AI-Guided Clinical Image Interpretation System for Precision Treatment Planning

Abstract

Radiological examinations increasingly leverage artificial intelligence to enhance diagnostic performance by compensating for expertise shortages, particularly in specialized interpretation. Despite academic AI models achieving compelling accuracy, adoption remains low. An objective, evidence-based examination of AI-guided clinical image interpretation seeks to expose the underlying reasons. The primary challenge lies not in accuracy but in fully automated analysis, especially for multimodal and multidimensional datasets. Introduction of uncertainty measures, image explainability, and real-world data reduce recognition to a decisive-supporting role— helping radiologists to detect previously unnoticed abnormalities—or even to precautionary analysis. Patients requesting precision medicine and therapy planning—and those at high risk of neglected pathology—stand to gain the greatest benefit. Relevant AI architecture considers imaging cues suitable for precision medicine or requiring additional methods to obtain interpretable images. Successful performance testing against an external, hidden reference dataset promotes vital user trust. Machine learning achieves best results where all potentially pathological abnormalities are identified and their presence virtually guaranteed, as in multi-class recognition. Data scarcity, however, impedes clinically relevant solutions. Explainability, essential for medical applications and clinical acceptance, extends simply beyond clinical decision support to provide saliency maps and, better still, clinically meaningful image regions or qualities. Attention maps may even bring a form of rule-based reasoning within reach. Clinical integration that reproduces the technical conditions under which model diagnostic measures are calculated raises the challenge of uncertainty quantification—a necessary condition for clinical decision support. Convolutional neural networks trained for photo-quality classification, par excellence supervised image quality assessment, provide the basic cues for uncertainty quantification. Imaging acquisition, modality, and systems are also defined in terms of current PACS/EPIC systems. PE, pre-symptomatic examination or precautionary examination, is defined as a patientuser group prepared to cover the cost of an examination with lower diagnostic accuracy and a high level of uncertainty