The rise of large language models (LLMs) has opened new possibilities in online medical consultation, but the gap between academic benchmarks and real clinical practice remains huge. MedRealMM emerges as a direct response to this gap: a multimodal benchmark built from real patient-doctor interactions from a Chinese internet hospital, capturing the most clinically demanding moments through a Multimodal Clinical Challenge Point (MCCP) extraction framework. Unlike synthetic sets or simulators, each case preserves the text and image context uploaded by the patient, and evaluation uses case-specific rubrics designed by specialists that reward clinically desirable behaviors and penalize unsafe, unsupported, or contradictory responses. With 5,620 real cases across 64 clinical departments, MedRealMM enables reproducible evaluation of multimodal reasoning in LLMs, and the results are revealing: although some frontier models match or exceed physicians in positive criteria, they still generate more negative criteria, indicating that safe error avoidance remains a central bottleneck. This type of benchmark not only drives research but also has direct implications for developing AI-based healthcare applications.
For technology and software development companies, MedRealMM represents a real use case of how multimodal clinical data—text and images—can be structured to train and validate AI models. Building a similar system in a business environment requires a solid architecture combining natural language processing, computer vision, and a clinical evaluation pipeline. This is where custom aplicaciones a medida (custom software) becomes key: there is no generic solution that covers all medical specialties, and each integration with electronic health records or telemedicine platforms needs specific adaptations. A company like Q2BSTUDIO, specialized in IA (AI) and software development, can provide the necessary expertise to design and implement these pipelines, from secure data capture to generating clinically coherent responses.
Information security is another critical pillar. Patient data is extremely sensitive and its handling must comply with regulations like GDPR or HIPAA. In the MedRealMM context, data was de-identified, but in a production environment ciberseguridad (cybersecurity) must be comprehensive: from encryption at rest and in transit to multi-factor authentication and continuous monitoring. Any online medical consultation platform using AI must undergo penetration testing and have an incident response plan. Working with a technology partner offering specialized cybersecurity services is a necessary investment to protect reputation and patient trust.
Cloud infrastructure also plays a fundamental role. Processing thousands of multimodal consultations requires horizontal scalability and low latency. Public clouds like AWS or Azure provide managed machine learning services, object storage, and NoSQL databases ideal for such workloads. A benchmark like MedRealMM, releasing the dataset on Hugging Face, demonstrates the importance of flexible cloud platforms for experimentation and continuous deployment. Companies looking to build similar solutions can benefit from a cloud-native strategy, and Q2BSTUDIO offers consulting to migrate and optimize architectures on AWS and Azure, ensuring the availability and performance required by healthcare applications.
Beyond language processing, data analytics is essential for continuously improving models. MedRealMM results show that current LLMs still generate responses that, while clinically positive in some aspects, include dangerous errors. To refine these models, a feedback loop based on data is needed: quality metrics, error analysis, and specialty segmentation. Here BI / Power BI comes into play. Healthcare organizations can build dashboards that visualize model performance against real doctors, identifying areas for improvement. A Business Intelligence approach applied to AI allows informed decisions about which cases to escalate to a human or which confidence thresholds to set.
Finally, process automation and intelligent agents are the natural evolution. Imagine a system that, given a patient query with an image of a skin rash, classifies urgency, generates a draft response with references to clinical guidelines, and sends it to a doctor for validation. This requires orchestrating multiple AI agents: one for image analysis, one for extracting history, one for text generation, and an orchestrator managing the flow. agentes IA (AI agents) are revolutionizing complex process automation, and combining them with cloud platforms and robust cybersecurity is the path toward future telemedicine. Companies like Q2BSTUDIO, with experience in process automation, can help design these modular and scalable systems.
In short, MedRealMM is not just an academic benchmark: it is a demonstration of what is needed to bring AI into real clinical practice. From multimodal data collection to evaluation with expert rubrics, through cloud infrastructure, cybersecurity, and analytics, each component requires top-tier software engineering. Companies that bet on custom software and vertical integration of these technologies will be better positioned to capture the value of AI in healthcare. The gap between models and real practice is closed with investment in development, security, and quality data, and that is precisely the ground where Q2BSTUDIO brings its knowledge and experience.
