive exPlanations) are commonly used to highlight which features most influenced a particular decision.
However, technical explanations alone are insufficient if they are not accessible to their end users. A radiologist, a general practitioner, and a patient each have different backgrounds and expectations. Therefore, explanations must be tailored to the audience. Clinicians might appreciate visual explanations highlighting areas of an X-ray that influenced a diagnostic suggestion, whereas patients might require simplified language explaining the rationale behind a treatment recommendation. The design of user interfaces is crucial in making AI explanations digestible, intuitive, and contextually relevant.
Another important consideration in building explainable AI is recognising the limitations of current techniques. Not all AI models can be perfectly explained without sacrificing performance. There is often a trade-off between the complexity of a model and its predictive accuracy. Simpler models are easier to explain but may underperform compared to complex deep learning systems. Organisations must make careful decisions about when to prioritise interpretability over raw performance, particularly in high-stakes settings like oncology, cardiology, or mental health care. Hybrid approaches, combining interpretable models for critical decision points and black-box models for background processing, can sometimes offer a balanced solution.
Ethical considerations are central to the explainability challenge. There is growing consensus that patients have a right to understand how decisions affecting their health are made, especially when algorithms are involved. This aligns with broader principles of informed consent and patient autonomy. Healthcare providers deploying AI must consider how they disclose the role of AI in care pathways. Patients should not only be told that AI is being used but should also receive appropriate explanations that respect their capacity to understand and question AI-driven recommendations.
Involving clinicians in the design and deployment of AI systems is vital for achieving explainability. Developers often build tools in isolation from the realities of clinical workflows. By engaging healthcare professionals early and continuously in the development process, organisations can ensure that AI tools provide the kinds of explanations that clinicians find useful and credible. User feedback loops, pilot testing, and interdisciplinary collaboration between data scientists, healthcare practitioners, ethicists, and user experience (UX) designers can significantly improve the real-world utility and acceptability of explainable AI systems.
Beyond individual systems, there is also a need for organisational strategies to support explainable AI. Healthcare providers should establish governance frameworks that include principles for algorithmic transparency, monitoring, and auditing. AI decisions should be recorded and traceable, allowing organisations to conduct retrospective analyses if outcomes are disputed. Training programmes for clinicians can help demystify AI concepts, ensuring that healthcare workers feel empowered rather than threatened by new technologies. At the same time, public education campaigns can help patients understand the role of AI in healthcare and build societal trust in responsible AI use.
A related concept gaining traction is “causal AI,” which moves beyond correlation-based models towards systems that explicitly model cause-and-effect relationships. Causal AI holds particular promise for healthcare because it aligns more closely with clinical reasoning processes and may offer inherently more interpretable outputs. Though still an emerging field, research into causal models could provide future pathways for developing highly accurate yet explainable AI systems in health contexts.
There are promising examples already emerging of successful explainable AI in healthcare. Some imaging AI solutions provide saliency maps or heatmaps showing precisely which parts of an image led to a diagnosis, helping radiologists quickly verify findings. In predictive analytics, tools are being developed that allow clinicians to adjust input variables and see how predictions change, supporting a deeper understanding of risk factors and treatment outcomes. Furthermore, AI systems in mental health applications are beginning to offer transparent tracking of conversational cues and behavioural markers used in assessments, supporting clinicians’ clinical impressions with data-driven evidence.
Nevertheless, challenges remain. Data bias continues to be a significant problem; if AI systems are trained on unrepresentative datasets, explanations, no matter how clear, may still reinforce systemic inequalities. Moreover, overconfidence in AI outputs, even if explainable, can lead to automation bias, where clinicians defer to AI recommendations even when their own judgement suggests otherwise. Maintaining an appropriate level of scepticism and critical thinking around AI tools is essential to avoid new types of errors introduced by over-reliance on technology.
The regulatory landscape is also evolving. The European Union’s Artificial Intelligence Act, currently under development, is expected to classify high-risk AI systems in healthcare and impose strict requirements around transparency and accountability. In the United Kingdom, regulatory bodies like the MHRA and the Information Commissioner’s Office (ICO) are beginning to provide guidance on explainability, aligning with principles outlined in the NHS Code of Conduct for Data-Driven Health and Care Technologies. Organisations that embrace explainable AI early will be better positioned to navigate these emerging regulatory requirements.
Explainable AI represents not just a technical challenge but a fundamental requirement for ethical, safe, and effective healthcare. Trust is the bedrock of healthcare delivery, and AI systems must earn that trust by being transparent, understandable, and responsive to the needs of both clinicians and patients. As AI technologies continue to evolve, those organisations that prioritise explainability will not only enhance clinical outcomes but also foster deeper engagement with the healthcare communities they serve. At its heart, explainable AI is about more than explaining algorithms; it is about upholding the human values that healthcare depends on: transparency, accountability, empathy, and respect.
The journey towards truly explainable AI in healthcare is ongoing, but the destination is clear. By investing in explainability now, we can ensure that AI’s transformative potential is realised in a way that genuinely benefits patients, supports clinicians, and strengthens the overall resilience and trustworthiness of our healthcare systems.