arXiv:2512.01362v2 Announce Type: replace Abstract: Neural prediction offers a promising approach to forecasting the individual variability of neurocognitive functions and disorders and providing prognostic indicators for personalized invention. However, it is challenging to translate neural predictive models into medical artificial intelligent applications due to the limitations of domain shift and label scarcity. Here, we propose the directed evolution model (DEM), a novel computational model that mimics the trial-and-error processes of biological directed evolution to approximate optimal so
The continuous evolution of AI research is tackling fundamental challenges in applying predictive models, driving the development of novel approaches like directed evolution.
This development could significantly enhance the accuracy and applicability of AI in personalized medicine, overcoming critical hurdles like domain shift and data scarcity in neural prediction.
Neural predictive models become more robust and adaptable for real-world medical applications, potentially accelerating AI's integration into healthcare.
- · AI in healthcare
- · Patients with neurocognitive disorders
- · Computational biologists
- · Biomedical research institutions
- · Traditional diagnostic methods
- · Companies with less adaptable AI models
Improved diagnosis and prognosis for neurological conditions using AI.
Accelerated development of personalized treatments and interventions based on more accurate neural predictions.
Ethical and regulatory discussions intensify regarding AI's expanding role in critical medical decisions and patient-specific interventions.
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