iLABMED in the AI Era: Redefining Laboratory Medicine Through ChatGPT, DeepSeek and Beyond
Funding: This work is supported by Shenzhen Key Laboratory of Biochip (ZDSYS201504301534057) and Shenzhen High-level Hospital Construction Fund (23274G1001).
Graphical Abstract
Trends and future directions for laboratory medicine in the AI era.
Abbreviations
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- AI
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- artificial intelligence
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- LLM
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- large language model
The integration of artificial intelligence (AI) into clinical laboratory medicine has evolved from an emerging concept to a cornerstone of modern diagnostics [1, 2]. Over the past 5–8 years, advancements in machine learning [3], deep learning [4], and domain-specific AI models [5] have revolutionized workflows, enabling laboratories to transition from reactive testing hubs to proactive decision-making centers. This editorial summarizes key developments in AI-driven laboratory medicine, with a focus on innovations like ChatGPT, and the more recently emerged DeepSeek, while critically addressing persistent challenges and proposing forward-looking solutions.
1 The Evolution of AI in Clinical Diagnostics
The past decade has witnessed a paradigm shift in laboratory medicine, driven by AI's ability to process complex datasets and deliver actionable insights. Early applications focused on automating routine tasks, such as image analysis in hematology and microbiology [6, 7]. For instance, convolutional neural networks demonstrated 99% sensitivity in detecting malaria parasites in blood smears, outperforming manual microscopy in both speed and accuracy [8].
However, the limitations of generic large language models (LLMs) became apparent [9]. Studies revealed that LLMs like ChatGPT-4.0 could answer basic laboratory medicine questions with 75.9% preference over human experts [10]. They somehow struggled with complex case analyses, often producing misinformation and inconsistent outputs due to insufficient domain-specific training [9]. This underscored the need for domain-specific AI models underwent extensive training on rigorously validated medical datasets.
The recent launch of DeepSeek exemplifies the rapid evolutions in the AI field, has a great impact on various scenarios including laboratory medicine thanks to its partially open-sourced models with API access and cost-effectiveness. A recent study demonstrated that DeepSeek-R1 performed comparable performance to OpenAI o1 across a range of ophthalmic subspecialty cases, with robust diagnostic reasoning and management decision making capabilities, while offering a substantial reduction in usage costs [11]. It hints that the open-source models might reshape the healthcare system in the coming future, allowing cost-efficient medical LLMs to be deployed within hospital networks.
2 Key Trends and Unique Perspectives
2.1 From Generic to Specialized AI
The shift from generic LLMs to domain-specific AI models marks a critical evolution. For example, DeepSeek-R1 outperformed ChatGPT-3 (GPT-3.5) on the United States Medical Licensing Examination (USMLE) with the June 2022 USMLE MCQ dataset through a zero-shot instruction structure with ROUGE scores and BERTScore metrics. Specifically, in USMLE Step 1 and Step 2 CK, DeepSeek-R1 achieved exact answer matches (ROUGE-2 = 1.0) in 77.7% (73/94) and 76.1% (83/109) of cases respectively, significantly outperforming ChatGPT-3 which failed to achieve any exact matches. This performance differential was maintained in broader textual alignment metrics (ROUGE-L ≥ 0.8) with 87.2% (82/94) and 80.7% (88/109) success rates for DeepSeek-R1 versus none for ChatGPT-3, demonstrating enhanced capability in medical knowledge retrieval through curated training data [12].
2.2 AI-Driven Equity in Global Health
AI-driven diagnostics in low- and middle-income countries show promise in improving diagnostic accuracy (e.g., tuberculosis, malaria), predicting disease outbreaks, and optimizing health resource allocation, thereby addressing systemic challenges such as workforce shortages and weak surveillance systems. While high-resource increasingly settings benefit from automated systems, AI's equitable potential lies in point-of-care testing. Portable devices with embedded AI algorithms, such as those for tuberculosis screening and COVID-19 detection, could bridge diagnostic gaps in low-income regions, particularly in regions with limited health providers and insufficient clinical expertise. However, algorithmic bias remains a critical concern, as training datasets often underrepresentation of populations in less-developed regions, and systemic documentation biases in historical health data risk perpetuating inequities if unaddressed [13].
2.3 Ethical and Regulatory Imperatives
The rapid adoption of AI has outpaced regulatory frameworks, particularly in healthcare, where lagging policies risk exacerbating systemic inequities and ethical breaches. Critical ethical imperatives include addressing algorithmic biases in underrepresented populations, ensuring data privacy compliance with evolving standards and establishing accountability mechanisms for AI-driven clinical decisions. While regulatory bodies must prioritize global standards updating-such as those for data governance frameworks to balance innovation with safeguards against misuses. Ultimately, bridging this regulatory-ethical gap demands urgent investment in flexible governance infrastructures and ethical AI literacy programs to align technological progress with better health outcomes [14].
3 Future Directions: Convergence and Collaboration
The next decade will be defined by the convergence of AI with emerging technologies.
3.1 AI-CRISPR Theoretical Integration
Combining LLMs (e.g., DeepSeek-R1) predictive analytics with CRISPR-based diagnostics could enable ultra-rapid identification of novel pathogens during pandemics.
3.2 Federated Learning Networks
Training AI models across decentralized datasets (e.g., multi-hospital union) enhances universality while significantly reduces raw data exposure [14].
3.3 Multimodal AI in Laboratory Medicine
Contemporary laboratory medicine encompasses a multidisciplinary array of diagnostic modalities, integrating biofluid analyses, medical imaging, wearable sensor data, and genomic sequencing results. These diverse datasets are synthesized to produce comprehensive narrative reports, enabling a holistic understanding of human health and disease pathophysiology. Advanced multimodal AI architectures demonstrate great potential in synthesizing and processing these heterogeneous datasets through computational phenotyping, enabling the generation of contextualized diagnostic narratives that consider with the patient’s individual uniqueness. Therefore, the pervasive integration of multimodal AI in clinical practice should come as no surprise, thanks to its potential dual capacity as a virtual health assistant for patients and a clinical reasoning architecture for clinicians enabling comprehensive analysis the complexity of the diagnostic case [15].
In short, the integration of AI into clinical laboratories is not simply a technological advancement but a redefinition of the field's role in global health. DeepSeek-R1 and its counterparts illustrate how domain-specific AI can improve diagnostic precision, equity, and pandemic prevention and control. However, success hinges on addressing ethical dilemmas, promoting interdisciplinary collaboration, and reimagining regulatory frameworks. As iLABMED goes through this transformative era, clinical laboratories must embrace AI as a collaborative partner—one that amplifies human expertise rather than replacing it.
Author Contributions
Jiuxin Qu: conceptualization (equal), funding acquisition (equal), supervision (equal), visualization (equal), writing – original draft (equal), writing – review and editing (equal). Hongzhou Lu: conceptualization (equal), funding acquisition (equal), supervision (equal), visualization (equal), writing – original draft (equal), writing – review and editing (equal).
Acknowledgments
The authors have nothing to report.
Ethics Statement
The authors have nothing to report.
Consent
The authors have nothing to report.
Conflicts of Interest
Professor Hongzhou Lu is the Editor-in-Chief of iLABMED. To minimize bias, he was excluded from all editorial decision-making related to the acceptance of this article for publication. The remaining author declares no conflicts of interest.
Open Research
Data Availability Statement
Data sharing is not applicable to this article as no datasets were generated or analyzed.
