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PaiX Net: A Next-Generation Second-Opinion Platform for Pathology

Baumann, J.; Kanani, B.; Tamboli, S.; Kucherenko, Y.; Fritz, P.; Aswolinskiy, W.; Bosch, C.; Paulikat, M.; Wong, J. K. L.; Arora, B.; Zapukhlyak, M.; Eickmeyer, J.; Pavlova, M.; Laskorunskyi, R.; Kindruk, Y.; Kalteis, S.; Tamang, N.; Aichmüller-Ratnaparkhe, M.; Yazli, G.; Uluc, G.; Adam, P.; Quick, D.; Aichmüller, C.

2026-02-09 pathology
10.64898/2026.02.04.26345344 medRxiv
Show abstract

Pathology faces persistent challenges including a global shortage of specialists, uneven access to expertise, increasing diagnostic complexity, and a growing need for second-opinion consultations. While digital and telepathology platforms address parts of this problem, existing solutions often trade accessibility for structured, workflow-aware clinical integration. At the same time, multimodal medical AI shows promise for diagnostic support but raises concerns regarding transparency, automation bias, and clinical accountability. We present PaiX Net, a structured, AI-augmented second-opinion platform designed to support collaborative pathology consultation while preserving human decision ownership. The platform integrates standardized case templates, moderated expert discussion, and human-centered AI assistance within a scalable, browser-based architecture compliant with data protection requirements. AI support is embedded at defined workflow stages to assist with case structuring, summarization, and exploratory interpretation, while diagnostic conclusions remain under expert control. To mitigate automation bias, AI-generated content is visually separated, collapsed by default, and presented only after independent expert input. PaiX Net incorporates a multimodal medical AI model (MedGemma-4B), selected for its open availability and computational efficiency, and fine-tuned on curated, anonymized consultation cases. An illustrative retrospective evaluation demonstrates substantial reductions in case preparation time and modest but consistent improvements in diagnostically relevant summaries. PaiX Net illustrates how structured, human-centered AI integration can enhance access to expert second opinions while maintaining clinical accountability and supporting continuous human-AI learning in digital pathology.

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