Biomarkers, imaging and AI systems converge to transform medical diagnosis

A new editorial published in Diagnostics argues that the future of disease detection will depend on combining biomarkers, imaging, pathology, genomics and electronic health records into systems that can support faster interpretation and more precise treatment decisions.

Titled "A New Era in Diagnosis: From Biomarkers to Artificial Intelligence," the editorial examines how deep learning and large language models (LLMs) are entering diagnostic workflows, while warning that clinical adoption must be matched by stronger safeguards against hallucinations, opacity, bias and misuse of patient data.

Deep learning is changing image-based diagnosis

The editorial identifies deep learning as one of the clearest sources of change in modern diagnostics. Neural networks, especially convolutional neural networks, can analyze large visual datasets and detect image patterns that are difficult to capture through older statistical methods, making AI especially relevant in dermatology, radiology and pathology.

In dermatology, neural networks trained on large collections of clinical images have reached performance comparable to specialist-level skin cancer classification. In radiology, deep learning systems have shown strong performance in detecting pneumonia from chest X-rays, supporting faster triage of high-risk patients.

The point is not that image-based AI makes clinicians unnecessary. The editorial presents AI as an additional diagnostic layer that can improve speed, consistency and detection in settings where medical imaging volumes are rising and specialist capacity is limited. Hospitals already face growing workloads in radiology and pathology, and AI tools may help prioritize urgent cases, flag suspicious regions and reduce delays in review.

Digital pathology is another major area of change. Whole-slide pathology images contain massive amounts of information, and AI can help classify tumor patterns, estimate differentiation grades and reduce variability between observers. In cancers such as prostate and breast malignancies, this could help make diagnostic pipelines more consistent.

The editorial also points to a growing link between visual diagnosis and molecular medicine. AI models can sometimes detect morphological features that suggest genetic mutations or treatment resistance. That connection matters because cancer diagnosis increasingly depends on both what a tumor looks like and what its molecular profile reveals. A cancer diagnosis may no longer be only a tissue-based classification. It may include imaging patterns, genetic markers, treatment-resistance signals and predicted response pathways. AI can help connect these different layers, but only if the output can be validated and understood by clinicians.

AI can assess image quality, reconstruct anatomical structures and flag high-risk areas for clinician review. These operational uses may prove just as important as direct disease detection because they can help health systems function under workforce shortages and rising diagnostic demand.

Language models bring order to clinical records, but also risk

LLMs play a key role in the next stage of diagnosis. Much of the information needed for diagnosis is locked inside electronic health records, clinical narratives, discharge summaries, referral letters, laboratory comments and specialist reports. These records are often fragmented and hard to process at scale.

LLMs can analyze medical language, summarize patient histories and extract clinically relevant details from unstructured text. In oncology, LLM-supported clinical decision systems have been tested for tumor board preparation, where they reduced the time needed to assemble and synthesize reports while improving the completeness and accuracy of extracted patient information.

Diagnostic decisions are often delayed not by the absence of data, but by the difficulty of finding, organizing and interpreting it. A patient's record may contain years of scattered information across departments and visits. An AI system that can retrieve and structure that information could reduce administrative burden and help clinicians focus on judgment rather than document assembly.

Medication safety is another area where LLMs may have immediate value. The editorial cites work showing that LLMs used as clinical pharmacy co-pilots helped detect more dangerous drug interactions and prescribing errors than standard processes. In complex health systems, where patients may receive multiple medications from different specialists, this type of support could reduce preventable harm.

However, the editorial also makes clear that generative AI introduces risks that are especially serious in medicine. One such risk is hallucination. LLMs can produce statements that are fluent and plausible but medically wrong. In a consumer setting, that may be misleading. In a diagnostic or treatment setting, it can be dangerous.

Systems must be anchored in scientifically verified data before they are used in patient-facing or high-stakes clinical settings. Medical AI cannot rely on language fluency as a proxy for truth. It must be tested against evidence, updated with reliable knowledge and constrained by clinical safeguards.

The black-box problem is another unresolved issue. Many deep learning systems and LLMs do not show a clear reasoning path for their conclusions. That limits clinician trust and complicates accountability. If an AI system suggests a diagnosis, recommends further testing or flags a drug interaction, clinicians need to understand the basis for that output.

Bias is also a major concern. AI models learn from existing data, and medical datasets often reflect inequalities in access, diagnosis and treatment. If training data underrepresent certain populations, the model may perform worse for those groups. That could deepen existing health disparities instead of reducing them.

Privacy and patient rights are equally important. LLMs trained on real clinical records raise legal and ethical questions about consent, data protection and secondary use. Diagnostic AI cannot be separated from governance. Its value depends not only on technical performance but also on how data are collected, protected and audited.

Generalist medical AI could change how diagnoses are made

Current AI tools, as the study states, are often narrow. For instance, a model may analyze retinal images, chest X-rays, pathology slides or clinical text, but is usually limited to one domain. Generalist medical AI would integrate multiple types of information at once, including imaging, blood tests, genetic sequences and clinical narratives.

Clinicians rarely make decisions from a single data point. They combine symptoms, medical history, imaging, biomarkers, pathology, lab values and treatment response. A multimodal AI system could help synthesize these signals and produce more detailed diagnostic reasoning.

The special issue introduced by the editorial includes work on multimodal and multi-omics approaches that combine genomic, transcriptomic, metabolomic and microbiomic data. This reflects a broader movement toward representing disease as a biological system rather than a single abnormal test result.

Such systems could accelerate biomarker discovery, support earlier detection and help tailor treatment to individual patients. In neurology, psychiatry, infectious disease, cancer and cognitive assessment, machine learning is being applied to data types that are difficult to analyze with traditional methods, including speech, sleep patterns, electrophysiology and complex imaging.

AI could help clinicians detect disease earlier, identify patient subgroups, predict progression and match patients to more suitable treatments. It could also help reduce the time between data collection and clinical action. The editorial stresses that diagnostic accuracy alone is not enough. AI systems need transparent validation, external testing, explainability and privacy-preserving methods. A model that performs well in a research dataset may fail in a hospital with different patients, equipment, workflows or documentation practices.