Personalized AI: Machine Learning for Tailored Interventions in Medicine and Education

Dear Colleagues,

The era of one-size-fits-all solutions is rapidly giving way to a new paradigm of personalization, driven by breakthroughs in Artificial Intelligence and Machine Learning. Two domains at the forefront of this revolution are medicine and education, where the potential to tailor interventions to individual needs promises unprecedented improvements in outcomes. In medicine, this translates to precision treatments based on a patient's unique genetic and lifestyle profile. In education, it means creating adaptive learning paths that cater to a student's specific knowledge gaps and learning pace.

While these fields often advance in parallel, they share a deep-seated foundation in common ML methodologies—from reinforcement learning for dynamic policy-making to recommendation engines for content delivery. This Special Issue, "Personalized AI: Machine Learning for Tailored Interventions in Medicine and Education," aims to bridge the gap between these two critical domains. We seek to foster a cross-disciplinary dialogue, highlighting shared challenges and synergistic solutions in the development of sophisticated, human-centric personalized systems. 

We invite the submission of high-quality, original research articles, reviews, and communications that explore the theory, application, and impact of personalized AI. Topics of interest include, but are not limited to, the following:

Machine Learning for Personalized Medicine:

• Precision medicine: Patient-specific diagnosis, prognosis, and treatment planning.
• AI-driven drug discovery and repurposing for individual patient profiles.
• Adaptive clinical trials and personalized dosing algorithms.
• Predictive models for patient-specific risk stratification.
• Personalized digital health coaching and remote monitoring systems.
• NLP for extracting personalized insights from electronic health records (EHRs).

Machine Learning for Personalized Education:

• Intelligent Tutoring Systems (ITSs) and AI-powered educational guides.
• Adaptive learning platforms that dynamically adjust content and difficulty.
• Educational Data Mining (EDM) for granular student modeling and knowledge tracing.
• Personalized and gamified learning environments to enhance engagement.
• Automated, individualized feedback and assessment systems.
• Recommendation systems for educational resources and learning pathways.

Cross-Cutting Methodologies and Considerations:

• Reinforcement learning for optimizing sequential personalized interventions.
• Explainable AI (XAI) to ensure transparency and trust in personalized models.
• Federated and privacy-preserving learning for sensitive personal data.
• Causal inference for evaluating the effectiveness of personalized strategies.
• Ethical frameworks, fairness, and bias mitigation in personalized AI.

Dr. Dimitrios Karapiperis
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Information is an international peer-reviewed open access monthly 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 1800 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.

• personalized AI
• machine learning
• medicine
• education
• reinforcement learning
• explainable AI (XAI)

• Real-World Applications of Machine Learning Techniques in Information (25 articles)