Deepseek one year later: What impact has Deepseek had in healthcare and AI?

DeepSeek’s main impact a year on has been to dramatically lower the cost of high‑end models, accelerate open‑source healthcare AI adoption (especially in China), and intensify regulatory and safety debates rather than to introduce a single “killer app.”

Healthcare deployment and use cases

By late 2025, DeepSeek variants were deployed in roughly 90–100 leading hospitals in China, used primarily for clinical decision support, diagnostic assistance, treatment recommendations, and documentation.

Hospitals report use across three broad domains: assisting medical decision‑making (diagnosis, treatment, documentation), patient management (education, adherence support, triage, appointment coordination), and research/education (literature review, protocol drafting, teaching).

In primary care and rural settings, DeepSeek has been used to support GPs with triage, diagnostics, and bridging urban–rural care gaps by enabling remote consultations and AI‑assisted decision support on limited infrastructure.​

Clinical performance and limitations

Survey work and benchmarking suggest DeepSeek‑R1 reaches performance comparable to leading proprietary LLMs (e.g., GPT‑4‑class) on several medical benchmarks, including USMLE‑style exams and specialty decision‑support tasks (e.g., pediatrics, ophthalmology).

Its architecture (mixture‑of‑experts, chain‑of‑thought, reinforcement learning) is explicitly tuned for structured reasoning, which is attractive for diagnostics and protocolized care, but papers highlight persistent risks: hallucinations, biased outputs, and vulnerability to adversarial prompts, especially in multilingual and ethically sensitive cases.

Early hospital deployments in China frequently lacked rigorous pre‑deployment validation, transparent reporting, or systematic risk management: in one review, only about one‑third of DeepSeek deployments reported any formal pre‑deployment assessment and fewer than 10% clearly documented risk mitigation.​

Economic and ecosystem effects

DeepSeek R1’s launch in early 2025 triggered a major repricing in global AI equities, with estimates of around USD 1 trillion in US tech market value wiped out and specific names like NVIDIA down about 20% in the immediate aftermath as investors digested the implications of much cheaper, efficient models.

The model is reported to achieve up to roughly 45‑fold efficiency gains versus some peers via 8‑bit computation, compression, and MoE design, significantly lowering inference cost and hardware requirements.​

Its permissive licensing and low cost have shifted value downstream: analyses conclude that end‑users and AI application providers (including healthcare software vendors) are the “winners,” while closed, high‑priced foundation model providers face pricing and margin pressure.

Adoption, trust, and safety concerns

Surveys across India, the UK, and the US show that nearly half of users who have tried DeepSeek for health‑related queries are willing or very willing to switch from incumbent LLMs, driven by perceived usefulness and ease of use, with trust as a key mediator.​

At the same time, health‑policy and ethics papers emphasize that DeepSeek’s rapid, low‑friction rollout has outpaced governance: there is concern that irresponsible or poorly validated use in clinical workflows could harm patient outcomes and that regulators must extend oversight to downstream deployers, not just model creators.

Commentaries frame DeepSeek as a test case for “do no harm” in scalable open models: it democratizes access and enables resource‑constrained settings, but it also amplifies the need for robust evaluation, monitoring, and local regulatory frameworks before routine clinical use.

Strategic implications for healthcare AI

For providers and vendors, DeepSeek has made high‑performance reasoning models a near‑commodity input, shifting defensibility towards proprietary data, integration with hospital information systems, workflow design, and compliance/regulatory capabilities.

For policymakers, its deployment illustrates how quickly an efficient open model can spread through a health system without commensurate governance, sharpening the focus on standards for validation, reporting, and liability in AI‑assisted care.

For investors and strategics, DeepSeek has accelerated the trend toward modular, domain‑tuned, and locally hosted health AI stacks, especially in Asia, and increased competitive pressure on Western closed‑model economics while expanding the overall health‑AI TAM via lower costs.

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