Contents (Artificial Intelligence Technology and Forensic Science)
- Introduction
- Artificial Intelligence and Forensic Science
- Terms related to AI and Medicine
- Uses of Artificial Intelligence in Forensic Science
- Limitation of AI in Forensic Science
- Conclusion
- References
Introduction
Artificial Intelligence Technology and Forensic Science collaborate to revolutionize crime investigation and evidence analysis, enhancing accuracy and efficiency. The integration of artificial intelligence has become more prevalent in various areas, revolutionizing Medical and forensic science and solving all complex problems. This is possible due to recent updates in computer science. Kaplan and Haenlein define AI as “the ability of a system to interpret external data correctly, learn from such data, and use it to achieve specific goals and tasks through flexible adaptation.”
In 1950, Alan Turing published his influential paper ‘Computer Machinery and Intelligence’, which described machines and thinking.
John McCarthy (1956) defines Artificial intelligence and is known as the Father of Artificial Intelligence.
In medical science, AI helps in accurate diagnosis, personalized treatment plans, and improved patient health outcomes. By early disease detection, machine learning algorithms can analyze large amounts of data and provide framed treatment plans. AI-powered imaging technologies, such as X-ray CT scans and MRIs, have enhanced radiology, enabling faster and more accurate medical image interpretation. AI-driven robotics systems are used in complex surgeries to reduce human operation errors.
Artificial Intelligence Technology and Forensic Science
Forensic science has also embraced AI, augmenting investigation techniques and contributing to more effective criminal justice. In forensic science and criminal Investigations, experts face many challenges due to the large amount of data, tiny pieces of evidence in the chaotic and complex environment, traditional labs and methods, and insufficient knowledge, which may lead to investigation failure. Investigation work is very much simplified now by using digital forensic science technology. Thorough examination and comparison of massive amounts of data and evidence, such as images, videos, audio, fingerprints, handwriting, etc., within a very short time that is also error-free, are possible due to technology.
With AI in forensics, Algorithms can be programmed to analyze data, recognize patterns, identify anomalies, or provide suggestions and decisions based on the data provided. These sets of instructions and mathematical operations can be used by artificial intelligence to perform specific tasks within the field of forensics.
Forensic medicine is a medical area that aims to prepare scientific medical and biological evidence for applying judicial rules. So, forensic medicine and pathology must use new expertise techniques. The classic way of performing an autopsy and drawing up an expert report has many limitations, but these can be reduced with the help of AI. Demonstrating the feasibility and advantages of using AI technology in forensic medicine and pathology, a new worldwide direction has been launched, which includes technology adaptability challenges with the potential of integration into the well-known medico-legal practice.
Terms related to AI and Medicine
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Natural language processing
- Robotics
- Artificial Neural Network
- Convolutional Neural Network
Machine learning is a branch of artificial intelligence that involves the development of algorithms and models that enable computers to learn from data and make predictions or decisions without being explicitly programmed.
Deep learning is also a branch of machine learning that utilizes artificial neural networks with multiple layers to learn complex patterns from data. These neural networks draw their structure and operation from the human brain, which consists of interconnected layers of nodes or neurons that process information. Deep learning algorithms automatically learn hierarchical representations of data at different levels of abstraction, allowing them to extract features and patterns from raw input data.
Robotics is a reprogrammable multifunctional manipulator designed to move material, parts, tools, or specialized devices through various programs. It is used for its precision, especially in surgery.
Artificial neural networks are a part of synthetic intelligence algorithms that emerged in the 1980s due to cognitive and computer research developments.
Convolutional neural networks are similar to regular networks because they are also composed of neurons with learnable weights.
Uses of Artificial Intelligence in Forensic Science
There are numerous applications of AI in the field of forensic science. Facial and voice recognition, handwriting identification, identification and age estimation from teeth, ballistics expertise and additional shooting factors, traumatic injuries – bruise colours recognition, forensic toxicology, sperm identification, crime scene reconstruction, virtual autopsy, etc., are now possible with high accuracy—identification of an unknown person from a skull bone by creating a 3D image and superimposition techniques. With the help of AI, they can be analyzed and compared faster and more accurately than forensic experts.
Forensic Science In Criminal Investigation
We can use AI in forensic Odontostomatological identification. Historically, identification by visual or clinical methods has had some difficulties. The existing technique can be exhausting and complicated in considerable scale expertise, which requires many forensic odontostomatological identifications such as human bite marks, sex estimation, age estimation, and dental comparison. AI provides a set of data and a correctly assigned algorithm in the case of hyperparameters, which allows for creating a predictive model at a very high level of performance. The models are based on either artificial neural networks or convolutional neural networks. During COVID-19, person identification with the help of AI would have been beneficial, considering the number of deaths worldwide and the existence of unknown bodies that needed to be identified.
Identification is essential in the field of forensic science as it is required in the identification of criminals, identification of unknown dead bodies, and in mass disaster situations like earthquakes, bomb blasts, floods, etc. The traditional way of identification involves anthropology, facial description tattoos, scars, and body marks, which are very time-consuming. But with the advent of AI, machines can be used to establish identity. When the machine is provided with the input of various body parameters, it will store this data electronically.
Pattern recognition is another important aspect of crime investigation. It is a process of automatic machine recognition that is categorized according to the type of learning process. With the help of AI, this technique is done in less time. In multimedia analysis, AI accurately analyzes images, videos, or CCTV footage in criminal investigations.
AI technology is also successfully applied in the field of macro-analysis. Artificial neural networks (ANN) can analyze brain images during macro analysis to detect whether the person is telling a lie. ANN technology also helps in forensic ballistics. It can guide experts in searching gunpowder and cartridge cases and support them in comparing bullet marks, firearm identification, and other ballistic evidence from the database with the help of image processing without any manual interference.
AI technology is also used to develop e-noses for sensing and analyzing aromas released from various sources for forensic applications. These e-noses are aiding in the detection of multiple explosive materials and biological and chemical weapons.
AI technology is also popular in forensic medicine. It helps in crime scene reconstruction. The time since death can be better predicted by analyzing enzymes such as Lactate dehydrogenase, Aspartate Aminotransferase, Triglycerides, cholesterol, etc. This data, along with the blood pH level, can be interpreted and compared to different databases to estimate the death of time. Advanced AI algorithms enable the identification of unknown bodies from teeth, especially in mass disasters.
AI-enhanced virtual autopsy is an emerging technique in medicine. Machine learning tools will take an image of the body with the help of a CT scan or MRI technique. The machine will process the data and conclude the disease condition of the organs, and the cause of death can be framed. The method will also help collect samples from the specific pathological sites of organs and help establish an accurate disease diagnosis.
Forensic Science In Criminal Investigation
Sperm detection is an essential aspect of investigating cases of sexual assault, providing evidence of sexual contact. However, the absence of semen or small quantities thereof does not invalidate a victim’s account. Therefore, the biological material is treated under an optical microscope. This work can be very time-consuming. Convolutional neural networks trained by the VGG-19 network can reduce the scanning time by locating the sperm on the microscope images.
AI used in forensic toxicology expands the search field and links with millions of pieces of data to identify toxic substances, drugs, and metabolites. Automated toxicological analysis through AI allowed for quantitative and qualitative identification. AI can play an essential role by providing a data set as a sample, which will increase the precision of the method efficiency and reduce the cost of investigation.
In postmortem interval estimation, applying next-generation sequencing and AI techniques, the forensic pathologist can enhance the data set of microbial communities and obtain detailed information on the inventory of specific ecosystems, quantifications of community diversity, description of their ecological function and their application in pathology through post mortem sequencing of the cadaveric microbiome.
Estimation of Time Since Death
In 2015, Maharashtra police started using AI technologies in crime control by acquiring “predictive policing software” as a part of the scheme; the department has also procured a Universal Forensic Extraction Device (UFED) from a leading global brand used in digital forensics and investigation.
Read article on: Predictive Policing: Review of Benefits and Drawbacks
As per the 2016 crime report, Uttar Pradesh has the highest crime rate in India. To handle this, UP police started using various AI technologies, such as crime mapping analytics and predictive systems, in partnership with the Indian Space Research Organisation (ISRO).
In December 2018, DIG of Police Om Prakash Singh launched an AI-powered mobile application named Trinetra. Trinetra records 5 lakh criminals, each with a picture, address, and criminal history.
Limitation of AI in Forensic Science
Despite its transformative potential, integrating AI into forensic science presents several challenges. One of the primary concerns is the reliability and transparency of AI algorithms. Forensic experts must ensure that AI systems produce accurate and reproducible results and can withstand scrutiny in legal proceedings. Additionally, biases embedded in AI models can lead to unjust outcomes, particularly in facial recognition and predictive policing applications.
Moreover, the adoption of AI technologies requires specialized training for forensic professionals to understand their capabilities and limitations fully. The scarcity of qualified personnel with AI and forensic science expertise poses a significant barrier to widespread implementation.
Conclusion
Forensic science and criminal investigation are advancing daily with the application of the latest scientific technologies. In recent years, these technologies have demonstrated significant potential in optimizing the data analysis and interpretation process. One of the critical challenges in using AI in this context is ensuring the quality of scientific data. Collecting and cleaning data is of fundamental importance to ensure the performance and accuracy of AI algorithms. We can reach this technology’s maximum potential in forensic medicine only by providing reliable and accurate data. The benefits of AI within the justice system are evident. Thus, the progressive integration of AI in forensic medicine and pathology represents a significant step towards improved well-being and security of society.
References
- Gupta RR Roe of Artificial Intelligence (AI) in Forensic and Medical Science. IP INT J Forensic MED Toxicol Sci 2023;8(2):47-47
- Piraianu, A.-I.; Fulga, A.; Musat, C.L.; Ciobotaru, O.-R.; Poalelungi, D.G.; Stamate, E.; Ciobotaru, O.; Fulga, I. Enhancing the Evidence with Algorithms: How Artificial Intelligence Is Transforming Forensic Medicine. Diagnostics 2023, 13, 2992. https://doi.org/10.3390/ diagnostics13182992 Academic Editors: Migue
- Wankhade T D, Ingale S W, Mohite P M, et al. (August 25, 2022) Artificial Intelligence in Forensic Medicine and Toxicology: The Future of Forensic Medicine. Cureus 14(8): e28376. DOI 10.7759/cureus.28376
About the Author
Vineeta Johri is a research scholar in the Department of Biotechnology, NIMS University, Jaipur, Rajasthan.
