Challenges and Opportunities in Applying Large‑Scale AI Models to Healthcare

Medical AI's biggest change is the application of large models; overall, the sector quickly adopts new technologies but struggles to produce results due to a shortage of high‑quality physicians and a lack of mature techniques.

The difficulty of implementation stems more from changing doctors' behavior than from technology itself; physicians resist efficiency‑boosting or replacement tools, even when such tools could handle high‑volume imaging tasks that exceed human capacity.

Strategically, the key questions are not whether to pursue AI, but what to build, how to build it, and how to allocate resources at each stage, as well as whether suitable technical partners and internal talent are available.

Enterprises must break down data, business, and process silos and demonstrate the value of digitization, data‑driven, and intelligent solutions, yet evaluating that value is difficult because data quality is often poor and investment is large.

Among use‑case maturity, R&D is the least mature due to long cycles and point‑wise innovation; manufacturing is largely automated with traditional data and IT systems; marketing is the most mature, with CDP platforms and sales‑enablement tools delivering measurable outcomes; customer service automation depends on scale and existing infrastructure.

Medical AI applications fall into two categories: text processing, which is thriving abroad, and domestic doctor‑assistant or agent solutions.

Doctor‑assistant models consider efficiency, replacement, and augmentation from the physician’s perspective, reducing implementation friction.

Domestic medical AI faces high safety requirements that limit grassroots innovation and can lead to monopolies; the lack of high‑quality SaaS services further hinders widespread AI adoption, while data collection, quality, governance, and sharing remain challenging.

Consequently, moving from project to product carries high risk; in well‑developed internet markets, SaaS and large‑model acceptance are higher, which explains why AIGC companies focus on short‑video due to viable business models.

In the United States, engineering culture and financing are more mature, making AI startups easier to fund; many industry applications, including entertainment, law, education, and healthcare, draw heavily on overseas innovations and rely on OpenAI frameworks.

DataFunCon2024 Shanghai will continue the trend with forums on data intelligence and new biopharma, inviting experts to discuss current and future AI implementation.

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线下大会议程

4/19-4/20

时间