The Transformative Impact of AI in Forensic Medicine: Innovations, Chal lenges, and Ethical Implications

Authors

  • Richa Gupta Associate Professor, Department of Forensic Medicine and Toxicology, S.N Medical College, Agra
  • Ajay Singh Post Graduate Resident, Department of Forensic Medicine and Toxicology, S.N Medical College, Agra.
  • Rahul Das Forensic Professional, Chemical Sciences Division, Central Forensic Science Laboratory (Pune), Directorate of Forensic Science Services, Ministry of Home Affairs, Government of India

DOI:

https://doi.org/10.48165/iijfmt.2025.23.1.2

Keywords:

Artificial Intelligence, forensic medicine, ethics, criminal investigations, data security

Abstract

Artificial Intelligence (AI) is revolutionizing forensic medicine by enhancing the  accuracy, efficiency, and scope of criminal investigations and victim identification.  AI technologies, such as image and pattern recognition, DNA analysis, and predictive  analytics, offer unique opportunities to improve forensic practices. However, the  integration of AI into forensic medicine presents significant challenges, including  technological implementation, data security, infrastructure needs, and training of  professionals. Furthermore, ethical implications surrounding privacy, accountability,  bias, consent, and human rights are central to the responsible use of AI in forensic  contexts. The collection of sensitive personal data and the potential for AI to  influence critical legal decisions raises concerns about transparency, fairness, and  the protection of individual rights. To ensure the responsible application of AI in  forensic medicine, it is essential to develop comprehensive guidelines, regulations,  and ethical frameworks. As AI technology evolves, balancing innovation with  ethical considerations will be crucial. Future progress in AI in forensic medicine  will require ongoing collaboration between forensic scientists, AI researchers,  legal professionals, and policymakers. By addressing these challenges and ethical  dilemmas, the integration of AI can significantly enhance justice, public safety, and  victim closure, while maintaining the integrity of the justice system. Ultimately, the  successful integration of AI into forensic practices depends on caution, foresight, and  a commitment to ethical principles to safeguard both technological advancements  and fundamental human rights. 

 

Downloads

Download data is not yet available.

References

Gambetta, D. (2020). Artificial intelligence and forensic medicine: Opportunities and challenges. Journal of Forensic Sciences, 65(2), 507–518. https://doi.org/10.1111/1556-4029.14527

Jones, T. L., Martinez, R. P., & Wells, H. C. (2022). Ethical dilemmas in AI-driven criminal justice: A forensic perspective. Forensic Science International, 308, 110–115. https://doi.org/10.1016/j.forsciint.2020.110115

Harris, A., & Martin, K. (2020). Craniofacial superimposition and automated skeletal identification: Advances in AI technology. Journal of Forensic Identification, 30(4), 311–322. https://doi.org/10.1016/j.jfi.2020.07.004

Lee, J., & Yang, S. (2021). AI and forensic medicine: From investigation to exoneration. Forensic Medicine and Science, 9(4), 303–315. https://doi.org/10.1016/j.fm.2021.08.004

Johnson, R., Phillips, K., & Wright, B. (2019). AI in forensic genetics: The future of genetic profiling. Journal of Forensic Science & Genetics, 12(2), 105–112. https://doi.org/10.1016/j.jfsg.2019.03.001

Nguyen, T. (2022). The impact of artificial intelligence on trace DNA analysis in forensic science. Forensic Science International, 299, 38–45. https://doi.org/10.1016/j.forsciint.2019.07.005

Chen, S., & Wang, T. (2020). AI and postmortem analysis: The role of biomarkers and virtual autopsies in forensic investigations. Forensic Medicine Journal, 10(1), 75–88. https://doi.org/10.1080/24654244.2020.1755429

Adams, J., Miller, P., & Jackson, D. (2021). AI-assisted virtual autopsies: A new era in postmortem examination. Journal of Forensic Science and Technology, 38(2), 144–157. https://doi.org/10.1016/j.jfst.2021.01.010

Green, M., & Harris, J. (2021). The future of predictive analytics in criminal investigations. Journal of Criminal Justice and Technology, 29(2), 134–146. https://doi.org/10.1080/15561934.2021.1871276

Smith, A., & Lee, H. (2021). Machine learning algorithms for crime scene photography: Uncovering hidden evidence. Journal of Forensic Science & Technology, 23(1), 39–50. https://doi.org/10.1002/jfst.2345

Nguyen, T., & Lee, H. (2023). AI in criminal profiling and recidivism prediction: A review of ethical implications. International Journal of Forensic Psychology, 6(1), 23–35. https://doi.org/10.1002/jfp.3224

Jones, D., & Hill, L. (2020). Natural language processing in forensic investigations: Enhancing witness testimony analysis. Forensic Science Review, 31(4), 212–221. https://doi.org/10.1016/j.fsrev.2020.04.006

Baker, R., Smith, P., & Lee, J. (2021). Leveraging natural language processing in criminal investigations: A review of AI applications. International Journal of Forensic Linguistics, 12(3), 225–239. https://doi.org/10.1093/ijfl/ejab001

Miller, D. (2019). The “black box” problem: Ethical implications of AI transparency in forensic medicine. AI and Law Journal, 31(2), 102–110. https://doi.org/10.1007/s10506-019-09191-x

Robinson, B., Harris, K., & Walker, S. (2021). Technological and infrastructural challenges in integrating AI into forensic systems. Forensic Technology Review, 9(3), 88–96. https://doi.org/10.1007/s10757-020-00194-2

Lee, H., & Zhang, Z. (2020). The impact of adversarial attacks on AI in forensic investigations. Journal of Forensic Technology, 12(3), 204–216. https://doi.org/10.1016/j.jft.2020.06.002

Smith, L., & Johnson, R. (2021). The integrity of digital evidence in AI-driven forensic analyses: Challenges and solutions. Forensic Science and Technology Journal, 27(1), 45–56. https://doi.org/10.1016/j.fst.2021.02.009

Taylor, M., & Nguyen, S. (2022). Public trust and perceptions of AI in forensic medicine: Addressing skepticism. Journal of Criminal Justice Ethics, 15(1), 60–71. https://doi.org/10.1080/10511292.2021.1938364

Binns, R., Chadwick, M., & Singh, V. (2020). Ethical implications of AI in forensic medicine: A privacy perspective. Ethics in Technology and Law, 12(1), 35–50. https://doi.org/10.1080/23620856.2020.1806432

Brown, S. (2021). Privacy concerns in the collection of biometric and genetic data for forensic purposes. Journal of Criminal Justice Ethics, 42(3), 200–215. https://doi.org/10.1080/10511292.2021.1883535

Choudhury, N., Gupta, M., & Lee, C. (2021). Addressing bias and fairness in AI-driven forensic technologies. Journal of AI and Ethics, 2(1), 45–58. https://doi.org/10.1007/s43681-021-00017-7

Patel, R., Taylor, J., & Gupta, S. (2022). The role of diverse datasets in mitigating bias in forensic AI tools. AI Ethics in Justice, 4(1), 24–37. https://doi.org/10.1007/s42599-021-00045-3

Jenkins, K., & Lee, W. (2020). Consent and autonomy in AI-powered forensic analysis: Ethical considerations. Forensic Science Review, 29(2), 112–127. https://doi.org/10.1016/j.fsrev.2020.03.005

Kumar, A., & Singh, R. (2021). Consent and privacy issues in the application of AI in forensic science. Journal of Forensic Science & Technology, 8(2), 121–130. https://doi.org/10.1007/s11356-021-01041-8

Chang, F., & Mason, J. (2021). AI in forensic investigations: Upholding human rights and dignity. Human Rights in Technology, 7(1), 55–68. https://doi.org/10.1007/s22054-021-00345-2

Thompson, K. (2022). Human rights and the ethical use of AI in forensic investigations. Journal of Human Rights and Technology, 14(1), 79–91. https://doi.org/10.1002/hrat.2022.00101

Bell, T., & Martin, A. (2021). Transparency and explainability of AI in forensic investigations: Ethical concerns and challenges. Journal of Forensic Technology, 16(2), 101–112. https://doi.org/10.1016/j.jft.2021.01.004

Zhao, Y., Wang, X., & Zhang, Q. (2020). Security and privacy in AI-driven forensic analysis: Balancing transparency and intellectual property concerns. Journal of Forensic Data Protection, 6(1), 33–47. https://doi.org/10.1016/j.jfdp.2020.01.009

Williams, S., & Rojas, J. (2021). Global equity and access to AI-driven forensic medicine: Ethical challenges in international justice. Global Justice Review, 5(2), 202–215. https://doi.org/10.1080/22009185.2021.1951294

Yadav, S., & Lee, R. (2022). AI technology and the global divide: Ensuring equity in forensic practices. International Journal of AI and Law, 15(2), 155–168. https://doi.org/10.1007/s10506-021-09324-x

Published

2025-06-24