In 2025, the emergence of DeepSeek captured widespread attention, as its technology fundamentally reshapes the landscape of the healthcare and pharmaceutical industriesThe acceleration of artificial intelligence (AI) applications in these sectors marks a significant shift toward enhanced decision-making, efficiency, and innovation.

Multiple companies across various fields are now integrating DeepSeek's capabilities into their operationsA notable example comes from Fosun Pharma, which recently launched its PharmAID decision-making platform, harnessing cutting-edge large model technology and first adopting the DeepSeek-R1 modelThis strategic move allows Fosun Pharma to enhance its decision-making processes regarding the commercial value of drugs, thus optimizing its operational perspective and accuracy.

Other major players such as Hengrui Medicine, Innovent Biologics, and Zhiyun Health are also aligning with DeepSeek’s advanced capabilitiesThis has led to the question: what is the true potential of DeepSeek in the pharmaceutical sector? In what transformative ways might this AI innovation impact healthcare?

According to Liu Lihe, Managing Director of CIC Consulting, internet healthcare enterprises can leverage DeepSeek's advantages far more quickly than traditional pharmaceutical companiesFor instance, Weimai, a comprehensive disease management platform, has fully integrated its CareAI application with DeepSeek-V3 and R1 models, merging DeepSeek's advanced logical reasoning capabilities with CareAI’s intelligent processingCompanies such as Yidu Tech and iFlytek have also witnessed remarkable market performances by incorporating similar AI technologies into their operations.

“AI can be key to drug development, production management, diagnosis, and chronic disease management,” Liu explained. “It streamlines every stage from research and development to sales and treatment, significantly increasing efficiency

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DeepSeek's optimization based on a Chinese language corpus allows for rapid advantages in patient-focused AI applications.”

DeepSeek has already demonstrated marked increases in efficiency and cost reduction throughout various critical stages of drug developmentFor instance, the target discovery phase has seen a drastic reduction in screening time from 18 months to just 4 months, thanks to a sophisticated integration of multi-omics data analysis techniques.

The emergence of DeepSeek-R1 in 2025 signifies a paradigm shift from a "trial-and-error" approach to a more predictive landscape in the pharmaceutical industryThe launch of the PharmAID platform by Fosun Pharma is a noteworthy manifestation of this ongoing transformationThe platform boasts AI capabilities for translation and medical writing, thereby significantly enhancing the efficiency of information acquisition and documentation processes.

Fosun Pharma is not alone in its endeavorsHengrui Medicine has boldly announced its adaptation of DeepSeek's AI model into its management assessment system, aiming to catalyze its application within healthcareSimilarly, Yidu Tech integrates DeepSeek into its internally developed "AI Medical Brain," intending to augment the scale and innovative practices of AI in healthcareZhiyun Health is also using DeepSeek-R1 to enhance its data mining and chronic disease management efficiencyMeanwhile, Yingtong Technology's Wan Yu Medical model, after upgrades, connects with DeepSeek-R1 to facilitate advancements in retinal imaging applications.

Liu Lihe emphasizes that DeepSeek’s open-source model is crucial for attracting developers while also providing monetization opportunities through advanced private deployment features, creating a closed-loop ecosystemHe posits that the 2024 capital markets will prioritize cost reduction in inferences and capabilities over simplistic algorithmic race, alleviating pressure on AI development companies to keep investing vast resources into research.

Industry experts frequently highlight the core tenet of AI technology: generating cost efficiencies

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For instance, Anke Biotechnology utilized AI-computer-aided design platforms to markedly reduce drug development timelines and costs, reporting a 70% decrease in design time and a tenfold increase in success rates, with expenses dropping to one-fourth of traditional modelsMajor global players such as Pfizer and Roche are similarly exploring AI applications, with studies indicating that AI-powered drug development can conserve time by 40% to 60% while slashing costs by up to 30%.

Recent publications from Lyon Securities underscore the rapid growth of China's internet healthcare sector, buoyed by AI technologiesCompanies like Alibaba Health and JD Health have seen notable stock price increases, reflecting market optimism surrounding AI's prospective applications in healthcareWith AI, healthcare providers can achieve precise diagnostics and personalized treatment plans, greatly enhancing the quality of medical services and operational efficienciesMoreover, AI can aid institutions in optimizing resource allocation and reducing operational expenses.

Nevertheless, the road to commercial feasibility remains fraught with challengesWhile the AI healthcare sector gains momentum, with large corporations rapidly deploying their own models, the reality is that very few large model products currently see practical clinical applicationsIndustry barriers, stringent safety standards, and regulatory compliance create significant hurdles for commercializationPractical implementation of large medical models faces challenges related to the effective integration within existing healthcare systems as well as ensuring model accuracy and reliability in clinical settings.

Prior interviews with executives from pharmaceutical companies reveal a consensus: there is as yet no mature commercial model for AI in healthcare, despite the strong interest from both established firms and startupsFrom a product perspective, many large model technologies are still in nascent stages of application

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While several content generation tools are user-friendly and aimed at consumers, many users tend to engage with them for entertainment rather than turning into consistent paying customersRealizing the value of large model applications for B2B solutions, enhancing workflow assistance and operational efficiency, remains crucial for sustainable business models.

Recent comments from Zhang Chang, Head of AI and Innovation Business at IQVIA China, highlight the increasing involvement of pharmaceutical companies, medical device makers, and consultancies in AI healthcare initiativesYet, he remains cautious about the pace of commercializationWhile open-source technologies that allow for private deployments address some data privacy concerns, ethical dilemmas and biases remain pressing issues that require resolutionThese are essential challenges facing industry stakeholders.

Zhang posited that while DeepSeek has made functional advancements, it still has hurdles to overcome regarding AI biases, which have not yet seen significant breakthroughsFor the healthcare field, ensuring accuracy and rigor is paramountNonetheless, Zhang recognizes that at least DeepSeek has addressed cost challenges and may have mitigated privacy issues; however, major breakthroughs in accuracy and rigor remain necessary.

To navigate the complexities of commercializing healthcare AI models, Zhang suggests exploring three dimensions: first, broadening expert participation—potentially involving organizations like IQVIA; second, leveraging engineering-based materials and expert assessments to gather industry insights that can bridge organizational divides; and third, focusing on private deployment methodologies that promise to enhance data security and privacy, while monitoring for successful use case developments.

Lastly, a report from Founder Securities asserts that since 2021, China has entered a significant development phase for AI healthcareWith increased healthcare data interconnectivity, iterative algorithm advancements, and intensifying competitive landscapes, only viable commercial models will thrive amidst fierce competition.

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