Artificial Intelligence in Forensic Medicine

Abstract

The use of Artificial Intelligence (AI) in forensic medicine is transforming the field by improving how forensic evidence is analyzed, interpreted, and applied. This review looks at the history, current uses, and future possibilities of AI in forensic investigations, focusing on areas like medical imaging, biometric identification, and digital forensics. It highlights the benefits of AI, such as increased accuracy, efficiency, and the ability to automate tasks like post-mortem imaging, DNA profiling, and toxicology analysis. However, there are still significant challenges, such as biases in algorithms, a lack of transparency, and the \"black box\" nature of deep learning models, which raise ethical and legal concerns. Another issue is whether AI-generated evidence can be reliably used in court. To overcome these challenges, the review suggests developing more transparent and explainable AI models, as well as creating strong legal and ethical guidelines. The goal of this review is to provide a clear understanding of both the potential and limitations of AI in forensic medicine, encouraging collaboration between technologists, forensic experts, and legal professionals to ensure AI is used responsibly.

Introduction

Forensic medicine is vital in legal investigations, helping identify victims, determine causes of death, and support criminal justice. Recently, artificial intelligence (AI) has emerged as a transformative tool in this field, enhancing accuracy, efficiency, and the ability to process complex forensic data.

AI Applications in Forensics:
AI is used in:

• Medical imaging (e.g., CT, MRI analysis)

• Biometric identification (facial recognition, fingerprint, DNA profiling)

• Digital forensics (electronic evidence analysis)

• Toxicology (chemical data analysis)

• Crime scene reconstruction
  Techniques like machine learning, deep learning, neural networks, and data analytics help automate and improve evidence interpretation, reduce human error, and accelerate investigations.

Historical Development:

• 1970s–80s: Expert systems used in pathology and toxicology

• 2000s: Machine learning for fingerprint and DNA analysis

• 2010s: Deep learning enabled better image analysis

• 2020s–Present: Advanced AI in post-mortem imaging, biometrics, and digital evidence

Benefits:

• Greater accuracy and speed

• Automated, objective analysis

• Enhanced capabilities in imaging, toxicology, anthropology, and document analysis

• Virtual autopsies and advanced crime predictions

Challenges & Limitations:

• Ethical and legal concerns: AI decisions may lack transparency (black box issue)

• Algorithmic bias: Risk of discrimination and wrongful convictions

• Data privacy: Especially when handling sensitive or personal data

• Over-reliance: Experts may defer too much to AI systems

• Legal admissibility: AI-generated evidence must be explainable, validated, and used under existing legal frameworks (e.g., Indian Evidence Act, IT Act)

Legal Context in India:

• Section 45 (Indian Evidence Act): Allows expert opinion

• Section 65B: Governs electronic evidence

• Challenges include lack of specific standards, privacy concerns, and the need for AI interpretability in court.

Future Directions:

• Develop transparent and explainable AI

• Improve datasets and reduce biases

• Foster collaboration among forensic, legal, and AI experts

• Establish legal and regulatory frameworks for AI-based forensic tools

Conclusion

The use of Artificial Intelligence (AI) in forensic medicine has led to major improvements, making forensic investigations more accurate, efficient, and wide-ranging. AI is being used in areas like medical imaging, biometric identification, and digital forensics, where it can transform how evidence is analyzed, automate routine tasks, and offer new insights. However, this fast progress also brings important challenges. Problems like biases in AI algorithms, lack of transparency, and the \"black box\" nature of many AI models create risks for using AI-generated evidence fairly and ethically in court. Additionally, clear legal guidelines are needed to determine how AI evidence can be accepted and understood in legal settings. For AI to be successfully used in forensic medicine, experts in technology, forensics, law, and policy must work together to solve these issues and ensure that AI tools are responsibly developed and applied. With this collaboration, AI can greatly improve forensic science while maintaining justice and fairness. Artificial intelligence (AI) has the power to transform forensic medicine by making investigations faster, more accurate, and more efficient, from analyzing crime scenes to identifying people through biometrics and recognizing patterns. However, issues like reliability, transparency, and the potential for bias in AI systems, along with ethical concerns like privacy and data security, need to be addressed to ensure its proper use and acceptance in court. Going forward, experts from different fields must work together to create AI models that are easy to understand and develop legal and ethical guidelines for its safe and effective use in forensic work.

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Copyright © 2025 Komal Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.