Artificial Intelligence in Oncology: Advances in Cancer Diagnosis, Genomic Profiling, and Treatment Planning
DOI:
https://doi.org/10.64229/6xvf1387Keywords:
Artificial intelligence, Cancer diagnosis and treatment, Precision oncology, Medical imaging and histopathology, Ethical and clinical challengesAbstract
Artificial intelligence (AI) has made significant advances in oncology, demonstrating excellent diagnostic accuracy in imaging, enhanced genetic interpretation, and decision-support capabilities for personalized therapy planning. Despite these advances, implementation in routine clinical practice remains limited and uneven. This review argues that the primary bottleneck is not algorithmic performance, but rather a persistent Clinical Embedding Gap, which disconnects model accuracy from real-world integration within clinical workflows, institutional infrastructure, incentive structures, and outcome-based validation frameworks. A structured narrative review was conducted. A systematic literature search was performed using PubMed/MEDLINE and Web of Science to identify relevant peer-reviewed literature published between 2015 and 2025. The findings suggest that AI systems are commonly performance-validated but insufficiently outcome-verified, which contributes to limited adoption. This gap must be bridged through workflow-native design, prospective clinical validation, interoperable digital infrastructure, and alignment of technological innovation with healthcare system readiness. Addressing these structural challenges is critical for advancing AI from experimental augmentation to integrated standard-of-care oncology practice.
References
[1]Ghufran MS, Soni P, Duddukuri GR. The global concern for cancer emergence and its prevention: A systematic unveiling of the present scenario, in Bioprospecting of tropical medicinal plants. Handbook of Cancer and Public Health. Cham: Springer Nature, 2023, 1429-1455. DOI: 10.1007/978-3-031-28780-0_60
[2]Feng X, Shu W, Li M, Li J, Xu J, He M. Pathogenomics for accurate diagnosis, treatment, prognosis of oncology: A cutting edge overview. Journal of Translational Medicine, 2024, 22(1), 131. DOI: 10.1186/s12967-024-04915-3
[3]Lee D, Yoon SN. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. International Journal of Environmental Research and Public Health, 2021, 18(1), 271. DOI: 10.3390/ijerph18010271
[4]Sebastian AM, Peter D. Artificial intelligence in cancer research: Trends, challenges and future directions. Life, 2022, 12(12), 1991. DOI: 10.3390/life12121991
[5]Fitzgerald RC, Antoniou AC, Fruk L, Rosenfeld N. The future of early cancer detection. Nature Medicine, 2022, 666-677. DOI: 10.1038/s41591-022-01746-x
[6]Chen YM, Hsiao TH, Lin CH, Fann YC. Unlocking precision medicine: Clinical applications of integrating health records, genetics, and immunology through artificial intelligence. Journal of Biomedical Science, 2025, 32(1), 16. DOI: 10.1186/s12929-024-01110-w
[7]Panagoulias DP, Tsihrintzis GA, Virvou M. Challenges in regulating and validating AI-driven healthcare. Artificial Intelligence-empowered Bio-medical Applications. Cham: Springer Nature, 2025, 135-152. DOI: 10.1007/978-3-031-90174-4_6
[8]Elemento O, Leslie C, Lundin J, Tourassi G. Artificial intelligence in cancer research, diagnosis and therapy. Nature Reviews Cancer, 2021, 21(12), 747-752. DOI: 10.1038/s41568-021-00399-1
[9]Taylor LA, Nong P, Platt J. Fifty years of trust research in health care: A synthetic review. The Milbank Quarterly, 2023, 101(1), 126-178. DOI: 10.1111/1468-0009.12598
[10]Calleja-Sanz G, Olivella-Nadal J, Solé-Parellada F. Technology forecasting: Recent trends and new methods. Research Methodology in Management and Industrial Engineering. Cham: Springer Nature, 2020, 45-69. DOI: 10.1007/978-3-030-40896-1_3
[11]Crosby D, Bhatia S, Brindle KM, Coussens LM, Dive C, Emberton M, et al. Early detection of cancer. Science, 2022, 375(6586), eaay9040. DOI: 10.1126/science.aay9040
[12]Jiang X, Hu Z, Wang S, Zhang Y. Deep learning for medical image-based cancer diagnosis. Cancers, 2023, 15(14), 3608. DOI: 10.3390/cancers15143608
[13]Mallum A, Mkhize T, Akudugu JM, Ngwa W, Vorster M. The role of positron emission tomography and computed tomographic (PET/CT) imaging for radiation therapy planning: A literature review. Diagnostics, 2022, 13(1), 53. DOI: 10.3390/diagnostics13010053
[14]Alzubaidi L, Zhang J, Humaidi AJ, Al-Dujaili A, Duan Y, Al-Shamma O, et al. Review of deep learning: Concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data, 2021, 8(1), 53. DOI: 10.1186/s40537-021-00444-8
[15]Davoodi P, Ezoji M, Sadeghnejad N. Classification of natural images inspired by the human visual system. Neurocomputing, 2023, 518, 60-69. DOI: 10.1016/j.neucom.2022.10.055
[16]McKinney SM, Sieniek M, Godbole V, Godwin J, Antropova N, Ashrafian H, et al. International evaluation of an AI system for breast cancer screening. Nature, 2020, 577(7788), 89-94. DOI: 10.1038/s41586-019-1799-6
[17]Grenier PA, Brun AL, Mellot F. The potential role of artificial intelligence in lung cancer screening using low-dose computed tomography. Diagnostics, 2022, 12(10), 2435. DOI: 10.3390/diagnostics12102435
[18]Homayounieh F, Digumarthy S, Ebrahimian S, Rueckel J, Hoppe BF, Sabel BO, et al. An artificial intelligence-based chest X-ray model on human nodule detection accuracy from a multicenter study. JAMA Network Open, 2021, 4(12), e2141096. DOI: 10.1001/jamanetworkopen.2021.41096
[19]Chaddad A, Tan G, Liang X, Hassan L, Rathore S, Desrosiers C, et al. Advancements in MRI-based radiomics and artificial intelligence for prostate cancer: A comprehensive review and future prospects. Cancers, 2023, 15, 3839. DOI: 10.3390/cancers15153839
[20]Bahadir CD, Omar M, Rosenthal J, Marchionni L, Liechty B, Pisapia DJ, et al. Artificial intelligence applications in histopathology. Nature Reviews Electrical Engineering, 2024. 1(2), 93-108. DOI: 10.1038/s44287-023-00012-7
[21]Shafi S, Parwani AV. Artificial intelligence in diagnostic pathology. Diagnostic Pathology, 2023, 18(1), 109. DOI: 10.1186/s13000-023-01375-z
[22]Singhal N, Soni S, Bonthu S, Chattopadhyay N, Samanta P, Joshi U, et al. A deep learning system for prostate cancer diagnosis and grading in whole slide images of core needle biopsies. Scientific Reports, 2022, 12(1), 3383. DOI: 10.1038/s41598-022-07217-0
[23]Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, Abedalthagafi M. Artificial intelligence in neuro-oncology: Advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. npj Precision Oncology, 2024, 8(1), 80. DOI: 10.1038/s41698-024-00575-0
[24]Romanò M. The meanings of prognosis: When and how to discuss it? Palliative Care in Cardiac Intensive Care Units. Cham: Springer International Publishing, 2021, 67-81. DOI: 10.1007/978-3-030-80112-0_4
[25]Teh BT, Fearon ER. 14-Genetic and epigenetic alterations in cancer. Abeloff’s Clinical Oncology (Sixth Edition), 2020, 209-224. e2. DOI: 10.1016/B978-0-323-47674-4.00014-1.
[26]Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, et al. Next-generation sequencing technology: Current trends and advancements. Biology, 2023, 12(7), 997. DOI: 10.3390/biology12070997
[27]Mukherjee A, Abraham S, Singh A, Balaji S, Mukunthan KS. From data to cure: A comprehensive exploration of multi-omics data analysis for targeted therapies. Molecular Biotechnology, 2025, 67(4), 1269-1289. DOI: 10.1007/s12033-024-01133-6
[28]Wei L, Niraula D, Gates EDH, Fu J, Luo Y, Nyflot MJ, et al. Artificial intelligence (AI) and machine learning (ML) in precision oncology: A review on enhancing discoverability through multiomics integration. British Journal of Radiology, 2023, 96(1150), 20230211. DOI: 10.1259/bjr.20230211
[29]Ben-Hamo R, Jacob Berger A, Gavert N, Miller M, Pines G, Oren R, et al. Predicting and affecting response to cancer therapy based on pathway-level biomarkers. Nature Communications, 2020, 11(1), 3296. DOI: 10.1038/s41467-020-17090-y
[30]Roy S, Kumar R, Mittal V, Gupta D. Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning. Scientific Reports, 2020, 10(1), 4113. DOI: 10.1038/s41598-020-60740-w
[31]Ali H. Artificial intelligence in multi-omics data integration: Advancing precision medicine, biomarker discovery and genomic-driven disease interventions. International Journal of Science and Research Archive, 2023, 8(1), 1012-1030. DOI: 10.30574/ijsra.2023.8.1.0189
[32]Yetgin A. Revolutionizing multi-omics analysis with artificial intelligence and data processing. Quantitative Biology, 2025, 13(3), e70002. DOI: 10.1002/qub2.70002
[33]Xiong X, Zheng LW, Ding Y, Chen YF, Cai YW, Wang LP, et al. Breast cancer: Pathogenesis and treatments. Signal Transduction and Targeted Therapy, 2025, 10(1), 49. DOI: 10.1038/s41392-024-02108-4
[34]Khazeeva G, Sablauskas K, van der Sanden B, Steyaert W, Kwint M, Rots D, et al. DeNovoCNN: A deep learning approach to de novo variant calling in next generation sequencing data. Nucleic Acids Research, 2022, 50(17), e97. DOI: 10.1093/nar/gkac511
[35]Kudłacik-Kramarczyk S, Kieres W, Przybyłowicz A, Ziejewska C, Marczyk J, Krzan M. Recent advances in micro-and nano-enhanced intravascular biosensors for real-time monitoring, early disease diagnosis, and drug therapy monitoring. Sensors, 2025, 25(15), 4855. DOI: 10.3390/s25154855
[36]Tsimberidou AM, Kahle M, Vo HH, Baysal MA, Johnson A, Meric-Bernstam F. Molecular tumour boards-current and future considerations for precision oncology. Nature Reviews Clinical Oncology, 2023, 20(12), 843-863. DOI: 10.1038/s41571-023-00824-4
[37]Tiwari A, Mishra S, Kuo TR. Current AI technologies in cancer diagnostics and treatment. Molecular Cancer, 2025, 24(1), 159. DOI: 10.1186/s12943-025-02369-9
[38]Kann BH, Hosny A, Aerts HJWL. Artificial intelligence for clinical oncology. Cancer Cell, 2021, 39(7), 916-927. DOI: 10.1016/j.ccell.2021.04.002
[39]Wang L, Chen X, Zhang L, Li L, Huang Y, Sun Y, et al. Artificial intelligence in clinical decision support systems for oncology. International Journal of Medical Sciences, 2023, 20(1), 79-86. DOI: 10.7150/ijms.77205
[40]Jie Z, Zhiying Z, Li L. A meta-analysis of Watson for Oncology in clinical application. Scientific Reports, 2021, 11(1), 5792. DOI: 10.1038/s41598-021-84973-5
[41]Kulavi S, Ghosh C, Saha M, Chatterjee S. One size does not fit all: An overview of personalized treatment in cancer. Journal of Pharmaceutical Research International, 2021, 33(28A), 87-103. DOI: 10.9734/JPRI/2021/v33i28A31513
[42]Mei T, Wang T, Zhou Q. Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients. Clinical and Experimental Medicine, 2024, 24(1), 60. DOI: 10.1007/s10238-024-01324-0
[43]Bonci EA, Bandura A, Dooley A, Erjan A, Gebreslase HW, Hategan M, et al. Artificial intelligence in NSCLC management for revolutionizing diagnosis, prognosis, and treatment optimization: A systematic review. Critical Reviews in Oncology/Hematology, 2025, 216, 104929. DOI: 10.1016/j.critrevonc.2025.104929
[44]Fahim YA, Hasani IW, Kabba S, Ragab WM. Artificial intelligence in healthcare and medicine: Clinical applications, therapeutic advances, and future perspectives. European Journal of Medical Research, 2025, 30(1), 848. DOI: 10.1186/s40001-025-03196-w
[45]Gardner SJ, Kim J, Chetty IJ. Modern radiation therapy planning and delivery. Hematology/Oncology Clinics of North America, 2019, 33(6), 947-962. DOI: 10.1016/j.hoc.2019.08.005
[46]Kalantar R, Lin G, Winfield JM, Messiou C, Lalondrelle S, Blackledge MD, et al. Automatic segmentation of pelvic cancers using deep learning: State-of-the-art approaches and challenges. Diagnostics, 2021, 11(11), 1964. DOI: 10.3390/diagnostics11111964
[47]Ng CKC. Performance of commercial deep learning-based auto-segmentation software for prostate cancer radiation therapy planning: A systematic review. Information, 2025, 16(3), 215. DOI: 10.3390/info16030215
[48]Garcıa S, Menghi C, Pelliccione P, Berger T, Wohlrab R. An architecture for decentralized, collaborative, and autonomous robots. 2018 IEEE International Conference on Software Architecture (ICSA). IEEE, 2018, 75-7509. DOI: 10.1109/ICSA.2018.00017
[49]Duchateau N, Puyol-Antón E, Ruijsink B, King A. AI and machine learning: The basics. AI and Big Data in Cardiology. Cham: Springer International Publishing, 2023, 11-33. DOI: 10.1007/978-3-031-05071-8_2
[50]Ghodke BS. The role of AI in enhancing healthcare quality and safety. International Journal of Innovative Research in Science, Engineering and Technology, 2024, 13(12), 20807-20815. DOI: 10.15680/IJIRSET.2024.1312200
[51]Kok Wah JN. AI-driven robotic surgery in oncology: Advancing precision, personalization, and patient outcomes. Journal of Robotic Surgery, 2025, 19(1), 382. DOI: 10.1007/s11701-025-02555-3
[52]Das P, Ganguly S, Margel S, Gedanken A. Tailor made magnetic nanolights: Fabrication to cancer theranostics applications. Nanoscale Advances, 2021, 3(24), 6762-6796. DOI: 10.1039/d1na00447f
[53]Fan X, Liu X, Xia Q, Chen G, Cheng J, Shi Z, et al. Advanced image-guidance and surgical-navigation techniques for real-time visualized surgery. Advanced Science, 2025, 12(41), e09294. DOI: 10.1002/advs.202509294
[54]Iftikhar M, Saqib M, Zareen M, Mumtaz H. Artificial intelligence: Revolutionizing robotic surgery: Review. Annals of Medicine and Surgery, 2024, 86(9), 5401-5409. DOI: 10.1097/MS9.0000000000002426
[55]Guni A, Varma P, Zhang J, Fehervari M, Ashrafian H. Artificial intelligence in surgery: The future is now. European Surgical Research, 2024, 65(1), 22-39. DOI: 10.1159/000536393
[56]Yangi K, On TJ, Xu Y, Gholami AS, Hong J, Reed AG, et al. Artificial intelligence integration in surgery through hand and instrument tracking: A systematic literature review. Frontiers in Surgery, 2025, 12, 1528362. DOI: 10.3389/fsurg.2025.1528362
[57]Kondylakis H, Axenie C, Kiran Bastola D, Katehakis DG, Kouroubali A, Kurz D, et al. Status and recommendations of technological and data-driven innovations in cancer care: Focus group study. Journal of Medical Internet Research, 2020, 22(12), e22034. DOI: 10.2196/22034
[58]Gentile F, Yaacoub JC, Gleave J, Fernandez M, Ton AT, Ban F, et al. Artificial intelligence-enabled virtual screening of ultra-large chemical libraries with deep docking. Nature Protocols, 2022, 17(3), 672-697. DOI: 10.1038/s41596-021-00659-2
[59]Carpenter KA, Huang X. Machine learning-based virtual screening and its applications to Alzheimer’s drug discovery: A review. Current Pharmaceutical Design, 2018, 24(28), 3347-3358. DOI: 10.2174/1381612824666180607124038
[60]Upadhyaya V. Predictive analytics in medical diagnosis. Intelligent Data Analytics for Bioinformatics and Biomedical Systems, 2024, 27-66. DOI: 10.1002/9781394270910.ch2
[61]Bhatia A, Kumar A, Kumar R, Jain V. Medications and the role of tailored healthcare. Smart Healthcare, Clinical Diagnostics, and Bioprinting Solutions for Modern Medicine. Hershey: IGI Global Scientific Publishing, 2025, 165-192. DOI: 10.4018/979-8-3373-0659-9.ch009
[62]Mango VL, Sun M, Wynn RT, Ha R. Should we ignore, follow, or biopsy? Impact of artificial intelligence decision support on breast ultrasound lesion assessment. American Journal of Roentgenology, 2020, 214(6), 1445-1452. DOI: 10.2214/AJR.19.21872
[63]Tsoupras G, Syed ZA. AI-driven decision support systems for early breast cancer detection: Adoption implications in healthcare contexts. Lund: Lund University, 2025.
[64]Badawy E, ElNaggar R, Soliman SAM, Elmesidy DS. Performance of AI-aided mammography in breast cancer diagnosis: Does breast density matter? Egyptian Journal of Radiology and Nuclear Medicine, 2023, 54(1), 178. DOI: 10.1186/s43055-023-01129-3
[65]Agrawal S, Vagha S. A comprehensive review of artificial intelligence in prostate cancer care: State-of-the-art diagnostic tools and future outlook. Cureus, 2024, 16(8), e66225. DOI: 10.7759/cureus.66225
[66]Nasir ES, Parvaiz A, Fraz MM. Nuclei and glands instance segmentation in histology images: A narrative review. Artificial Intelligence Review, 2023, 56(8), 7909-7964. DOI: 10.1007/s10462-022-10372-5
[67]Arita Y, Roest C, Kwee TC, Paudyal R, Lema-Dopico A, Fransen S, et al. Advancements in artificial intelligence for prostate cancer: Optimizing diagnosis, treatment, and prognostic assessment. Asian Journal of Urology, 2025, 12(4), 434-444. DOI: 10.1016/j.ajur.2024.12.001
[68]Xu Q, Zhai JC, Huo CQ, Li Y, Dong XJ, Li DF, et al., OncoPDSS: An evidence-based clinical decision support system for oncology pharmacotherapy at the individual level. BMC cancer, 2020, 20(1), 740. DOI: 10.1186/s12885-020-07221-5
[69]Magrabi F, Ammenwerth E, McNair JB, De Keizer NF, Hyppönen H, Nykänen P, et al. Artificial intelligence in clinical decision support: Challenges for evaluating AI and practical implications. Yearbook of Medical Informatics, 2019, 28(1), 128-134. DOI: 10.1055/s-0039-1677903
[70]Singh RP, Hom GL, Abramoff MD, Campbell JP, Chiang MF, on behalf of the AAO Task Force on Artificial Intelligence. Current challenges and barriers to real-world artificial intelligence adoption for the healthcare system, provider, and the patient. Translational Vision Science & Technology, 2020, 9(2), 45. DOI: 10.1167/tvst.9.2.45
[71]Yao S, Wang R, Qian K, Zhang Y. Real world study for the concordance between IBM Watson for Oncology and clinical practice in advanced non-small cell lung cancer patients at a lung cancer center in China. Thorac Cancer, 2020, 11(5), 1265-1270. DOI: 10.1111/1759-7714.13391
[72]Jiang B, Ozkara BB, Zhu G, Boothroyd D, Allen JW, Barboriak DP, et al. Assessing the performance of artificial intelligence models: Insights from the American Society of Functional Neuroradiology Artificial Intelligence Competition. AJNR American Journal of Neuroradiology, 2024, 45(9), 1276-1283. DOI: 10.3174/ajnr.A8317
[73]Ripatti AR. Screening diabetic retinopathy and age-related macular degeneration with artificial intelligence: New innovations for eye care. Oulu: Oulun ammattikorkeakoulu, 2025.
[74]Skevas C, de Olaguer NP, Lleó A, Thiwa D, Schroeter U, Lopes IV, et al. Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment. BMC Ophthalmology, 2024, 24(1), 51. DOI: 10.1186/s12886-024-03306-y
[75]Saravi B, Hassel F, Ülkümen S, Zink A, Shavlokhova V, Couillard-Despres S, et al. Artificial intelligence-driven prediction modeling and decision making in spine surgery using hybrid machine learning models. Journal of Personalized Medicine, 2022, 12(4), 509. DOI: 10.3390/jpm12040509
[76]Wang S, Liu J, Song L, Wen W, Huang J, Peng Y. Over-detection and over-surveillance in breast screening: Current status and the potential for artificial intelligence optimisation. Insights into Imaging, 2025, 16(1), 276. DOI: 10.1186/s13244-025-02160-w
[77]Xu B, Zhou F. The roles of cloud-based systems on the cancer-related studies: A systematic literature review. IEEE Access, 2022, 10, 64126-64145. DOI: 10.1109/ACCESS.2022.3181147
[78]Zhang X, Yang L, Liu C, Yuan X, Zhang Y. An artificial intelligence pipeline for hepatocellular carcinoma: From data to treatment recommendations. International Journal of General Medicine, 2025, 18, 3581-3595. DOI: 10.2147/IJGM.S529322
[79]Wah JNK. Revolutionizing surgery: AI and robotics for precision, risk reduction, and innovation. Journal of Robotic Surgery, 2025, 19(1), 47. DOI: 10.1007/s11701-024-02205-0
[80]Reva T, Reva T, Nizhenkovskyi O, Chkhalo O. A comparison study of artificial intelligence-driven no-code applications for drug discovery and development. ScienceRise: Pharmaceutical Science, 2024, 6(52), 80-89. DOI: 10.15587/2519-4852.2024.318920
[81]Tupasela A, Di Nucci E. Concordance as evidence in the Watson for Oncology decision-support system. Ai & Society, 2020, 35(4), 811-818. DOI: 10.1007/s00146-020-00945-9
[82]Subhan A, Manoharan G. Advancing cancer care through artificial intelligence: From innovative models to clinical decision-making and regulatory integration. Clinical Cancer Bulletin, 2025, 4(1), 23. DOI: 10.1007/s44272-025-00052-0
[83]Fatima G, Alhmadi H, Ali Mahdi A, Hadi N, Fedacko J, Magomedova A, et al. Transforming diagnostics: A comprehensive review of advances in digital pathology. Cureus, 2024, 16(10), e71890. DOI: 10.7759/cureus.71890
[84]Hamrouni J. Predictive modelling for clinical trial completion: Assessing the phase success-incorporating RAG techniques for predictive modelling of clinical trial outcomes. Universidade NOVA de Lisboa, 2024
[85]Cheng CH, Shi SS. Artificial intelligence in cancer: Applications, challenges, and future perspectives. Molecular Cancer, 2025, 24(1), 274. DOI: 10.1186/s12943-025-02450-3
[86]Srivastav AK, Singh A, Singh S, Rivers B, Lillard JW Jr, Singh R. Revolutionizing oncology through AI: Addressing cancer disparities by improving screening, treatment, and survival outcomes via integration of social determinants of health. Cancers, 2025, 17(17), 2866. DOI: 10.3390/cancers17172866
[87]Baweja B, Vats P, Kausik S, Singh J, Nema R, Yadav P. Challenges and limitations of computational methods in oncology. Advances in Cancer Detection, Prediction, and Prognosis Using Artificial Intelligence and Machine Learning. Singapore: Springer Nature Singapore, 2025, 307-324. DOI: 10.1007/978-981-96-9346-7_13
[88]Kaur P, Chand T, Rani S. Integration of artificial intelligence in laryngeal cancer diagnosis and prognosis: A comparative analysis bridging traditional medical practices with modern computational techniques. Archives of Computational Methods in Engineering, 2025, 1-37. DOI: 10.1007/s11831-025-10368-8
[89]Ramchandani R, Guo E, Biglou SG, Sabbah SG, Mostowy M, Mahiny D, et al. Representation and bias in artificial intelligence models for thyroid cancer: A systematic review. Thyroid, 2025, 35(12), 1391-1402. DOI: 10.1177/10507256251372175
[90]Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D, et al. A survey of methods for explaining black box models. ACM Computing Surveys, 2018, (5), 1-42. DOI: 10.1145/3236009
[91]Emma L. Explainable AI for high-stakes decision making in healthcare. Ladoke Akintola University of Technology, 2024.
[92]Rudin C, Chen C, Chen Z, Huang H, Semenova L, Zhong C. Interpretable machine learning: Fundamental principles and 10 grand challenges. Statistic Surveys, 2022, 16, 1-85. DOI: 10.1214/21-SS133
[93]Chandak T, Jayashree J, Vijayashree J. Trends and advancements of AI and XAI in drug discovery. Explainable AI (XAI) for sustainable development. Boca Raton, FL: CRC Press, 2024, 233-255.
[94]Dziedzic A, Issa J, Chaurasia A, Tanasiewicz M. Artificial intelligence and health-related data: The patient’s best interest and data ownership dilemma. Proceedings of the Institution of Mechanical Engineers, 2024, 238(10), 1023-1028. DOI: 10.1177/09544119241279630
[95]Norori N, Hu Q, Aellen FM, Faraci FD, Tzovara A. Addressing bias in big data and AI for health care: A call for open science. Patterns, 2021, 2(10), 100347. DOI: 10.1016/j.patter.2021.100347
[96]Gladwin O. Human rights in the age of AI: Establishing fairness and accountability through algorithmic governance. 2024. DOI: 10.13140/RG.2.2.13279.06567
[97]Mennella C, Maniscalco U, De Pietro G, Esposito M. Ethical and regulatory challenges of AI technologies in healthcare: A narrative review. Heliyon, 2024, 10(4), e26297. DOI: 10.1016/j.heliyon.2024.e26297
[98]Arvai N, Katonai G, Mesko B. Health care professionals’ concerns about medical AI and psychological barriers and strategies for successful implementation: Scoping review. Journal of Medical Internet Research, 2025, 27, e66986. DOI: 10.2196/66986
[99]Oualikene-Gonin W, Jaulent MC, Thierry JP, Oliveira-Martins S, Belgodère L, Maison P, et al. Artificial intelligence integration in the drug lifecycle and in regulatory science: Policy implications, challenges and opportunities. Frontiers in Pharmacology, 2024, 15, 1437167. DOI: 10.3389/fphar.2024.1437167
[100]Koc C, Özyurt F, Iantovics LB. Survey on latest advances in natural language processing applications of generative adversarial networks. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 2025, 15(1), e70004. DOI: 10.1002/widm.70004
[101]Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, et al. Using quantitative imaging for personalized medicine in pancreatic cancer: A review of radiomics and deep learning applications. Cancers, 2022, 14(7), 1654. DOI: 10.3390/cancers14071654
[102]Feretzakis G, Papaspyridis K, Gkoulalas-Divanis A, Verykios VS. Privacy-preserving techniques in generative AI and large language models: A narrative review. Information, 2024, 15(11), 697. DOI: 10.3390/info15110697
[103]Khan AOR, Islam SM, Sarkar A, Islam T, Paul R, Bari MS. Real-time predictive health monitoring using AI-driven wearable sensors: Enhancing early detection and personalized interventions in chronic disease management. International Journal for Multidisciplinary Research, 2024, 6(5), 1-21. DOI: 10.36948/ijfmr.2024.v06i05.28497
[104]Thacharodi A, Singh P, Meenatchi R, Tawfeeq Ahmed ZH, Kumar RRS, VN, et al. Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future-A comprehensive review. Health Care Science, 2024, 3(5), 329-349. DOI: 10.1002/hcs2.115
[105]Govindaraj M, Khan P, Krishnan R, Gnanasekaran C, Lawrence J. Revolutionizing healthcare: The transformative impact of artificial intelligence. Revolutionizing the healthcare sector with AI. Hershey: IGI Global Scientific Publishing, 2024, 54-78. DOI: 10.4018/979-8-3693-3731-8.ch003
[106]Tello M, Reich ES, Puckey J, Maff R, Garcia-Arce A, Bhattacharya BS, Feijoo F. Machine learning based forecast for the prediction of inpatient bed demand. BMC Medical Informatics and Decision Making, 2022, 22(1), 55. DOI: 10.1186/s12911-022-01787-9
[107]Areia M, Mori Y, Correale L, Repici A, Bretthauer M, Sharma P, et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: A modelling study. The Lancet Digital Health, 2022, 4(6), e436-e444. DOI: 10.1016/S2589-7500(22)00042-5
[108]Zhang DY, Venkat A, Khasawneh H, Sali R, Zhang V, Pei Z. Implementation of digital pathology and artificial intelligence in routine pathology practice. Laboratory Investigation, 2024, 104(9), 102111. DOI: 10.1016/j.labinv.2024.102111
[109]Balahur A, Jenet A, Hupont IT, Charisi V, Ganesh A, Griesinger CB, et al. Data quality requirements for inclusive, non-biased and trustworthy AI. 2022. DOI: 10.2760/365479
[110]Kalasampath K, Spoorthi KN, Sajeev S, Kuppa SS, Ajay K, Maruthamuthu A. A Literature review on applications of explainable artificial intelligence (XAI). IEEE Access, 2025, 13, 41111-41140. DOI: 10.1109/ACCESS.2025.3546681
[111]Greco L, Percannella G, Ritrovato P, Tortorella F, Vento M. Trends in IoT based solutions for health care: Moving AI to the edge. Pattern Recognition Letters, 2020, 135, 346-353. DOI: 10.1016/j.patrec.2020.05.016
[112]Wang Z, Davidsen TM, Kuffel GR, Addepalli K, Bell A, Casas-Silva E, et al. NCI cancer research data commons: Resources to share key cancer data. Cancer Research, 2024, 84(9), 1388-1395. DOI: 10.1158/0008-5472.CAN-23-2468
[113]Garcia-Saiso S, Marti M, Pesce K, Luciani S, Mujica O, Hennis A, et al. Artificial intelligence as a potential catalyst to a more equitable cancer care. JMIR Cancer, 2024, 10, e57276. DOI: 10.2196/57276
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Copyright (c) 2026 Imran Khan Yousafzai, Aqsa Mehreen, Noor Fatima, Hawaida Ahmad, Nadia Noreen, Khadija Tariq (Author)
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