DeepSeek versus ChatGPT: Multimodal artificial intelligence revolutionizing scientific discovery. From language editing to autonomous content generation—Redefining innovation in research and practice

Abstract

Artificial intelligence (AI) has become indispensable in modern research, revolutionizing workflows from code generation to clinical data interpretation. Tools like ChatGPT now underpin tasks as diverse as statistical genomics analysis—reducing costs and labour for laboratories [15, 29]—and clinical manuscript drafting, where they assist non-native English speakers in refining text, minimizing reliance on professional editing services and mitigating language-related publication barriers [18]. Yet, the AI ecosystem is undergoing seismic change with the rise of DeepSeek, a nimble competitor challenging OpenAI's dominance. Launched in January 2025, DeepSeek has rapidly disrupted the field by offering ChatGPT-tier performance at no cost, triggering volatility in global tech markets [7]. Remarkably, this breakthrough originates not from a tech giant but from a small-scale team aspiring to achieve artificial general intelligence rivalling human cognition [25]. Despite limited resources, DeepSeek matches OpenAI's flagship models in mathematical and scientific problem-solving [10], while its open-access model invites unprecedented scrutiny and adaptation by researchers—a stark contrast to ChatGPT's proprietary framework. This editorial examines how AI chatbots like ChatGPT and DeepSeek are transforming clinical research, highlighting their shared strengths and distinct differences. Both models accelerate research workflows by streamlining tasks such as data analysis, diagnosis and manuscript drafting. In scientific publishing, they democratize access by assisting non-native English speakers in refining manuscripts and reducing costs. For patient care, both tools enhance reproducibility by standardizing data interpretation and supporting evidence-based decisions, yet neither replaces human judgement in ethically complex scenarios. Their evolution demands ethical frameworks to balance innovation with accountability, ensuring human oversight remains central to equitable and safe AI integration in healthcare. By comparing DeepSeek's collaborative, transparent approach with ChatGPT's established ecosystem, we highlight paradigm shifts in innovation, ethics, and resource allocation for AI-driven science. AI chatbots like ChatGPT are now ubiquitous in clinical practice, offering transformative support across specialities. In sports medicine, ChatGPT generates evidence-based rehabilitation protocols and injury risk assessments [4, 17, 36], while in arthroplasty, it assists in preoperative planning by analyzing patient history and imaging data—though occasional inaccuracies underscore the need for human oversight [2, 14, 26]. Beyond speciality care, these tools streamline workflows: they draft patient education materials using curated medical databases [37], guide surgeons through complex intraoperative decisions via real-time data synthesis [6], and augment diagnostic accuracy by cross-referencing symptoms with clinical guidelines [24]. Notably, in oncology, ChatGPT has been piloted to personalize chemotherapy regimens based on genomic profiles, though ethical concerns about algorithmic bias persist [12]. The integration of deep learning has further expanded AI's utility, particularly in orthopaedics. Advanced models process radiographs, magnetic resonance imaging scans and intraoperative images to detect fractures, classify osteoarthritis severity, and predict surgical outcomes [13, 20, 30-32]. For example, AI-driven tools now quantify tumour margins in oncology imaging [5] or measure spinal alignment in degenerative disc disease [21], providing clinicians with quantitative insights alongside probabilistic reasoning [33]. Emerging multimodal systems—which synthesize text, imaging, audio (e.g., patient-reported symptoms) and even environmental data (e.g., wearable device metrics)—are poised to unlock next-generation applications, such as dynamic treatment adaptation for chronic conditions [19]. Deep learning models, particularly unsupervised machine learning, hold the promise to shape the future of medicine, including orthopaedics [27, 28]. These tools can enhance the analysis of high-dimensional data, uncover new risk factors, and drive advancements in personalized treatments and precision patient care [9]. The research itself is influenced in many ways by AI, such as automated critical appraisal tools or machine learning for updating living systematic reviews [16, 23]. In parallel, AI tools are reshaping the research process. Platforms like ChatPDF.com and SciSpace.com allow researchers to interact dynamically with papers, extracting key insights without manual skimming, while tools like Elicit.com and Consensus.app leverage AI to validate references, identify gaps in introductions, and ensure alignment with foundational studies. ConnectedPapers.com aids in visualizing citation networks, helping authors contextualize their work within existing literature. These tools, however, differ fundamentally from general-purpose AI models like ChatGPT or DeepSeek, which handle broader language generation and reasoning. Instead, research-focused AI applications are narrowly tailored to augment specific workflow stages—such as reference checks, summarization, or citation mapping—enhancing efficiency without replacing the need for human expertise in contextualizing outputs or addressing domain-specific nuances. For instance, AI-driven platforms like ResearchRabbit facilitate literature mapping, and Writefull offers language editing tailored to academic manuscripts, yet their utility depends on researchers' ability to critically interpret and refine their suggestions. As AI integration in research evolves, the synergy between task-specific tools and versatile language models is poised to redefine efficiency and rigour in scientific publishing. However, ethical and practical considerations, such as overreliance on automated outputs, will require ongoing scrutiny to preserve scholarly integrity. These advancements, however, hinge on addressing critical limitations. While ChatGPT relies on static, proprietary data sets, newer open-source frameworks like DeepSeek promise greater adaptability through community-driven model refinement. As AI evolves, its role in clinical research will likely shift from assistive (e.g., drafting manuscripts) to collaborative (e.g., co-designing trials), reshaping standards for reproducibility and innovation [11]. On 20 January 2025, the Chinese company DeepSeek AI launched DeepSeek-R1, a partially open-source reasoning model designed to rival OpenAI's flagship LLM, o1. Unlike ChatGPT, DeepSeek emphasizes accessibility: its online chatbot, DeepThink, offers free access, while researchers can download and deploy the model on private servers, enabling offline use and enhanced data security. Early independent evaluations confirm DeepSeek-R's parity with o1 in data-driven scientific tasks [10]. Table 1 juxtaposes DeepSeek and ChatGPT across performance, limitations, transparency, accessibility and cultural adaptability, illustrating their shared strengths in complex tasks and mutual struggles with basic errors. Key divergences—DeepSeek's open-weight customization versus ChatGPT's proprietary restrictions, and cost-free access versus paywalls—highlight trade-offs between innovation and practicality. Nuances in cultural handling and ethical risks further contextualize their roles in advancing AI-driven research [35]. While DeepSeek's open framework accelerates innovation by inviting collaborative refinement, it amplifies longstanding challenges inherent to AI systems. First, bias and generalization issues persist: both DeepSeek and ChatGPT inherit biases from their training data, which can skew clinical research outcomes—for instance, underrepresentation of non-European populations in oncology data sets may lead to flawed risk assessments or treatment recommendations. Second, contextual blind spots remain unresolved: while AI excels at pattern recognition, it lacks human empathy and adaptive judgement, limiting its utility in nuanced scenarios like end-of-life care discussions or mental health interventions [3]. Finally, regulatory gaps grow more pronounced as rapid AI adoption outpaces policy development [1]. Ambiguities in accountability—such as determining liability for diagnostic errors or misuse of self-hosted models—leave institutions vulnerable to ethical and legal risks, particularly in cross-border research collaborations. Together, these challenges underscore the need for proactive governance frameworks to balance innovation with patient safety and equity [34]. Open-source AI models enhance transparency and collaboration but introduce risks such as vulnerabilities in publicly accessible weights, which could enable misuse or bypass ethical safeguards. To address these challenges, frameworks like CONSORT-AI and SPIRIT-AI promote responsible integration by mandating the disclosure of AI contributions in research, enforcing human oversight for validation of outputs, and prioritizing ethical training with diverse data sets to mitigate bias [8, 22]. These guidelines aim to balance innovation with accountability, ensuring AI advances align with scientific integrity and patient safety. DeepSeek's emergence signals a pivotal shift in AI-driven research, balancing ChatGPT's established utility with unprecedented transparency and accessibility. However, its open-source paradigm demands rigorous ethical stewardship to mitigate risks of bias, fraud, and privacy breaches. As AI evolves from assistive to collaborative roles, the medical community must prioritize adaptive governance, ensuring these tools uphold the integrity of scientific discovery while democratizing innovation. The authors declare no conflicts of interest. All authors contributed to the writing of the manuscript. No generative AI was used in generating the manuscript.

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