AI-powered autopsies reshape forensic medicine with cultural and ethical advantages

Artificial intelligence and advanced medical imaging are poised to revolutionize forensic medicine, offering more accurate, non-invasive, and culturally sensitive diagnostic options for investigating causes of death. A systematic review, titled “Emerging Imaging Technologies in Forensic Medicine: A Systematic Review of Innovations, Ethical Challenges, and Future Directions”, published in Diagnostics in June 2025, delivers a sweeping analysis of how technologies such as virtual autopsy, multi-detector computed tomography (MDCT), and AI-driven diagnostic tools are reshaping the field.

Conducted by Feras Alafer of Jouf University, the study reviews evidence from 10 peer-reviewed articles, synthesizing advancements in forensic imaging with the operational, legal, and cultural challenges that hinder its wider adoption. Through thematic and narrative analysis, the review outlines the strategic shifts needed for global forensic institutions to integrate these technologies effectively.

How are AI and imaging transforming forensic investigations?

Advanced imaging technologies have expanded the toolkit of forensic medicine well beyond traditional autopsies. Tools such as MDCT, MRI, micro-CT, and 3D reconstruction now offer high-resolution, non-invasive visualizations of trauma, internal injuries, and cause-of-death markers. These technologies can preserve forensic evidence digitally, enable re-evaluation by other experts, and improve documentation quality for court proceedings.

Virtual autopsy, or “virtopsy”, is a standout innovation. By combining CT or MRI scans with 3D modeling, it allows forensic specialists to analyze internal body structures without invasive dissection. This proves particularly beneficial in cultures where traditional autopsies are discouraged on religious or ethical grounds. Beyond imaging hardware, AI algorithms are increasingly used to identify trauma patterns, estimate postmortem intervals, and automate evidence classification.

Machine learning tools offer the potential to reduce diagnostic subjectivity and accelerate time-sensitive forensic workflows. For example, algorithms trained on extensive forensic image datasets can identify subtle indicators of blunt force trauma or internal hemorrhaging with greater consistency than manual interpretation. The review highlights how these applications are already being piloted or implemented in high-resource forensic institutions.

What are the barriers to widespread adoption?

Despite the clinical promise, the study underscores that significant financial, operational, and legal obstacles hamper broader deployment of these technologies. The cost of acquiring, operating, and maintaining imaging equipment like CT and MRI scanners remains prohibitively high for many jurisdictions, especially in low- and middle-income countries. Even where the machines are available, a lack of trained personnel to operate and interpret the data creates further bottlenecks.

Operational challenges extend to the infrastructure required for secure data transmission and storage. Postmortem images, often containing highly sensitive and identifiable data, pose cybersecurity risks if not safeguarded by strong protocols. The absence of standardized guidelines across jurisdictions adds legal ambiguity to how forensic imaging findings are interpreted and accepted in court. In many countries, courts remain cautious about admitting digital imaging results unless the chain of custody, data integrity, and analytical methods are clearly defined and validated.

Ethical concerns also surface around the handling of deceased individuals’ digital remains. Without universal consent procedures and clear regulatory frameworks, the storage and cross-border sharing of postmortem imaging may violate privacy norms or local laws. Algorithmic bias in AI applications is another emerging issue. Models trained predominantly on datasets from specific regions may fail to generalize accurately to diverse populations, raising the risk of diagnostic errors or misinterpretation.

What strategic solutions and innovations are proposed?

The review presents a multi-pronged roadmap to address these barriers, beginning with the development of standardized forensic imaging protocols. Establishing clear operational and legal frameworks would enhance the scientific validity and evidentiary reliability of imaging findings. The adoption of secure blockchain technologies to track data provenance and chain-of-custody for digital evidence is proposed as a safeguard against data tampering and cyber intrusions.

Interdisciplinary collaboration is another key recommendation. Forensic pathologists, radiologists, anthropologists, and AI specialists must be trained to work in integrated teams. Current siloed practices, where radiologists lack forensic expertise and pathologists are unfamiliar with imaging modalities, limit the potential of these technologies. The review advocates for interprofessional training programs and updated curricula that blend medical imaging, legal procedure, and AI ethics.

Cultural adaptability is highlighted as a core advantage of virtual autopsy technologies. In societies where traditional autopsies are resisted for religious reasons, such as Islamic nations or some Jewish and Christian communities, virtual techniques offer a respectful alternative. These methods not only comply with cultural norms but also provide robust forensic evidence, opening avenues for global scientific collaboration. The study proposes establishing an international network, referred to as a “Global Virtual Forensic Network”, to share imaging data, diagnostic insights, and training programs across jurisdictions while preserving cultural sensitivities.

AI-driven tools continue to hold great promise but must be introduced responsibly. The review urges greater investment in explainable AI (XAI) models, which provide transparency on how algorithmic decisions are made, especially critical when forensic evidence is subject to legal scrutiny. It also encourages the creation of international, demographically diverse datasets to improve model robustness and fairness.