Adli Bilimlerde Yapay Zekâ Uygulamaları: Yöntemler, Performans, Doğrulama ve Medikolegal Etkiler Üzerine Sistematik Derleme

Jain AK, Feng J. Latent fingerprint matching. IEEE Trans Pattern Anal Mach Intell. 2011;33(1):88-100. https://doi.org/10.1109/TPAMI.2010.59

Moretti TR, Just RS, Kehl SC, Willis KD, Buckleton JS, Bright JA, et al. Internal validation of STRmix™ for the interpretation of single source and mixed DNA profiles. Forensic Sci Int Genet. 2017;29:126-144. https://doi.org/10.1016/j.fsigen.2017.04.004

Garfinkel SL. Digital forensics research: the next 10 years. Digit Investig. 2010;7 Suppl:S64-S73. https://doi.org/10.1016/j.diin.2010.05.009

Cao K, Jain AK. Automated latent fingerprint recognition. IEEE Trans Pattern Anal Mach Intell. 2019;41(4):788-800. https://doi.org/10.1109/TPAMI.2018.2818162

Grother PJ, Ngan M, Hanaoka K. Face recognition vendor test (FRVT) part 3: demographic effects. NIST Interagency Report 8280. 2019. https://doi.org/10.6028/NIST.IR.8280

Verdoliva L. Media forensics and deepfakes: an overview. IEEE J Sel Top Signal Process. 2020;14(5):910-932. https://doi.org/10.1109/JSTSP.2020.3002101

Zimmermann N, Sieberth T, Dobay A. Automated wound segmentation and classification of seven common injuries in forensic medicine. Forensic Sci Med Pathol. 2024;20(2):443-451. https://doi.org/10.1007/s12024-023-00668-5

Drozdowski P, Rathgeb C, Dantcheva A, Damer N, Busch C. Demographic bias in biometrics: a survey on an emerging challenge. IEEE Trans Technol Soc. 2020;1(2):89-103. https://doi.org/10.1109/TTS.2020.2992344

Phillips PJ, Jiang F, Narvekar A, Ayyad J, O'Toole AJ. An other-race effect for face recognition algorithms. NIST Interagency Report 7666. 2010. https://doi.org/10.6028/NIST.IR.7666

Adamowicz MS, Rambo TN, Clarke JL. Internal validation of MaSTR™ probabilistic genotyping software for the interpretation of 2-5 person mixed DNA profiles. Genes (Basel). 2022;13(8):1429. https://doi.org/10.3390/genes13081429

Bleka Ø, Storvik G, Gill P. EuroForMix: an open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Sci Int Genet. 2016;21:35-44. https://doi.org/10.1016/j.fsigen.2015.11.008

Bright JA, Richards R, Kruijver M, Kelly H, McGovern C, Magee A, et al. Internal validation of STRmix™: a multi-laboratory response to PCAST. Forensic Sci Int Genet. 2018;34:11-24. https://doi.org/10.1016/j.fsigen.2018.01.003

Casey E, Nelson A, Hyde J. Standardization of file recovery classification and authentication. Digit Investig. 2019;31:100873. https://doi.org/10.1016/j.diin.2019.06.004

Mirsky Y, Lee W. The creation and detection of deepfakes: a survey. ACM Comput Surv. 2021;54(1):1-41. https://doi.org/10.1145/3425780

Nataraj L, Karthikeyan S, Jacob G, Manjunath BS. Malware images: visualization and automatic classification. In: Proceedings of the 8th International Symposium on Visualization for Cyber Security; 2011. p. 4:1-4:7. https://doi.org/10.1145/2016904.2016908

Dobay A, Ford J, Decker SJ, Ampanozi G, Franckenberg S, Affolter R, et al. Potential use of deep learning techniques for postmortem imaging. Forensic Sci Med Pathol. 2020;16(4):671-679. https://doi.org/10.1007/s12024-020-00307-3

Fehr J, Kurmann A, Handschin P, Garyfalia A, Franckenberg S, Golomingi R, et al. Automated volumetric estimation of six basic thoracic and abdominal organs in postmortem computed tomography data using deep learning techniques. Forensic Imaging. 2025;42:200642. https://doi.org/10.1016/j.fri.2025.200642

Cheng J, Schmidt C, Wilson A, Wang Z, Hao W, Pantanowitz J, et al. Artificial intelligence for human gunshot wound classification. J Pathol Inform. 2023;15:100361. https://doi.org/10.1016/j.jpi.2023.100361

Lopez-Melia M, Magnin V, Schranz S, Andrearczyk V, Depeursinge A, Marchand-Maillet S, et al. Automatic rib fracture detection on postmortem CT data using deep learning. Int J Legal Med. 2026;140(2):857-866. https://doi.org/10.1007/s00414-025-03669-x

European Network of Forensic Science Institutes (ENFSI). Guideline for evaluative reporting in forensic science: strengthening the evaluation of forensic results across Europe (STEOFRAE). Wiesbaden: ENFSI; 2015.

Morrison GS. What should a forensic practitioner's likelihood ratio be? II. Sci Justice. 2017;57(6):472-476. https://doi.org/10.1016/j.scijus.2017.08.004

Samek W, Wiegand T, Müller KR. Explainable artificial intelligence: understanding, visualizing and interpreting deep learning models. arXiv. 2017. https://doi.org/10.48550/arXiv.1708.08296

Cheng K, Bright JA, Kelly H, Liu YY, Lin MH, Kruijver M, et al. Developmental validation of STRmix™ NGS, a probabilistic genotyping tool for the interpretation of autosomal STRs from forensic profiles generated using NGS. Forensic Sci Int Genet. 2023;62:102804. https://doi.org/10.1016/j.fsigen.2022.102804

Huffman K, Ballantyne J. Validation of probabilistic genotyping software for single cell STR analysis. Genes (Basel). 2023;14(3):674. https://doi.org/10.3390/genes14030674

Meuwly D, Ramos D, Haraksim R. A guideline for the validation of likelihood ratio methods used for forensic evidence evaluation. Forensic Sci Int. 2017;276:142-153. https://doi.org/10.1016/j.forsciint.2016.03.048

International Organization for Standardization. ISO/IEC 17025:2017: general requirements for the competence of testing and calibration laboratories. Geneva: ISO; 2017.

Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole-slide images. Nat Med. 2019;25(8):1301-1309. https://doi.org/10.1038/s41591-019-0508-1

Kayser M. Forensic DNA phenotyping: predicting human appearance from crime scene material for investigative purposes. Forensic Sci Int Genet. 2015;18:33-48. https://doi.org/10.1016/j.fsigen.2015.02.003