In recent conversations with many industry peers, someone raised a very practical question: now that large language models can search the internet, if we ask AI directly for expert information in a specific field or for a physician's academic focus, it seems possible to obtain a customer profile. So why is there still a need to purchase specialized data services?
This line of thinking is very natural.
As large AI models become widely adopted, using AI for information retrieval and preliminary organization has become a common work habit. But it is worth returning to the underlying requirements of the industry: healthcare has far higher standards for the authenticity, accuracy, and compliance of information than ordinary scenarios. Meanwhile, the output stability of general-purpose large models in fact-intensive content remains an industry-wide concern. Even for content with relatively clear standard answers, such as drug indications or guideline recommendations, large models often produce outdated information or imprecise wording.
1. Why do large models still produce errors when drug information has standard answers?
The core capabilities of general-purpose large models are language generation and semantic understanding. They are good at integrating information and producing fluent content, but they do not inherently have fact-checking capabilities. In a professional field such as medicine, the timeliness of training data and the authority of sources directly affect the accuracy of outputs.
This is not a problem with any single model, but a common reality across the industry: medical content generated by general-purpose models may contain citations that do not match the facts, outdated guideline versions, or imprecise descriptions of usage and dosage. A model can produce an answer that looks reasonable, but it cannot vouch for the truthfulness or timeliness of every conclusion.
If even highly standardized drug information faces these issues, HCP profiling is even less likely to have a single standard answer. Relevant information is scattered across hundreds of online channels and is constantly changing. Using AI-powered internet searches to extract HCP profile data that can support commercial decisions is therefore extremely difficult.
2. From "searchable" to "usable": five professional hurdles in customer profiling
An HCP customer profile is a complete data asset that supports customer segmentation, academic promotion, and resource allocation. From public information to usable assets, there are five critical steps, and each one sits precisely at the boundary of what general-purpose large models can do.
Authenticity: dynamic identities require continuous verification
Physicians' practice information is highly dynamic: practice locations change, professional titles are upgraded, multi-site practice status shifts, and job positions are updated every day. General-purpose large models naturally lag behind because of their training data, and they do not have a mechanism to verify the authenticity of every real-time web reference. The information they output may remain stuck months or even years in the past.
For life sciences companies, the authenticity of customer identity is the starting point of compliance for all marketing activities. The foundation of MeDomino's master data verification service is a publicly sourced, compliant database covering approximately 4 million HCPs nationwide. It is dynamically maintained and updated every day to ensure that every identity record is based on currently valid data, making identities authentic and traceable from the source.
Accuracy: fragmented multi-source information requires standardized alignment
Enterprises often run multiple business systems at the same time, such as CRM, meeting systems, and digital platforms. Information about the same physician often varies across channels: homophones in names, colloquial department names versus official names, and different ways of describing titles can all cause one person to appear as multiple identities.
General-purpose large models cannot connect internal enterprise data to deduplicate identities, nor do they have a unified industry standard for identity alignment. MeDomino's self-developed industry AI model is designed specifically for high-precision matching of non-standard expressions in pharmaceutical scenarios. Difficult cases are then manually reviewed by medical professionals, enabling full-scale cleaning, deduplication, and standardization of existing data so that information scattered across different systems truly points to the right person.
Compliance: usable data must be traceable throughout the entire process
Compliance requirements in the life sciences industry are a hard baseline. Whether data can be used depends not only on whether the information is correct, but also on whether its sources are lawful, whether the process is traceable, and whether it can withstand regulatory review. The retrieval sources of general-purpose large models are mixed. They cannot prove the authority and reliability of every data point, nor can they provide a complete audit trail later.
All MeDomino HCP data comes from publicly available and compliant channels. Every data point goes through compliance analysis, and the full process from collection and cleaning to verification is controllable. The system has passed compliance reviews by several leading multinational pharmaceutical companies. For life sciences companies, this is the prerequisite for data to truly be applied in business.
Completeness: a full-dimensional information loop prevents one-sided bias
General-purpose large models can only capture scattered, shallow public content. They cannot connect physicians' full-cycle academic, clinical, and collaboration behavior data. With incomplete dimensions and a one-sided perspective, the result is like touching only one part of the elephant and mistaking it for the whole picture, which can easily produce misleading partial profiles. A complete and usable HCP profile must connect full-chain information such as publications, academic conferences, brand collaborations, and industry influence, build a multidimensional quantitative assessment system, enable long-term dynamic tracking, and fully restore the physician's real profile.
Data depth: turning surface-level information into business insight requires systematic accumulation
Even when basic information such as identity and department is correct, general-purpose large models mostly provide surface-level information. But when enterprises build customer profiles, the real business questions they need to answer are: What is this physician's academic standing in the field? Which direction do their treatment concepts lean toward? What is their collaboration status with competitors? What types of content do they prefer? What message should be used in the next engagement?
Answering these questions requires integrating multidimensional data from literature, conferences, consultations, academic activities, and more. It also requires an industry-validated standardized evaluation system and long-term continuous tracking. This is exactly the core value of MeDomino HCP360: based on a product system refined through projects with dozens of leading life sciences companies, it conducts quantitative assessments across dimensions such as research, conferences, academic activities, and relationships. It can not only accurately identify target audiences, but also continuously track changes in physicians' treatment concepts and ultimately translate the data into actionable business recommendations for frontline teams.
Precisely because it fits real business scenarios, this tool has achieved a voluntary usage rate of over 85% among frontline sales teams. The value of data is ultimately verified through real use by the business side.
3. General-purpose AI is an efficient tool; a professional foundation is what gives decisions confidence
MeDomino has always believed that general-purpose large models are highly valuable efficiency tools. They can support preliminary information retrieval and material organization, saving us a great deal of time on foundational work.
At the same time, we must clearly distinguish between "being able to generate a fluent answer" and "being able to serve as a reliable basis for business decisions." The former pursues efficiency and convenience; the latter requires authenticity, accuracy, compliance, and practical applicability.
Customer profiling has never been a simple task that can be completed by simply searching and asking. Its foundation is a continuously updated, compliant database; its methodology is an industry-validated standardization framework; and its top layer is deep insight output aligned with real business scenarios. This full set of capabilities is the result of years of industry accumulation at MeDomino, professional team validation, and compliance system safeguards. It is also the core reason data can truly create business value for life sciences companies.
After all, efficiency matters, but reliability matters even more.
