Artificial Intelligence in Craniomaxillofacial Surgery

Dear Colleagues,

We are witnessing a paradigm shift in the medical world as Artificial Intelligence (AI) and Machine Learning (ML) permeate every facet of our professional lives. In the field of Oral and Maxillofacial Surgery (OMFS), this digital revolution is no longer a futuristic concept but a present-day reality that is reshaping diagnosis, treatment planning, surgical precision, education, and patient outcomes. This Special Issue, "Artificial Intelligence in Craniomaxillofacial Surgery", aims to explore the transformative impact of AI across the entire patient journey. The integration of intelligent algorithms is evident from the very first interaction, starting with patient intake and automated triage, continuing through advanced diagnostics and culminating in the surgical intervention itself through computer-assisted planning, navigation, and robotic-assisted procedures. Additionally, this Special Issue seeks to explore how AI is influencing personalized treatment planning, predictive analytics, surgical education, and clinical decision-making in craniomaxillofacial surgery. We invite researchers and clinicians to submit original research, reviews, and clinical perspectives that address AI applications in all OMFS sub-specialties, including:

• Diagnostics: AI in detecting pathologies, differentiating soft tissue infections, and interpreting complex imaging.
• Virtual Surgical Planning (VSP): Automated segmentation and patient-specific implant (PSI) design.
• Oncology and Pathology: Predictive modeling for tumor margins and prognostic outcomes.
• Trauma & Orthognathic Surgery: Intelligent algorithms for fracture reduction and aesthetic outcome prediction.
• Workflow Optimization: The use of Large Language Models (LLMs) and automation in clinical documentation and surgical education.
• Ethical and Regulatory Considerations: Bias mitigation, explainable AI, patient privacy, medico-legal implications, and responsible integration of AI into clinical practice.

By bridging the gap between data science and surgical expertise, this Special Issue seeks to define the new standard of precision and personalized care in craniomaxillofacial surgery. We hope this initiative will encourage interdisciplinary collaboration and foster meaningful advancements that translate AI innovation into improved patient care and surgical outcomes.

Dr. Omri Emodi
Dr. Anuj Jain
Dr. Amit Dhawan
Guest Editors

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 and 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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Craniomaxillofacial Trauma & Reconstruction is an international, peer-reviewed, open access, quarterly 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 1500 CHF (Swiss Francs). AO CMF members can benefit from a membership discount (subsidized by the AO CMF and MDPI). More information can be found here: https://www.mdpi.com/journal/cmtr/apc. 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.