Skip to content
2000
Volume 20, Issue 3
  • ISSN: 1574-8928
  • E-ISSN: 2212-3970

Abstract

An aberrant increase in cancer incidences has demanded extreme attention globally despite advancements in diagnostic and management strategies. The high mortality rate is concerning, and tumour heterogeneity at the genetic, phenotypic, and pathological levels exacerbates the problem. In this context, lack of early diagnostic techniques and therapeutic resistance to drugs, sole awareness among the public, coupled with the unavailability of these modern technologies in developing and low-income countries, negatively impact cancer management. One of the prime necessities of the world today is the enhancement of early detection of cancers. Several independent studies have shown that screening individuals for cancer can improve patient survival but are bogged down by risk classification and major problems in patient selection. Artificial intelligence (AI) has significantly advanced the field of oncology, addressing various medical challenges, particularly in cancer management. Leveraging extensive medical datasets and innovative computational technologies, AI, especially through deep learning (DL), has found applications across multiple facets of oncology research. These applications range from early cancer detection, diagnosis, classification, and grading, molecular characterization of tumours, prediction of patient outcomes and treatment responses, personalized treatment, and novel anti-cancer drug discovery. Over the past decade, AI/ML has emerged as a valuable tool in cancer prognosis, risk assessment, and treatment selection for cancer patients. Several patents have been and are being filed and granted. Some of those inventions were explored and are being explored in clinical settings as well. In this review, we will discuss the current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management. We are optimistic about the potential of AI/ML in improving outcomes for cancer and the need for further research and development in this field.

Loading

Article metrics loading...

/content/journals/pra/10.2174/0115748928361472250123105507
2025-02-03
2025-10-27
Loading full text...

Full text loading...

References

  1. LynchS.M. HeeranA.B. BurkeC. Precision oncology, artificial intelligence, and novel therapeutic advancements in the diagnosis, prevention, and treatment of cancer: Highlights from the 59th Irish Association for Cancer Research (IACR) Annual Conference.Cancers20241611198910.3390/cancers16111989
    [Google Scholar]
  2. ChenS. CaoZ. PrettnerK. Estimates and projections of the global economic cost of 29 cancers in 204 countries and territories from 2020 to 2050.JAMA Oncol.20239446547210.1001/jamaoncol.2022.7826 36821107
    [Google Scholar]
  3. LevineA.B. SchlosserC. GrewalJ. CoopeR. JonesS.J.M. YipS. Rise of the machines: Advances in deep learning for cancer diagnosis.Trends Cancer20195315716910.1016/j.trecan.2019.02.002 30898263
    [Google Scholar]
  4. BhardwajA. KishoreS. PandeyD.K. Artificial intelligence in biological sciences.Life2022129143010.3390/life12091430 36143468
    [Google Scholar]
  5. AlharbiF. VakanskiA. Machine learning methods for cancer classification using gene expression data: A review.Bioengineering202310217310.3390/bioengineering10020173 36829667
    [Google Scholar]
  6. SufyanM. ShokatZ. AshfaqU.A. Artificial intelligence in cancer diagnosis and therapy: Current status and future perspective.Comput. Biol. Med.202316510735610.1016/j.compbiomed.2023.107356 37688994
    [Google Scholar]
  7. XuM. OuyangY. YuanZ. Deep learning aided neuroimaging and brain regulation.Sensors20232311499310.3390/s23114993 37299724
    [Google Scholar]
  8. IqbalM.J. JavedZ. SadiaH. Clinical applications of artificial intelligence and machine learning in cancer diagnosis: Looking into the future.Cancer Cell Int.202121127010.1186/s12935‑021‑01981‑1 34020642
    [Google Scholar]
  9. FitzgeraldJ. HigginsD. Mazo VargasC. Future of biomarker evaluation in the realm of artificial intelligence algorithms: Application in improved therapeutic stratification of patients with breast and prostate cancer.J. Clin. Pathol.202174742943410.1136/jclinpath‑2020‑207351 34117103
    [Google Scholar]
  10. IbrahimA. GambleP. JaroensriR. Artificial intelligence in digital breast pathology: Techniques and applications.Breast20204926727310.1016/j.breast.2019.12.007 31935669
    [Google Scholar]
  11. JonasD.E. ReulandD.S. ReddyS.M. Screening for lung cancer with low-dose computed tomography: Updated evidence report and systematic review for the us preventive services task force.JAMA20213251097198710.1001/jama.2021.0377 33687468
    [Google Scholar]
  12. WaldmannE. KammerlanderA.A. GesslI. Association of adenoma detection rate and adenoma characteristics with colorectal cancer mortality after screening colonoscopy.Clin. Gastroenterol. Hepatol.20211991890189810.1016/j.cgh.2021.04.023 33878471
    [Google Scholar]
  13. WentzensenN. LahrmannB. ClarkeM.A. Accuracy and efficiency of deep-learning-based automation of dual stain cytology in cervical cancer screening.J. Natl. Cancer Inst.20211131727910.1093/jnci/djaa066 32584382
    [Google Scholar]
  14. LvY. WeiY. XuK. 3D deep learning versus the current methods for predicting tumor invasiveness of lung adenocarcinoma based on high-resolution computed tomography images.Front. Oncol.20221299587010.3389/fonc.2022.995870 36338695
    [Google Scholar]
  15. HungS.C. WangY.T. TsengM.H. An interpretable three-dimensional artificial intelligence model for computer-aided diagnosis of lung nodules in computed tomography images.Cancers20231518465510.3390/cancers15184655 37760624
    [Google Scholar]
  16. TaylorC.R. MongaN. JohnsonC. HawleyJ.R. PatelM. Artificial intelligence applications in breast imaging: Current status and future directions.Diagnostics20231312204110.3390/diagnostics13122041 37370936
    [Google Scholar]
  17. SarkerI.H. Deep learning: A comprehensive overview on techniques, taxonomy, applications and research directions.SN Comput. Sci.20212642010.1007/s42979‑021‑00815‑1 34426802
    [Google Scholar]
  18. AlowaisS.A. AlghamdiS.S. AlsuhebanyN. Revolutionizing healthcare: The role of artificial intelligence in clinical practice.BMC Med. Educ.202323168910.1186/s12909‑023‑04698‑z 37740191
    [Google Scholar]
  19. JonesN. AI now beats humans at basic tasks — New benchmarks are needed, says major report.Nature2024628800970070110.1038/d41586‑024‑01087‑4 38622298
    [Google Scholar]
  20. AlamM.R. Abdul-GhafarJ. YimK. Recent applications of artificial intelligence from histopathologic image-based prediction of microsatellite instability in solid cancers: A systematic review.Cancers20221411259010.3390/cancers14112590 35681570
    [Google Scholar]
  21. YuanZ. XuT. CaiJ. Development and validation of an image-based deep learning algorithm for detection of synchronous peritoneal carcinomatosis in colorectal cancer.Ann. Surg.20222754e645e65110.1097/SLA.0000000000004229 32694449
    [Google Scholar]
  22. KeL. DengY. XiaW. Development of a self-constrained 3D DenseNet model in automatic detection and segmentation of nasopharyngeal carcinoma using magnetic resonance images.Oral Oncol.202011010486210.1016/j.oraloncology.2020.104862 32615440
    [Google Scholar]
  23. LuoH. XuG. LiC. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: A multicentre, case-control, diagnostic study.Lancet Oncol.201920121645165410.1016/S1470‑2045(19)30637‑0 31591062
    [Google Scholar]
  24. BultenW. PinckaersH. van BovenH. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: A diagnostic study.Lancet Oncol.202021223324110.1016/S1470‑2045(19)30739‑9 31926805
    [Google Scholar]
  25. ZhouQ. ZhouZ. ChenC. Grading of hepatocellular carcinoma using 3D SE-DenseNet in dynamic enhanced MR images.Comput. Biol. Med.2019107475710.1016/j.compbiomed.2019.01.026 30776671
    [Google Scholar]
  26. LiuB. ChiW. LiX. Evolving the pulmonary nodules diagnosis from classical approaches to deep learning-aided decision support: Three decades’ development course and future prospect.J. Cancer Res. Clin. Oncol.2020146115318510.1007/s00432‑019‑03098‑5 31786740
    [Google Scholar]
  27. KhenedM. KoriA. RajkumarH. KrishnamurthiG. SrinivasanB. A generalized deep learning framework for whole-slide image segmentation and analysis.Sci. Rep.20211111157910.1038/s41598‑021‑90444‑8 34078928
    [Google Scholar]
  28. ZhangY. YangZ. ChenR. Histopathology images-based deep learning prediction of prognosis and therapeutic response in small cell lung cancer.NPJ Digit. Med.2024711510.1038/s41746‑024‑01003‑0 38238410
    [Google Scholar]
  29. KosarajuS. ParkJ. LeeH. YangJ.W. KangM. Deep learning-based framework for slide-based histopathological image analysis.Sci. Rep.20221211907510.1038/s41598‑022‑23166‑0 36351997
    [Google Scholar]
  30. El NahhasO.S.M. LoefflerC.M.L. CarreroZ.I. Regression-based deep-learning predicts molecular biomarkers from pathology slides.Nat. Commun.2024151125310.1038/s41467‑024‑45589‑1 38341402
    [Google Scholar]
  31. SharmaA. LysenkoA. JiaS. BoroevichK.A. TsunodaT. Advances in AI and machine learning for predictive medicine.J. Hum. Genet.20246948749710.1038/s10038‑024‑01231‑y
    [Google Scholar]
  32. ChengC.Y. LiY. VaralaK. Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.Nat. Commun.2021121562710.1038/s41467‑021‑25893‑w 34561450
    [Google Scholar]
  33. YamashitaR. LongJ. LongacreT. Deep learning model for the prediction of microsatellite instability in colorectal cancer: A diagnostic study.Lancet Oncol.202122113214110.1016/S1470‑2045(20)30535‑0 33387492
    [Google Scholar]
  34. HyndsR.E. FreseK.K. PearceD.R. GrönroosE. DiveC. SwantonC. Progress towards non-small-cell lung cancer models that represent clinical evolutionary trajectories.Open Biol.202111120024710.1098/rsob.200247 33435818
    [Google Scholar]
  35. MaY. LiJ. XiongC. SunX. ShenT. Development of a prognostic model for NSCLC based on differential genes in tumour stem cells.Sci. Rep.20241412093810.1038/s41598‑024‑71317‑2 39251710
    [Google Scholar]
  36. MuW. JiangL. ZhangJ. Non-invasive decision support for NSCLC treatment using PET/CT radiomics.Nat. Commun.2020111522810.1038/s41467‑020‑19116‑x 33067442
    [Google Scholar]
  37. ShboulZ.A. ChenJ.M. IftekharuddinK. Prediction of molecular mutations in diffuse low-grade gliomas using MR imaging features.Sci. Rep.2020101371110.1038/s41598‑020‑60550‑0 32111869
    [Google Scholar]
  38. BrigantiG. Le MoineO. Artificial intelligence in medicine: Today and tomorrow.Front. Med.202072710.3389/fmed.2020.00027 32118012
    [Google Scholar]
  39. RudnickaZ. PręgowskaA. GlądysK. PerkinsM. ProniewskaK. Advancements in artificial intelligence-driven techniques for interventional cardiology.Cardiol. J.202431232134110.5603/cj.98650 38247435
    [Google Scholar]
  40. SarkerI.H. Machine learning: Algorithms, real-world applications and research directions.SN Comput. Sci.20212316010.1007/s42979‑021‑00592‑x 33778771
    [Google Scholar]
  41. MassellaM. DriD.A. GramagliaD. Regulatory considerations on the use of machine learning based tools in clinical trials.Health Technol.20221261085109610.1007/s12553‑022‑00708‑0 36373014
    [Google Scholar]
  42. JordanM.I. MitchellT.M. Machine learning: Trends, perspectives, and prospects.Science2015349624525526010.1126/science.aaa8415 26185243
    [Google Scholar]
  43. LeeW.T. FangY.W. ChangW.S. Data-driven, two-stage machine learning algorithm-based prediction scheme for assessing 1-year and 3-year mortality risk in chronic hemodialysis patients.Sci. Rep.20231312145310.1038/s41598‑023‑48905‑9 38052875
    [Google Scholar]
  44. ZhouJ. PanY. WangS. Early detection and stratification of colorectal cancer using plasma cell-free DNA fragmentomic profiling.Genomics2024116411087610.1016/j.ygeno.2024.110876 38849019
    [Google Scholar]
  45. CobianchiL. VerdeJ.M. LoftusT.J. Artificial intelligence and surgery: Ethical dilemmas and open issues.J. Am. Coll. Surg.2022235226827510.1097/XCS.0000000000000242 35839401
    [Google Scholar]
  46. KrishnanG. SinghS. PathaniaM. Artificial intelligence in clinical medicine: Catalyzing a sustainable global healthcare paradigm.Front. Artif. Intell.20236122709110.3389/frai.2023.1227091 37705603
    [Google Scholar]
  47. LakaM. MilazzoA. MerlinT. Can evidence-based decision support tools transform antibiotic management? A systematic review and meta-analyses.J. Antimicrob. Chemother.20207551099111110.1093/jac/dkz543 31960021
    [Google Scholar]
  48. RichensJ.G. LeeC.M. JohriS. Improving the accuracy of medical diagnosis with causal machine learning.Nat. Commun.2020111392310.1038/s41467‑020‑17419‑7 32782264
    [Google Scholar]
  49. DadaE.G. BassiJ.S. ChiromaH. AbdulhamidS.M. AdetunmbiA.O. AjibuwaO.E. Machine learning for email spam filtering: Review, approaches and open research problems.Heliyon201956e0180210.1016/j.heliyon.2019.e01802 31211254
    [Google Scholar]
  50. SunJ. JeliazkovaN. ChupakhinV. ExCAPE-DB: An integrated large scale dataset facilitating Big Data analysis in chemogenomics.J. Cheminform.2017911710.1186/s13321‑017‑0203‑5 28316655
    [Google Scholar]
  51. PapadatosG. GaultonA. HerseyA. OveringtonJ.P. Activity, assay and target data curation and quality in the ChEMBL database.J. Comput. Aided Mol. Des.201529988589610.1007/s10822‑015‑9860‑5 26201396
    [Google Scholar]
  52. SturmN. MayrA. Le VanT. Industry-scale application and evaluation of deep learning for drug target prediction.J. Cheminform.20201212610.1186/s13321‑020‑00428‑5 33430964
    [Google Scholar]
  53. GuiotJ. VaidyanathanA. DeprezL. A review in radiomics: Making personalized medicine a reality via routine imaging.Med. Res. Rev.202242142644010.1002/med.21846 34309893
    [Google Scholar]
  54. BhallaS. LaganàA. Artificial intelligence for precision oncology.Adv. Exp. Med. Biol.2022136124926810.1007/978‑3‑030‑91836‑1_14 35230693
    [Google Scholar]
  55. WangY. MashockM. TongZ. Changing technologies of rna sequencing and their applications in clinical oncology.Front. Oncol.20201044710.3389/fonc.2020.00447 32328458
    [Google Scholar]
  56. VaskeO.M. BjorkI. SalamaS.R. Comparative tumor rna sequencing analysis for difficult-to-treat pediatric and young adult patients with cancer.JAMA Netw. Open2019210e191396810.1001/jamanetworkopen.2019.13968 31651965
    [Google Scholar]
  57. SatamH. JoshiK. MangroliaU. Next-generation sequencing technology: Current trends and advancements.Biology202312799710.3390/biology12070997 37508427
    [Google Scholar]
  58. NayarisseriA. KhandelwalR. TanwarP. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery.Curr. Drug Targets202122663165510.2174/18735592MTEzsMDMnz 33397265
    [Google Scholar]
  59. MintzY. BrodieR. Introduction to artificial intelligence in medicine.Minim. Invasive Ther. Allied Technol.2019282738110.1080/13645706.2019.1575882 30810430
    [Google Scholar]
  60. EltoraiA.E.M. BrattA.K. GuoH.H. Thoracic radiologists’ versus computer scientists’ perspectives on the future of artificial intelligence in radiology.J. Thorac. Imaging202035425525910.1097/RTI.0000000000000453 31609778
    [Google Scholar]
  61. Heywang-KöbrunnerS.H. HackerA. JänschA. Use of novel artificial intelligence computer-assisted detection (AI-CAD) for screening mammography: An analysis of 17,884 consecutive two-view full-field digital mammography screening exams.Acta Radiol.202364102697270310.1177/02841851231187382 37642981
    [Google Scholar]
  62. MalhiI.S. YiuZ.Z.N. Algorithm‐based smartphone apps to assess risk of skin cancer in adults: Critical appraisal of a systematic review.Br. J. Dermatol.2021184463863910.1111/bjd.19502 32866990
    [Google Scholar]
  63. DlaminiZ. FranciesF.Z. HullR. MarimaR. Artificial intelligence (AI) and big data in cancer and precision oncology.Comput. Struct. Biotechnol. J.2020182300231110.1016/j.csbj.2020.08.019 32994889
    [Google Scholar]
  64. BorowskyA.D. GlassyE.F. WallaceW.D. Digital whole slide imaging compared with light microscopy for primary diagnosis in surgical pathology.Arch. Pathol. Lab. Med.2020144101245125310.5858/arpa.2019‑0569‑OA 32057275
    [Google Scholar]
  65. HuangS. YangJ. FongS. ZhaoQ. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges.Cancer Lett.2020471617110.1016/j.canlet.2019.12.007 31830558
    [Google Scholar]
  66. NgiamK.Y. KhorI.W. Big data and machine learning algorithms for health-care delivery.Lancet Oncol.2019205e262e27310.1016/S1470‑2045(19)30149‑4 31044724
    [Google Scholar]
  67. LiZ. CaiH. LiZ. A tumor cell membrane-coated self-amplified nanosystem as a nanovaccine to boost the therapeutic effect of anti-PD-L1 antibody.Bioact. Mater.20232129931210.1016/j.bioactmat.2022.08.028 36157245
    [Google Scholar]
  68. WileńskiS. KoperA. ŚledzińskaP. BebynM. KoperK. Innovative strategies for effective paclitaxel delivery: Recent developments and prospects.J. Oncol. Pharm. Pract.202430236738410.1177/10781552231208978 38204196
    [Google Scholar]
  69. KleinK. StolkP. De BruinM.L. LeufkensH.G.M. CrommelinD.J.A. De VliegerJ.S.B. The EU regulatory landscape of non-biological complex drugs (NBCDs) follow-on products: Observations and recommendations.Eur. J. Pharm. Sci.201913322823510.1016/j.ejps.2019.03.029 30953753
    [Google Scholar]
  70. EdisZ. WangJ. WaqasM.K. IjazM. IjazM. Nanocarriers-mediated drug delivery systems for anticancer agents: An overview and perspectives.Int. J. Nanomedicine2021161313133010.2147/IJN.S289443 33628022
    [Google Scholar]
  71. AdirO. PoleyM. ChenG. Integrating artificial intelligence and nanotechnology for precision cancer medicine.Adv. Mater.20203213190198910.1002/adma.201901989 31286573
    [Google Scholar]
  72. SebastianA.M. PeterD. Artificial intelligence in cancer research: Trends, challenges and future directions.Life20221212199110.3390/life12121991 36556356
    [Google Scholar]
  73. AmannJ. BlasimmeA. VayenaE. FreyD. MadaiV.I. PreciseQ. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective.BMC Med. Inform. Decis. Mak.202020131010.1186/s12911‑020‑01332‑6 33256715
    [Google Scholar]
  74. BakerA. PerovY. MiddletonK. A comparison of artificial intelligence and human doctors for the purpose of triage and diagnosis.Front. Artif. Intell.2020354340510.3389/frai.2020.543405 33733203
    [Google Scholar]
  75. KannB.H. HosnyA. AertsH.J.W.L. Artificial intelligence for clinical oncology.Cancer Cell202139791692710.1016/j.ccell.2021.04.002 33930310
    [Google Scholar]
  76. CabralB.P. BragaL.A.M. Syed-AbdulS. MotaF.B. Future of artificial intelligence applications in cancer care: A global cross-sectional survey of researchers.Curr. Oncol.20233033432344610.3390/curroncol30030260 36975473
    [Google Scholar]
  77. ZeineldinR.A. KararM.E. BurgertO. Mathis-UllrichF. Neuroign: Explainable multimodal image-guided system for precise brain tumor surgery.J. Med. Syst.20244812510.1007/s10916‑024‑02037‑3 38393660
    [Google Scholar]
  78. OkadaT. KawadaK. SumiiA. Stereotactic navigation for rectal surgery: Comparison of 3-dimensional c-arm-based registration to paired-point registration.Dis. Colon Rectum202063569370010.1097/DCR.0000000000001608 32271219
    [Google Scholar]
  79. BertoloR. VecciaA. AntonelliA. Democratizing robotic prostatectomy: Navigating from novel platforms, telesurgery, and telementoring.Prostate Cancer Prostatic Dis.202410.1038/s41391‑024‑00812‑4
    [Google Scholar]
  80. OrecchiaL. MjaessG. AlbisinniS. Setting new standards: Robot-assisted radical prostatectomy as a day case.Prostate Cancer Prostatic Dis.202410.1038/s41391‑024‑00856‑6
    [Google Scholar]
  81. López-CortésA. Cabrera-AndradeA. Echeverría-GarcésG. Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses.Sci. Rep.20241411935910.1038/s41598‑024‑68565‑7 39169044
    [Google Scholar]
  82. HanJ.W. LeeS.K. KwonJ.H. A machine learning algorithm facilitates prognosis prediction and treatment selection for barcelona clinic liver cancer stage c hepatocellular carcinoma.Clin. Cancer Res.202430132812282110.1158/1078‑0432.CCR‑23‑3978 38639918
    [Google Scholar]
  83. Blanco-GonzálezA. CabezónA. Seco-GonzálezA. The role of AI in drug discovery: Challenges, opportunities, and strategies.Pharmaceuticals202316689110.3390/ph16060891 37375838
    [Google Scholar]
  84. PatelV. ShahM. Artificial intelligence and machine learning in drug discovery and development.Intell. Med.20222313414010.1016/j.imed.2021.10.001
    [Google Scholar]
  85. PawarA.D. Role of artificial intelligence in drug discovery and development.World J. Pharm. Res.202413514261436
    [Google Scholar]
  86. KangJ. JiaX. WangN. Insights into the structure-bioactivity relationships of marine sulfated polysaccharides: A review.Food Hydrocoll.202212310704910.1016/j.foodhyd.2021.107049
    [Google Scholar]
  87. GaoP. LiuZ. TanY. Accurate predictions of drugs aqueous solubility via deep learning tools.J. Mol. Struct.2022124913156210.1016/j.molstruc.2021.131562
    [Google Scholar]
  88. Jiménez-Luna, J., Grisoni, F., Weskamp, N., & Schneider, G. (2021). Artificial intelligence in drug discovery: recent advances and future perspectives. Expert opinion on drug discovery, 16(9), 949-959.10.1080/17460441.2021.1909567
    [Google Scholar]
  89. KumarR. SahaP. A review on artificial intelligence and machine learning to improve cancer management and drug discovery.Int J Res Appl Sci Biotechnol20229314915610.31033/ijrasb.9.3.26
    [Google Scholar]
  90. CavasottoC.N. ScardinoV. Machine learning toxicity prediction: Latest advances by toxicity end point.ACS Omega2022751475364754610.1021/acsomega.2c05693 36591139
    [Google Scholar]
  91. RenF. AliperA. ChenJ. A small-molecule TNIK inhibitor targets fibrosis in preclinical and clinical models.Nat. Biotechnol.202411310.1038/s41587‑024‑02143‑0 38459338
    [Google Scholar]
  92. SetiyaA. JaniV. SonavaneU. JoshiR. MolToxPred: Small molecule toxicity prediction using machine learning approach.RSC Advances20241464201422010.1039/D3RA07322J 38292268
    [Google Scholar]
  93. GubinaN DmitrenkoA SolovevG YamshchikovaL PetrovO LebedevI. Hybrid generative AI for de novo design of co-crystals with enhanced tabletability. arXiv preprint2024241017005
    [Google Scholar]
  94. ShiW. YangH. XieL. YinX.X. ZhangY. A review of machine learning-based methods for predicting drug–target interactions.Health Inf. Sci. Syst.20241213010.1007/s13755‑024‑00287‑6 38617016
    [Google Scholar]
  95. PatelA.U. MohantyS.K. ParwaniA.V. Applications of digital and computational pathology and artificial intelligence in genitourinary pathology diagnostics.Surg. Pathol. Clin.202215475978510.1016/j.path.2022.08.001 36344188
    [Google Scholar]
  96. BaeS. ChoiH. LeeD.S. Discovery of molecular features underlying the morphological landscape by integrating spatial transcriptomic data with deep features of tissue images.Nucleic Acids Res.20214910e5510.1093/nar/gkab095 33619564
    [Google Scholar]
  97. FreemanK. GeppertJ. StintonC. Use of artificial intelligence for image analysis in breast cancer screening programmes: Systematic review of test accuracy.BMJ2021374n187210.1136/bmj.n1872 34470740
    [Google Scholar]
  98. RahseparA.A. TavakoliN. KimG.H.J. HassaniC. AbtinF. BedayatA. How ai responds to common lung cancer questions: Chatgpt vs google bard.Radiology20233075e23092210.1148/radiol.230922 37310252
    [Google Scholar]
  99. PammiM. AghaeepourN. NeuJ. Multiomics, artificial intelligence, and precision medicine in perinatology.Pediatr. Res.202393230831510.1038/s41390‑022‑02181‑x 35804156
    [Google Scholar]
  100. TianY WangS XiongJ BiR ZhouZ BhuiyanMZA Robust and privacy-preserving decentralized deep federated learning training: Focusing on digital healthcare applications. IEEE/ACM Trans Comput Biol Bioinform20242189090110.1109/TCBB.2023.3243932
    [Google Scholar]
  101. Barredo ArrietaA. Díaz-RodríguezN. Del SerJ. Explainable artificial intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI.Inf. Fusion2020588211510.1016/j.inffus.2019.12.012
    [Google Scholar]
  102. SaeedW. OmlinC. ExplainableA.I. XAI): A systematic meta-survey of current challenges and future opportunities.Knowl. Base. Syst.202326311027310.1016/j.knosys.2023.110273
    [Google Scholar]
  103. LuoS. IvisonH. HanS.C. PoonJ. Local interpretations for explainable natural language processing: A survey.ACM Comput. Surv.202456913610.1145/3649450
    [Google Scholar]
  104. DwivediR. DaveD. NaikH. Explainable AI (XAI): Core ideas, techniques, and solutions.ACM Comput. Surv.202355913310.1145/3561048
    [Google Scholar]
  105. DingW. Abdel-BassetM. HawashH. AliA.M. Explainability of artificial intelligence methods, applications and challenges: A comprehensive survey.Inf. Sci.202261523829210.1016/j.ins.2022.10.013
    [Google Scholar]
  106. LiangW. MeoP.D. TangY. ZhuJ. A survey of multi-modal knowledge graphs: Technologies and trends.ACM Comput. Surv.2024561114110.1145/3656579
    [Google Scholar]
  107. BhuyanB.P. Ramdane-CherifA. TomarR. SinghT.P. Neuro-symbolic artificial intelligence: A survey.Neural Comput. Appl.20243621128091284410.1007/s00521‑024‑09960‑z
    [Google Scholar]
  108. XieQ. JiangS. JiangL. Efficiency optimization techniques in privacy-preserving federated learning with homomorphic encryption: A brief survey.IEEE Internet Things J.20241114245692458010.1109/JIOT.2024.3382875
    [Google Scholar]
  109. SofferS. LahatA. KlangE. Artificial intelligence in colonoscopy.Lancet Gastroenterol. Hepatol.202161298410.1016/S2468‑1253(21)00349‑6 34774155
    [Google Scholar]
  110. Artificial intelligence to implement cost-saving strategies for colonoscopy screening based on in vivo prediction of polyp histology (save).Patent NCT060419452023
  111. Artificial intelligence-based smartphone application for skin cancer detection.Patent NCT052461632024
  112. ARCHERY - Artificial intelligence based radiotherapy treatment planning for cervical, head and neck and prostate cancer.Patent NCT056530632024
  113. Model study on cervical cancer screening strategies and risk prediction.Patent NCT062041332024
  114. Development of an imaging prediction model for pelvic lymph node metastasis of cervical cancer using artificial intelligence techniques.2021Available from: https://app.trialscreen.org/trials/development-imaging-prediction-model-pelvic-lymph-node-metastasis-cervical-study-nct06448897
    [Google Scholar]
  115. Novel, one stop, affordable, point of care and ai supported system of screening, triage and treatment selection for cervical cancer in lmics (easter)Patent NCT060425432024
  116. Results comparison of cervical cancer early detection using cerviray with via test.Patent NCT065180702024
  117. Combining artificial intelligence with balloon mucosal exposure device for polyp detection in screening individuals (combat)Patent NCT058294472024
  118. McGillB.C. WakefieldC.E. HetheringtonK. “Balancing expectations with actual realities”: Conversations with clinicians and scientists in the first year of a high-risk childhood cancer precision medicine trial.J. Pers. Med.2020101910.3390/jpm10010009 32075154
    [Google Scholar]
  119. ChuaI.S. Gaziel-YablowitzM. KorachZ.T. Artificial intelligence in oncology: Path to implementation.Cancer Med.202110124138414910.1002/cam4.3935 33960708
    [Google Scholar]
  120. ShreveJ.T. KhananiS.A. HaddadT.C. Artificial intelligence in oncology: Current capabilities, future opportunities, and ethical considerations.Am. Soc. Clin. Oncol. Educ. Book2022424284285110.1200/EDBK_350652 35687826
    [Google Scholar]
/content/journals/pra/10.2174/0115748928361472250123105507
Loading
/content/journals/pra/10.2174/0115748928361472250123105507
Loading

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test