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Introduction: The emergence of artificial intelligence (AI) has presented several opportunities to ease human work. AI applications are available for almost every domain of life. A new technology, Chat Generative Pre-Trained Transformer (ChatGPT), was introduced by OpenAI in November 2022, and has become a topic of discussion across the world. ChatGPT-3 has brought many opportunities, as well as ethical and privacy considerations. ChatGPT is a large language model (LLM) which has been trained on the events that happened until 2021. The use of AI and its assisted technologies in scientific writing is against research and publication ethics. Therefore, policies and guidelines need to be developed over the use of such tools in scientific writing. The main objective of the present study was to highlight the use of AI and AI assisted technologies such as the ChatGPT and other chatbots in the scientific writing and in the research domain resulting in bias, spread of inaccurate information and plagiarism. Methodology: Experiments were designed to test the accuracy of ChatGPT when used in research and academic writing. Results: The information provided by ChatGPT was inaccurate and may have far-reaching implications in the field of medical science and engineering. Critical thinking should be encouraged among researchers to raise awareness about the associated privacy and ethical risks.  Conclusions: Regulations for ethical and privacy concerns related to the use of ChatGPT in academics and research need to be developed.

•The article reviews sex estimation methods used in forensic anthropology casework.•It also discusses the reliability of morphological, metric, molecular and radiographic methods.•Direct methods of sex estimation are found to be more reliable than the other methods.•GM and DSP methods are emerging as valid and widely used techniques in forensic anthropology. Sex estimation is considered as one of the essential parameters in forensic anthropology casework, and requires foremost consideration in the examination of skeletal remains. Forensic anthropologists frequently employ morphologic and metric methods for sex estimation of human remains. These methods are still very imperative in identification process in spite of the advent and accomplishment of molecular techniques. A constant boost in the use of imaging techniques in forensic anthropology research has facilitated to derive as well as revise the available population data. These methods however, are less reliable owing to high variance and indistinct landmark details. The present review discusses the reliability and reproducibility of various analytical approaches; morphological, metric, molecular and radiographic methods in sex estimation of skeletal remains. Numerous studies have shown a higher reliability and reproducibility of measurements taken directly on the bones and hence, such direct methods of sex estimation are considered to be more reliable than the other methods. Geometric morphometric (GM) method and Diagnose Sexuelle Probabiliste (DSP) method are emerging as valid methods and widely used techniques in forensic anthropology in terms of accuracy and reliability. Besides, the newer 3D methods are shown to exhibit specific sexual dimorphism patterns not readily revealed by traditional methods. Development of newer and better methodologies for sex estimation as well as re-evaluation of the existing ones will continue in the endeavour of forensic researchers for more accurate results.

•The Suchey-Brooks method is one of the most researched methods for pubic symphyseal age estimation.•CT-based pubic symphyseal age estimation studies using the Suchey-Brooks method have reported varying accuracies.•A systematic review and Meta-analysis of the extracted data from relevant studies was undertaken.•The Meta-analysis performed indicates that the Suchey-Brooks method is fairly accurate. Forensic age estimation is routinely applied in investigations involving identification of individuals. Over the past century a myriad of methods have been devised for age estimation. One such method, proposed by Suchey and Brooks in 1990, groups the observed changes occurring in the pubic symphysis into six phases, each defined by a corresponding age range. The present study was piloted with the focussed question being to empirically determine the accuracy of the Suchey-Brooks method in computed tomographic age estimation by analysing morphological changes occurring in the pubic symphysis. Original articles pertaining to the use of the Suchey-Brooks method for CT based age estimation were extracted from four different databases- PubMed, CENTRAL, Google Scholar and ScienceDirect. Research papers which were answering the focussed question were selected for data analysis. After assessing the risk of bias of the selected articles, the data was subjected to Meta-analysis. Pooled analysis of correctly/accurately aged individuals/remains using the random and fixed effect models yielded a prediction percentage of 78% and 86%, respectively. Higher percentages were obtained for phase-wise and subgroup analysis, indicating that the Suchey-Brooks method is a reliable method for age estimation.

This communication stresses the importance of the complete lockdown of a developing nation as a powerful tool against COVID-19 acting as a ‘vaccine’. India has been under complete lockdown since 24th March 2020 in addition to other measures emphasized by the Indian Government such as promoting hand washing, social distancing, and use of face masks. A strict lockdown is suggested as an effective measure for containing the novel Corona virus infection transmission worldwide.

Personal identification of an individual has always been a major concern in forensic science. Reconstruction of the facial profile is considered as one of the final stages in the process of identification. Nevertheless, recent advancements in artificial intelligence (AI) and machine learning (ML) have demonstrated remarkable potential in predictive modelling and forensic applications. The current study uses customised machine learning models to predict facial dimensions based on dental and jaw parameters. A sample of 422 participants (201 males and 221 females) from a North Indian population was collected and analysed. Dental casts, anthropometric facial measurements and photographs of the participants were collected with informed consent. ML models such as Support Vector Regression (SVR), Random Forest Regression (RFR), Decision Tree Regression (DTR), and Linear Regression (LR) were trained using dental and jaw measurements as input features for the models. The results show that the ML models predicted the facial dimensions with an accuracy of 90–94% and a very low prediction error of 0.1–0.9 across all facial measurements. Among the models, SVR and LR models perform well, followed by RFR, whereas DFR yielded comparatively lower accuracy. The findings demonstrate that machine learning models (SVR, RFR, DTR, and LR) can be used as novel approach to predict facial dimensions from jaw and teeth parameters. These techniques can be combined with other facial reconstruction techniques to produce more precise and accurate outcomes. The reliability and accuracy in predicting the facial dimensions indicate that the results can be applied in the practical and real situations such as personal identification, forensic investigations, disaster victim identification cases, and archaeological remains where only jaw and teeth are available for examination. Integrating ML-based predictions with traditional facial reconstruction techniques could enhance the accuracy and reliability of forensic identification methodologies.

Background External human ear is considered to be a highly variable structure showing different morphological and individualistic features in different individuals and population groups. The uniqueness of the ear may be useful in establishing the identity of individuals by direct examination, during the examination of CCTV footage or analysis of the ear prints. Considering the forensic significance of the human ear and ear prints encountered at the scene of the crime, the present study is an attempt to evaluate various morphological characteristics of the ear in a north Indian population. Methodology The sample for the present study comprises of 90 males and 87 females aged between 18 and 30 years. All the study participants were from upper reaches of Himachal Pradesh in North India. The morphological characteristics such as overall shape of the ear, size and shape of the tragus, earlobe, shape of the helix, and forms of Darwin’s tubercle were studied in the participants. Results The oval-shaped ear was present among 40% of the males and 44.8% of the females in the study sample. The other types of the ear such as oblique, rectangular, round, and triangular were also found in both sexes. Bilateral asymmetry was observed in the shape of the ear. The shape of the tragus also varied with respect to the left and right sides as well as sexes. The earlobe showed different characteristics in different individuals. In nearly half of the cases in both males and females, the earlobe was found to be attached to the face; in many cases, it was free and in some partially attached. The size and shape of the earlobe also showed variations with respect to sides as well as sexes. The Darwin’s tubercle showed a variety of structures in both the left and right sides in both sexes. Conclusion The present study shows that the individualistic characteristics of the ear can provide very useful information for personal identification in forensic examinations. The shape of the ear and the important structures such as the tragus, helix, earlobe, and Darwin’s tubercle show a variety of structures and individuality. The importance and variability of the human ear may encourage the researchers in conducting further studies and solving the forensic cases pertaining to the investigation of CCTV footage and in examination of dead in airplane crashes, intentional mutilation and dismemberment, explosions, or other mass disasters.

Objective Sex determination is an important parameter for personal identification in forensic and medico-legal examinations. The study aims at predicting sex accuracy from different parameters of ear and nose by using a novel approach of Machine Learning Library, ‘PyCaret’. Results The present research was carried out on 508 participants (264 males and 244 females) aged 18–35 years from north India. Various ear and nose measurements were recorded on each participant. PyCaret employs a train-eval-testing validation approach, yielding a comprehensive output of the model in the form of a table that consolidates the average scores of all models over ten folds, including the respective time values. These models were compared based on performance metrics, and time taken. The logistic regression classifier emerged as the top-performing model, achieving the highest scores of 86.75% for sex prediction accuracy. Nasal breadth has been concluded as the most significant variable in accurate sex prediction. The findings indicate that the majority of the ear and nose characteristics significantly contribute to sexual dimorphism. This novel approach for sex classification can be efficiently used in a variety of forensic examinations and crime scene investigation especially where there is a need for estimation of sex for personal identification.

The present communication emphasizes on a very pertinent issue of aerosol transmission, persistence and surface viability of novel SARS-CoV-2. Studies in this regard have been conducted on previously known human coronaviruses, and similarities have been drawn for novel SARS-CoV-2. The communication highlights that caution should be excercised while drawing inferences regarding the persistence and viability of the novel SARS-CoV-2 based on the knowledge of already known human coronaviruses.

Since December 2019, the global outbreak of coronavirus disease had a significant impact on humanity. Because of the large number of casualties worldwide, the WHO (World Health Organization) declared the coronavirus disease caused by SARS-CoV-2 a pandemic. Since the start of the pandemic, facial masks have become essential as well as mandatory to protect ourselves from COVID-19. As a result of the pandemic, healthcare professionals (HCPs) have been required to wear personal protective equipment (PPE) for extended periods. Wearing face masks for an extended period has been shown to have several negative effects on HCPs. Additionally, face masks have hampered the use of digital techniques for facial identification. This paper examines the effects of wearing face masks for an extended period, as well as the effect of wearing face masks on facial identification technology. The Web of Science, PubMed, and Scopus databases were searched and screened for relevant studies. According to the current review, prolonged use of masks was found to be associated with adverse effects on the face and skin, including acne, redness, rashes, and itching. The use of masks also resulted in headaches, hypoxic conditions, and changes in voice and speech parameters. This communication in no way intends to advocate the discontinuation of wearing masks, on the contrary, the primary goal of this article is to spread awareness about the adverse effects associated with prolonged use of facial masks (N95, KF94, or surgical). This will help in increasing compliance with mask mandates by helping to develop preventive solutions to the problems that tend to deter the general public. This also demonstrates how the use of masks has become a challenge for facial recognition technologies.

Age estimation constitutes an important aspect of forensic research, investigation and human identification. For the purpose of age estimation, various markers within the skeletal framework are employed. Degenerative morphological changes in the skeleton can be used for age estimation in adults. Amongst the various bones, age-progressive changes in the innominate bone are of particular significance in age estimation. Within the pelvis, the acetabulum presents as a durable and well-preserved evidence, characteristic manifestations of which can be employed for age estimation. The present study aimed at a CT-based evaluation of acetabular changes for the purpose of age estimation in an Indian population. CT images of 250 individuals aged 10–88 years were scrutinized according to the features defined in the Calce method of acetabular age estimation. Scores were allotted to the various features and a cumulative score was calculated. No significant bilateral and sex differences were observed. Significant correlation was obtained between the scores for these defined characteristics and the chronological age of individuals. Population-specific regression models were generated for age estimation. The scoring method devised in the present research requires further validation as it represents a new tool for age estimation in medico-legal cases.