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Current and Future Perspectives of Artificial Intelligence in Medicine
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Current and Future Perspectives of Artificial Intelligence in Medicine

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".

Deadline for manuscript submissions: 21 November 2025 | Viewed by 2494

Special Issue Editors

Dr. Giuseppe Consorti

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Guest Editor
Unit of Maxillofacial Surgery, Marche University Hospital—Umberto I, Ancona, Italy
Interests: craniofacial surgery; cleft lip and palate; orbit; facial trauma and surgery; facial fracture; cancer; stem cell; oncology; reconstructive surgery; microsurgery; tumor resection; squamous cell carcinoma; malformation; rare disease; craniofacial malformation; new technologies; CAD-CAM; genetics; oral surgery
Special Issues, Collections and Topics in MDPI journals
Dr. Lisa Catarzi

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Guest Editor
Department of Maxillofacial Surgery, University of Siena, Siena, Italy
Interests: reconstructive and cranio-maxillofacial surgery
Special Issues, Collections and Topics in MDPI journals
Dr. Guido Gabriele

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Guest Editor
Maxillofacial Surgery Unit, Department of Medical Biotechnologies, University of Siena, 53100 Siena, Italy
Interests: maxillofacial surgery; head and neck surgery; microsurgery; lymphedema; supramicorsurgery
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) holds the potential to revolutionize healthcare across all medical domains, marking a pivotal moment for the field of medicine. Traditionally, medical specialties have relied heavily on human interaction and innovation, but physicians are now adapting to incorporate AI as a valuable tool in patient care. AI brings the promise of ensuring patient safety, enhancing autonomy, and providing timely medical assistance, especially in remote areas, while simultaneously reducing the administrative workload, screen time, and the risk of professional burnout for healthcare providers. Moreover, AI has the capability of decreasing medical errors and enhancing diagnostic precision by leveraging algorithms and software to integrate, analyze, and interpret vast amounts of medical data. By automating repetitive tasks, AI can afford healthcare personnel more time to focus on building stronger doctor–patient relationships, emphasizing personalized care, communication, empathy, and trust during times of illness—essential aspects of care that cannot be replaced by AI. However, there remains a need to standardize research in this field to ensure the quality of scientific evidence, understand its benefits and drawbacks, and expedite its integration into mainstream medical practices.

Dr. Giuseppe Consorti
Dr. Lisa Catarzi
Dr. Guido Gabriele
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 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. Life 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 2600 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.

Keywords

  • artificial intelligence
  • machine learning
  • deep learning
  • medical practices
  • artificial neural network
  • computational health informatics
  • predictive analytics in healthcare
  • ethics in AI for medical applications

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Published Papers (2 papers)

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Research

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14 pages, 4139 KiB  
Article
Artificial Intelligence in the Surgery-First Approach: Harnessing Deep Learning for Enhanced Condylar Reshaping Analysis: A Retrospective Study
by Umberto Committeri, Gabriele Monarchi, Massimiliano Gilli, Angela Rosa Caso, Federica Sacchi, Vincenzo Abbate, Stefania Troise, Giuseppe Consorti, Francesco Giovacchini, Valeria Mitro, Paolo Balercia and Antonio Tullio
Life 2025, 15(2), 134; https://doi.org/10.3390/life15020134 - 21 Jan 2025
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Abstract
Background: The surgery-first approach (SFA) in orthognathic surgery eliminates the need for pre-surgical orthodontic treatment, significantly reducing overall treatment time. However, reliance on a compromised occlusion introduces risks of condylar displacement and remodeling. This study employs artificial intelligence (AI) and deep learning to [...] Read more.
Background: The surgery-first approach (SFA) in orthognathic surgery eliminates the need for pre-surgical orthodontic treatment, significantly reducing overall treatment time. However, reliance on a compromised occlusion introduces risks of condylar displacement and remodeling. This study employs artificial intelligence (AI) and deep learning to analyze condylar behavior, comparing the outcomes of SFA to the traditional surgery-late approach (SLA). Methods: A retrospective analysis was conducted on 77 patients (18 SFA and 59 SLA) treated at Perugia Hospital between 2016 and 2022. Preoperative (T0) and 12-month postoperative (T1) cone-beam computed tomography (CBCT) scans were analyzed using the 3D Slicer software and its Dental Segmentator extension, powered by a convolutional neural network (CNN). This automated approach reduced segmentation time from 7 h to 5 min. Pre- and postoperative 3D models were compared to assess linear and rotational deviations in condylar morphology, stratified via dentoskeletal classification and surgical techniques. Results: Both the SFA and SLA achieved high surgical accuracy (<2 mm linear deviation and <2° rotational deviation). The SFA and SLA exhibited similar rates of condylar surface remodeling, with minor differences in resorption and formation across dentoskeletal classifications. Mean surface changes were 0.41 mm (SFA) and 0.36 mm (SLA, p < 0.05). Conclusions: Deep learning enables rapid, precise CBCT analysis and shows promise for the early detection of condylar changes. The SFA does not increase adverse effects on condylar morphology compared to SLA, supporting its safety and efficacy when integrated with AI technologies. Full article
(This article belongs to the Special Issue Current and Future Perspectives of Artificial Intelligence in Medicine)
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Review

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26 pages, 1131 KiB  
Review
Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care—Innovations, Limitations, and Future Directions
by Yoojin Shin, Mingyu Lee, Yoonji Lee, Kyuri Kim and Taejung Kim
Life 2025, 15(4), 654; https://doi.org/10.3390/life15040654 - 16 Apr 2025
Viewed by 908
Abstract
Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration—particularly convolutional and recurrent neural networks—across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology [...] Read more.
Artificial intelligence is rapidly transforming quality assurance in healthcare, driving advancements in diagnostics, surgery, and patient care. This review presents a comprehensive analysis of artificial intelligence integration—particularly convolutional and recurrent neural networks—across key clinical domains, significantly enhancing diagnostic accuracy, surgical performance, and pathology evaluation. Artificial intelligence-based approaches have demonstrated clear superiority over conventional methods: convolutional neural networks achieved 91.56% accuracy in scanner fault detection, surpassing manual inspections; endoscopic lesion detection sensitivity rose from 2.3% to 6.1% with artificial intelligence assistance; and gastric cancer invasion depth classification reached 89.16% accuracy, outperforming human endoscopists by 17.25%. In pathology, artificial intelligence achieved 93.2% accuracy in identifying out-of-focus regions and an F1 score of 0.94 in lymphocyte quantification, promoting faster and more reliable diagnostics. Similarly, artificial intelligence improved surgical workflow recognition with over 81% accuracy and exceeded 95% accuracy in skill assessment classification. Beyond traditional diagnostics and surgical support, AI-powered wearable sensors, drug delivery systems, and biointegrated devices are advancing personalized treatment by optimizing physiological monitoring, automating care protocols, and enhancing therapeutic precision. Despite these achievements, challenges remain in areas such as data standardization, ethical governance, and model generalizability. Overall, the findings underscore artificial intelligence’s potential to outperform traditional techniques across multiple parameters, emphasizing the need for continued development, rigorous clinical validation, and interdisciplinary collaboration to fully realize its role in precision medicine and patient safety. Full article
(This article belongs to the Special Issue Current and Future Perspectives of Artificial Intelligence in Medicine)
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