AI in diagnostics: who holds the responsibility?

At a glance

• As AI technologies become integrated into routine clinical workflows, it is incumbent upon biomedical scientists to lead in ensuring that these tools meet stringent scientific standards.
• While complete transparency may be unattainable for certain AI architectures, a pragmatic balance must be achieved.
• AI has the potential to revolutionise diagnostics, but only if deployed within a framework of scientific rigour and ethical oversight with biomedical scientists as guardians of its responsible use.

A deep-learning algorithm scans a blood film in seconds and flags a rare abnormality confidently on the screen. But who is accountable if the algorithm is wrong? This scenario illustrates the central issue at the heart of artificial intelligence (AI) integration in diagnostics.

AI has emerged as a transformative force in diagnostic laboratory medicine, driven by advances in machine learning, deep learning architectures and natural language processing. AI systems have demonstrated significant potential in tasks including automated image analysis, microbial identification and multi-parameter data interpretation. These tools facilitate rapid pattern recognition and data synthesis, augmenting human diagnostic capabilities.

However, unlike conventional diagnostic tools, AI systems derive insights from training datasets, making their performance inherently dependent on the quality and representativeness of input data. This introduces significant scientific challenges concerning validation, accountability, algorithmic transparency, and bias mitigation.

As AI technologies become integrated into routine clinical workflows, it is incumbent upon biomedical scientists to lead in ensuring that these tools meet stringent scientific standards. Their role extends beyond technical application, encompassing governance, ethical oversight, and continuous performance monitoring.

For biomedical scientists, AI is not a rival – it is a partner that expands our capabilities and sharpens our judgement. The laboratories that thrive in the AI era will be those where human expertise and machine intelligence work together seamlessly.

Ensuring scientific and clinical rigour

AI is fast, but speed without validation is dangerous. Validation provides the backbone of trust in AI-assisted diagnostics. It must extend across four critical stages: analytical, clinical, external, and post-deployment surveillance.

The clinical utility of AI-assisted diagnostic tools is predicated on rigorous validation, encompassing analytical performance, clinical applicability and generalisability across diverse patient populations. This ensures that AI tools contribute meaningfully to diagnostic accuracy without compromising safety.

Analytical validation: This initial phase establishes the AI model’s capability to deliver consistent, reproducible results under defined laboratory conditions. Metrics, such as sensitivity, specificity, accuracy, precision and robustness, are evaluated to ensure reliability. Analytical validation must account for potential sources of variability, including differences in sample types, staining protocols, imaging modalities, and operator handling. Robust analytical validation is a cornerstone of confidence.

Clinical validation: Clinical validation determines whether AI outputs correlate with established clinical outcomes and diagnostic standards. This requires carefully designed prospective studies involving heterogeneous patient cohorts reflective of real-world clinical scenarios. Clinical validation assesses performance metrics including positive and negative predictive values, likelihood ratios, and diagnostic odds ratios. The inclusion of diverse demographic groups is critical.

External validation: AI models are susceptible to overfitting– performing well on training datasets but failing to generalise to new data. External validation involves testing AI systems on independent datasets sourced from different clinical environments, laboratories and patient populations. This phase evaluates model robustness, identifies potential biases, and ensures that performance metrics remain consistent across varied contexts. Successful external validation is indicative of a model’s readiness for broader clinical application.

Post-deployment surveillance: AI models do not remain static post-implementation. Continuous performance monitoring is imperative to detect algorithmic drift – where accuracy declines over time due to shifts in data distribution, laboratory methodologies, or clinical practices. Surveillance systems incorporating periodic recalibration, performance audits and real-time monitoring frameworks are essential to maintain diagnostic integrity. Post-deployment surveillance ensures that AI tools remain clinically valid and responsive.

Imagine an AI trained in one hospital laboratory deployed in another, only to misclassify cases because of subtle differences in staining protocols and demographics. Without robust external validation, such risks are invisible until patients are harmed.

Defining professional responsibility

When AI misclassifies – who is accountable? Accountability cannot be delegated to algorithms. While AI systems generate outputs, the biomedical scientist retains professional responsibility for interpretation and clinical correlation.

The deployment of AI introduces a complex accountability framework that redefines professional responsibilities within the lab. While AI systems can support diagnostic decision-making, they do not displace the critical role of the biomedical scientist in ensuring the accuracy of diagnostic outcomes.

Regulatory agencies, including the Medicines and Healthcare products Regulatory Agency (MHRA), consistently emphasise that AI tools are intended as clinical decision-support systems, rather than autonomous diagnostic entities. The accountability for diagnostic decisions and, by extension, patient outcomes remains with biomedical professionals.

Biomedical scientists are tasked with the critical appraisal of AI-generated outputs, exercising professional judgement to interpret findings within the clinical context. This includes evaluating whether AI-derived conclusions align with established diagnostic standards, corroborating findings with additional data where necessary and rejecting AI outputs that conflict with professional expertise.

Documentation and auditability are integral to maintaining accountability. Transparent records detailing AI integration into diagnostic workflows are essential for traceability and quality assurance. These records facilitate regulatory compliance, support clinical governance, and underpin continuous quality improvement initiatives.

Clearly defined roles and responsibilities within multidisciplinary teams ensure that AI’s contribution to diagnostics is understood, scrutinised, and contextualised by stakeholders. This collaborative approach fosters shared responsibility and enhances the reliability of AI-augmented diagnostic pathways.

Technology assists – professionals decide. Accountability remains firmly human.

Overcoming the black box challenge

If we cannot explain how AI reaches a conclusion, can we truly rely on it for patient care? Deep learning models often operate as opaque black boxes, delivering outputs without revealing how decisions are made. For biomedical scientists, this opacity undermines confidence and complicates validation.

A significant barrier to the clinical integration of AI systems is their lack of transparency, particularly with complex models such as deep neural networks, which often provide diagnostic outputs without clear explanations of the underlying decision-making processes. This opacity poses challenges for validation, clinical interpretation, and the establishment of trust.

Explainable AI (XAI) has emerged as a critical area of research aimed at enhancing the interpretability of AI models without compromising performance. Techniques such as saliency maps feature attribution methods and rule-based model approximations help elucidate the rationale behind AI-generated conclusions. These tools provide insights into which data features influence specific diagnostic outputs, enabling the critical appraisal of AI decisions.

While complete transparency may be unattainable for certain AI architectures, a pragmatic balance must be achieved. Biomedical scientists must advocate for the selection and deployment of AI systems that prioritise explainability, particularly in high-stakes diagnostic contexts. Ensuring that AI outputs are interpretable by domain experts is essential for maintaining clinical integrity, facilitating informed decision-making and preserving patient trust.

Biomedical scientists stand at the forefront of this transformation, uniquely positioned to lead the responsible integration of AI into laboratory medicine

Transparent communication with clinical stakeholders is equally vital. Biomedical scientists must articulate the capabilities, limitations, validation status, and oversight mechanisms associated with AI tools in diagnostic practice. This proactive approach fosters a culture of transparency, mitigates the risk of over-reliance on AI systems, and supports informed clinical collaboration.

AI is only as strong as the science behind it – and biomedical scientists provide that foundation. Trust in AI does not come from algorithms alone, but from the biomedical scientists who interpret and guide them.

Identifying and mitigating risks

Bias is not just a technical flaw – it is an ethical failure. Algorithmic bias represents a significant scientific and ethical challenge. It arises when AI models trained on imbalanced or non-representative datasets develop systematic errors that disproportionately affect specific demographic groups. In clinical diagnostics, such biases can manifest as misclassification, inaccurate risk stratification, and inequitable patient care.

Biomedical scientists play a pivotal role in identifying and mitigating algorithmic bias. This begins with rigorous dataset curation, ensuring that training data encompass a broad spectrum of patient demographics, clinical conditions, and lab variables.

Bias detection is a critical component of both validation and post-deployment monitoring. Statistical analyses should be employed to evaluate performance disparities across demographic subgroups, examining metrics such as false-positive and false-negative rates. Discrepancies warrant further investigation and model refinement.

Collaboration between biomedical scientists, data scientists and AI developers is essential to incorporate bias mitigation strategies into model development pipelines. This includes iterative testing, feedback loops and adaptive learning approaches informed by clinical expertise. Ethical stewardship not only safeguards scientific validity, but also upholds the principles of justice and equity in healthcare delivery.

Bias left unchecked is a direct threat to patient safety and healthcare equity. Bias is a risk, but it’s also a scientific opportunity for biomedical scientists to strengthen equity in diagnostics.

Leadership in AI governance

AI changes the tools. Biomedical scientists safeguard the standards. The integration of AI into diagnostic medicine necessitates an expansion of the biomedical scientist’s professional role. Beyond technical proficiency, biomedical scientists must cultivate expertise in AI literacy, data governance, regulatory compliance and ethical oversight. This broadened scope positions them as key stakeholders in the governance and strategic deployment of AI technologies within clinical settings.

AI literacy encompasses a foundational understanding of machine learning principles, algorithmic design, performance metrics and interpretability challenges. This knowledge equips biomedical scientists to critically evaluate AI tools, engage in meaningful dialogue with developers and contribute to informed decision-making processes.

Data stewardship is central to the ethical deployment of AI. Biomedical scientists must ensure the integrity, security and appropriate use of datasets in AI training, validation, and deployment. This responsibility includes adherence to data protection regulations, ethical guidelines, and best practices for data management within the laboratory environment.

Active participation in institutional AI governance structures, policy development and interdisciplinary committees enables biomedical scientists to influence the ethical and scientific standards guiding AI integration. Their involvement is critical in shaping policies that balance innovation with patient safety, regulatory compliance, and professional accountability.

Scientific rigour and ethical integrity

AI has the potential to revolutionise diagnostics, but only if deployed within a framework of scientific rigour and ethical oversight. Biomedical scientists are not passive adopters of this technology; they are the guardians of its responsible use.

The potential of AI in diagnostic medicine is contingent upon rigorous scientific validation, steadfast professional accountability, algorithmic transparency and proactive bias mitigation. These pillars uphold the integrity of diagnostic practice, safeguard patient safety and ensure that AI technologies serve as augmentative tools rather than autonomous arbiters of clinical decision-making.

Biomedical scientists stand at the forefront of this transformation, uniquely positioned to lead the responsible integration of AI into laboratory medicine. By embracing expanded roles in governance, validation, ethical oversight and interdisciplinary collaboration, biomedical scientists will ensure that AI’s promise is realised in a manner that enhances diagnostic precision, maintains professional standards, and protects patient welfare.

AI will never replace biomedical scientists – but biomedical scientists who embrace AI will shape the future of diagnostics.

Explore further

• Evaluating accountability, transparency, and bias in AI-assisted healthcare decision-making: b.link/vp359and
• Artificial intelligence vs. pandemics in The Biomedical Scientist: b.link/g0su9w4f
• IBMS Support Hub on AI in Education, Training, and Assessment: b.link/0ojm8hob

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