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Artificial Intelligence as a Tool for Forensic Medicine: Future Challenges, Limits of Application and Critical Issues

A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Digital Health".

Deadline for manuscript submissions: closed (20 October 2023) | Viewed by 7019

Special Issue Editor

Dr. Roberto Scendoni

E-Mail Website
Guest Editor
Forensic Medicine Laboratory, Institute of Legal Medicine, University of Macerata, 62100 Macerata, Italy
Interests: legal medicine; public health; forensic pathology; assessment of personal injury; forensic anthropology; forensic toxicology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Intelligence (AI) is spreading rapidly in the healthcare and medico-legal field with the perspective, for operators, of having to deal, more or less in the short term, with innovative systems capable of significantly changing diagnostic paths and decision-making methods. AI systems require rigorous scientific validation, based on methodologically valid studies, which demonstrate non-inferiority and cost effectiveness compared to the conventional diagnostic and decision-making process. An uncontrolled and ungoverned development of AI is not free from potential risks, deriving from the development of algorithms without scientifically validated standards, from the lack of control over the data processed by expert systems, from possible privacy violations, from the discrimination introduced by the algorithms of programming, from illusory and misleading expectations for healthcare professionals and patients. Furthermore, the algorithm-determined decisions can inevitably undermine the doctor-patient relationship. The purpose of this special issue is both to propose new AI research studies in the various application fields of forensic medicine, and to discuss the existing methodologies by analyzing possible limitations and criticalities from a diagnostic, ethical and legal point of view, as well as identify future challenges to implement and standardize new AI models.

Potential topics include, but are not limited to:

  • Machine Learning in Risk Management
  • Interpretation of data in post-mortem examination
  • The role of AI in analytical methods
  • Automated tools and human efforts
  • Controversy and prospectives of new AI systems
  • Decision-making and medico-legal implications
  • AI and principles of medical ethics
  • Limitis and legal value in a court of law

Dr. Roberto Scendoni
Guest Editor

Manuscript Submission Information

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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. International Journal of Environmental Research and Public Health 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 2500 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
  • decision-making
  • public health
  • forensic medicine
  • legal medicine
  • privacy
  • doctor-patient relationship
  • medical ethics

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Published Papers (2 papers)

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Research

9 pages, 856 KiB  
Article
Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae
by Hiroki Kondou, Rina Morohashi, Hiroaki Ichioka, Risa Bandou, Ryota Matsunari, Masataka Kawamoto, Nozomi Idota, Deng Ting, Satoko Kimura and Hiroshi Ikegaya
Int. J. Environ. Res. Public Health 2023, 20(6), 4806; https://doi.org/10.3390/ijerph20064806 - 9 Mar 2023
Cited by 5 | Viewed by 1869
Abstract
Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. We performed a postmortem [...] Read more.
Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. We performed a postmortem computed tomography (CT) examination of 1000 and 500 male and female cadavers, respectively. These CT slices were converted into 3-dimensional images, and only the thoracolumbar region was extracted. Eighty percent of them were categorized as training datasets and the others as test datasets for both sexes. We fine-tuned the ResNet152 models using the training datasets. We conducted 4-fold cross-validation, and the mean absolute error (MAE) of the test datasets was calculated using the ensemble learning of four ResNet152 models. Consequently, the MAE of the male and female models was 7.25 and 7.16, respectively. Our study shows that DNN models can be useful tools in the field of forensic medicine. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Tool for Forensic Medicine: Future Challenges, Limits of Application and Critical Issues)
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13 pages, 1839 KiB  
Article
Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M
by Romain Bui, Régis Iozzino, Raphaël Richert, Pascal Roy, Loïc Boussel, Cheraz Tafrount and Maxime Ducret
Int. J. Environ. Res. Public Health 2023, 20(5), 4620; https://doi.org/10.3390/ijerph20054620 - 6 Mar 2023
Cited by 6 | Viewed by 4124
Abstract
Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset [...] Read more.
Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Tool for Forensic Medicine: Future Challenges, Limits of Application and Critical Issues)
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