Special Issue "Artificial Intelligence for Personalized Medicine"

A special issue of Journal of Personalized Medicine (ISSN 2075-4426). This special issue belongs to the section "Methodology, Drug and Device Discovery".

Deadline for manuscript submissions: closed (10 June 2023) | Viewed by 1503

Special Issue Editors

1. Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy
2. CEO, GDMH - Gemelli Digital Medicine and Health, Rome, Italy
Interests: adjuvant chemotherapy; non-small cell lung carcinoma; lung neoplasms
Special Issues, Collections and Topics in MDPI journals
1. IRCCS Fondazione Policlinico Universitario A. Gemelli, Rome, Italy
2. Gemelli Generator RWD, Rome, Italy
Interests: machine learning; distributed learning; big data
Generator Real World Data, Fondazione Policlinico Universitario A. Gemelli IRCSS, Rome, Italy
Interests: artificial intelligence; algorithmics; bio-engineering

Special Issue Information

Dear Colleagues,

As digitalization is progressively transforming the healthcare sector, artificial intelligence represents an incredible asset to foster personalized digital solutions in clinical research and care. In this issue, we will discuss several items related to the adoption of AI methods and technologies into the medical sciences. We will dive in is to illustrate how the AI approach harmoniously complements the traditional RCT model by offering some real-case scenarios where present-day technologies are applied in clinical research contexts. The study will discuss about some of the main AI’s current issues, including the use of real-world data and behavioural data, deep learning, and data taken from IOT, which inevitably leads to some ethical considerations on how to preserve both patiens’ data and the institutions’ intellectual property. We will consider federated learning and sandboxing as potential solutions to these latter issues. Subsequently, the study will consider the potentialities of AI in investigating the natural history of chronic pathologies with the support of innovative tools and DSS for clinicians to support heuristics. This study also considers the need of explainable artificial intelligence (XAI) to ensure that doctors understand its potential and correctly apply it to their clinical practice. After addressing the need for the further development of natural language processing tools, especially with local languages, the study investigates the importance of radio genomics as heavily based on AI combined with genomics data, and ultimately addresses the need to optimize the whole research pipeline to increase the complexity of AI tools.

We are soliciting editorials, original research articles, and reviews. Research areas may include (but are not limited to) all the aforementioned topics.

Dr. Alfredo Cesario
Dr. Andrea Damiani
Dr. Carlotta Masciocchi
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. Journal of Personalized Medicine 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.



  • artificial intelligence
  • personalized digital medicine
  • big data
  • digital therapeutics
  • GDPR
  • digital composite biomarkers, digital clinical end point
  • avatar, digital tween

Published Papers (1 paper)

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Perception of Pathologists in Poland of Artificial Intelligence and Machine Learning in Medical Diagnosis—A Cross-Sectional Study
J. Pers. Med. 2023, 13(6), 962; https://doi.org/10.3390/jpm13060962 - 07 Jun 2023
Viewed by 751
Background: In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients’ diagnosis and [...] Read more.
Background: In the past vicennium, several artificial intelligence (AI) and machine learning (ML) models have been developed to assist in medical diagnosis, decision making, and design of treatment protocols. The number of active pathologists in Poland is low, prolonging tumor patients’ diagnosis and treatment journey. Hence, applying AI and ML may aid in this process. Therefore, our study aims to investigate the knowledge of using AI and ML methods in the clinical field in pathologists in Poland. To our knowledge, no similar study has been conducted. Methods: We conducted a cross-sectional study targeting pathologists in Poland from June to July 2022. The questionnaire included self-reported information on AI or ML knowledge, experience, specialization, personal thoughts, and level of agreement with different aspects of AI and ML in medical diagnosis. Data were analyzed using IBM® SPSS® Statistics v.26, PQStat Software v., and RStudio Build 351. Results: Overall, 68 pathologists in Poland participated in our study. Their average age and years of experience were 38.92 ± 8.88 and 12.78 ± 9.48 years, respectively. Approximately 42% used AI or ML methods, which showed a significant difference in the knowledge gap between those who never used it (OR = 17.9, 95% CI = 3.57–89.79, p < 0.001). Additionally, users of AI had higher odds of reporting satisfaction with the speed of AI in the medical diagnosis process (OR = 4.66, 95% CI = 1.05–20.78, p = 0.043). Finally, significant differences (p = 0.003) were observed in determining the liability for legal issues used by AI and ML methods. Conclusion: Most pathologists in this study did not use AI or ML models, highlighting the importance of increasing awareness and educational programs regarding applying AI and ML in medical diagnosis. Full article
(This article belongs to the Special Issue Artificial Intelligence for Personalized Medicine)
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