AI-Enhanced Decision Support Systems

Dear Colleagues,

The field of decision support is undergoing a fundamental transformation driven by the convergence of artificial intelligence and an increase in heterogeneous data. Traditional systems, which often rely on structured data and pre-defined rules, are increasingly inadequate for complex, real-world scenarios, particularly those that involve uncertainty and multi-modal information streams, including, for instance, medical images paired with clinical notes or industrial sensor data alongside maintenance logs. The core challenge, then, lies in moving beyond siloed data analysis and merely passive information delivery, with the aim of creating integrated, prescriptive systems that can effectively synthesize diverse evidence, reason under uncertainty, and provide actionable, context-aware recommendations to human experts. Promising solutions are emerging at the intersection of several key AI disciplines. In particular, the fusion of deep learning for perception (e.g., image processing), causal reasoning for understanding, and, importantly, the contextual capabilities of Large Language Models (LLMs) provides a pathway for building next-generation Decision Support Systems (DSSs). These systems are positioned to act not merely as tools, but also as collaborative partners that help enhance human expertise across critical domains such as healthcare and industrial operations.

This Special Issue will curate pioneering research that demonstrates this paradigm shift, with a specific focus on the following interconnected areas:

• Image processing & computer vision for DSSs;
• Computer-aided diagnosis & clinical decision support;
• Large Language Models (LLMs) in decision-centric workflows;
• Industrial predictive maintenance (PDM) & operational intelligence;
• Multi-modal data fusion: the architectural backbone.

We welcome contributions that address the integration of two or more of these focus areas, presenting end-to-end AI-DSS solutions that are validated on real-world challenges and include critical discussions on deployment, scalability, and human–AI collaboration.

Dr. Lu Meng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied System Innovation is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• decision support systems
• medical image processing
• computer-aided diagnosis
• deep learning
• clinical decision making
• large language models
• financial risk assessment
• industrial predictive maintenance
• smart city management
• multi-modal data fusion