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2000
Volume 22, Issue 1
  • ISSN: 1875-6921
  • E-ISSN: 1875-6913

Abstract

Personalized medicine (PM) offers a significant possibility for enhancing the future of tailored healthcare. This article assesses the challenges and opportunities that multi-omics research faces globally to advance personalized medicine. It provides a broad review of these issues. AI has improved the healthcare possibilities for emerging innovations including artificial intelligence (AI), and it initiates a discussion amongst essential projects in this field. Without inquiry, artificial intelligence (AI) is the most widely debated topic in healthcare imaging studies, both diagnostically and therapeutically. AI has remained applied toward radiation oncology image modalities for objectives such as therapy evaluation and tumor delineation. It provides considerable promise for increased effectiveness and efficiency, as well as in the pharmaceutical sector is no exception. The use of AI technology for assessing and analyzing several crucial pharmacy disciplines, such as drug research, dosage form design, poly-pharmacy, and hospital pharmacy, has garnered a great deal of attention in the last few decades. The difficulty is in efficiently evaluating large volumes of data to provide specific treatment strategies. The infrastructure of healthcare requires modifications to integrate AI into personalized care. With authorization, patient's personal information and clinical data—such as imagery, electrophysiological results, genetic details, arterial pressure, medical records, .—are incorporated into the AI system upon their accession. The AI system then makes use of this individual patient's information to provide advice for healthcare, enabling healthcare staff to make clinical assessments. AI also enables predictive modeling, drug discovery, and precision medicine, ultimately revolutionizing how healthcare is delivered.

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References

  1. SindelarR.D. Genomics, other “omics” technologies, personalized medicine, and additional biotechnology-related tech-niques.Pharmaceutical Biotechnology.CRC Press2016149190
     [Google Scholar]
  2. BecciaF. HoxhajI. CastagnaC. An overview of personalized medicine landscape and policies in the European Union.Eur. J. Public Health202232684485110.1093/eurpub/ckac103 36305782
     [Google Scholar]
  3. OldoniE. SaundersG. BietrixF. Tackling the translational challenges of multi-omics research in the realm of European personal-ised medicine: A workshop report.Front. Mol. Biosci.2022997479910.3389/fmolb.2022.974799 36310597
     [Google Scholar]
  4. PaananenJ. FortinoV. An omics perspective on drug target discovery platforms.Brief. Bioinform.20202161937195310.1093/bib/bbz122 31774113
     [Google Scholar]
  5. JohnsonK.B. WeiW.Q. WeeraratneD. Precision medicine, AI, and the future of personalized health care.Clin. Transl. Sci.2021141869310.1111/cts.12884 32961010
     [Google Scholar]
  6. GrayI.D. KrossA.R. RenfrewM.E. WoodP. Precision medicine in lifestyle medicine: The way of the future?Am. J. Lifestyle Med.202014216918610.1177/1559827619834527 32231483
     [Google Scholar]
  7. TopolE. Deep medicine: How artificial intelligence can make healthcare human again.UKHachette2019
     [Google Scholar]
  8. RamachanderA. GowriD.P. The future of digital health in transforming healthcare.Digital Technology in Public Health and Rehabilita-tion Care.Academic Press202536338510.1016/B978‑0‑443‑22270‑2.00021‑6
     [Google Scholar]
  9. RauniyarA. HagosD.H. JhaD. Federated learning for medical applications: A taxonomy, current trends, challenges, and future research directions.IEEE Internet Things J.202311573747398
     [Google Scholar]
  10. WangY. KungL. ByrdT.A. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations.Technol. Forecast. Soc. Change201812631310.1016/j.techfore.2015.12.019
     [Google Scholar]
  11. WangD. WeiszJ.D. MullerM. Human-AI collaboration in data science: Exploring data scientists’ perceptions of automated AI.Proceedings of the ACM on human-computer interactionNew York, NY, USA, November20193(CSCW)12410.1145/3359313
     [Google Scholar]
  12. MohantyS. VyasS. MohantyS. VyasS. The economics of artificial intelligence: Implementing a collaborative human-machine strate-gy for your business.In: How to Compete in the Age of Artificial Intelligence.Berkeley, CAApress201814010.1007/978‑1‑4842‑3808‑0_1
     [Google Scholar]
  13. BajwaJ. MunirU. NoriA. WilliamsB. Artificial intelligence in healthcare: Transforming the practice of medicine.Future Healthc. J.202182e188e19410.7861/fhj.2021‑0095 34286183
     [Google Scholar]
  14. FügenerA. GrahlJ. GuptaA. KetterW. Cognitive challenges in human–artificial intelligence collaboration: Investigating the path to-ward productive delegation.Inf. Syst. Res.202233267869610.1287/isre.2021.1079
     [Google Scholar]
  15. DavenportT.H. The AI advantage: How to put the Artificial Intelligence revolution to work.Cambridge, MassachusettsMIT Press2018
     [Google Scholar]
  16. DwivediY.K. HughesL. IsmagilovaE. Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, oppor-tunities, and agenda for research, practice and policy.Int. J. Inf. Manage.20215710199410.1016/j.ijinfomgt.2019.08.002
     [Google Scholar]
  17. NayarisseriA. KhandelwalR. TanwarP. Artificial intelligence, big data and machine learning approaches in precision medicine & drug discovery.Curr. Drug Targets202122663165510.2174/18735592MTEzsMDMnz 33397265
     [Google Scholar]
  18. ItchhaporiaD. SnowP.B. AlmassyR.J. OetgenW.J. Artificial neural networks: Current status in cardiovascular medicine.J. Am. Coll. Cardiol.199628251552110.1016/S0735‑1097(96)00174‑X 8800133
     [Google Scholar]
  19. McBeeM.P. AwanO.A. ColucciA.T. Deep learning in radiology.Acad. Radiol.201825111472148010.1016/j.acra.2018.02.018 29606338
     [Google Scholar]
  20. KhyaniD. SiddharthaB.S. NivedithaN.M. DivyaB.M. An interpretation of lemmatization and stemming in natural language processing.J Univ Shanghai Sci Technol20212210350357
     [Google Scholar]
  21. KhanW. DaudA. KhanK. MuhammadS. HaqR. Exploring the frontiers of deep learning and natural language processing: A com-prehensive overview of key challenges and emerging trends.Nat Lang Process J2023410002610.1016/j.nlp.2023.100026
     [Google Scholar]
  22. LockeS. BashallA. Al-AdelyS. MooreJ. WilsonA. KitchenG.B. Natural language processing in medicine: A review.Trends Anaesth Crit Care2021384910.1016/j.tacc.2021.02.007
     [Google Scholar]
  23. MeffordD. Steps toward artificial intelligence: Rule-based, case-based, and explanation-based models of politics.Artificial Intelligence and International Politics.Routledge20195696
     [Google Scholar]
  24. CastilloV.H. Martínez-GarcíaA.I. PulidoJ.R.G. A knowledge-based taxonomy of critical factors for adopting electronic health record systems by physicians: A systematic literature review.BMC Med. Inform. Decis. Mak.20101016010.1186/1472‑6947‑10‑60 20950458
     [Google Scholar]
  25. Hayes-RothF. The knowledge-based expert system: A tutorial.Computer1984179112810.1109/MC.1984.1659242
     [Google Scholar]
  26. RebouhS. LefnaouiS. BouheddaM. YahoumM.M. HaniniS. Neuro-fuzzy modeling of ibuprofen-sustained release from tablets based on different cellulose derivatives.Drug Deliv. Transl. Res.20199116217710.1007/s13346‑018‑00592‑0 30341764
     [Google Scholar]
  27. ZakiM.R. VarshosazJ. FathiM. Preparation of agar nanospheres: Comparison of response surface and artificial neural network model-ing by a genetic algorithm approach.Carbohydr. Polym.201512231432010.1016/j.carbpol.2014.12.031 25817674
     [Google Scholar]
  28. ZhaoF. LuJ. JinX. Comparison of response surface methodology and artificial neural network to optimize novel ophthalmic flexible nano-liposomes: Characterization, evaluation, in vivo pharmacokinetics and molecular dynamics simulation.Colloids Surf. B Biointerfaces201817228829710.1016/j.colsurfb.2018.08.046 30173096
     [Google Scholar]
  29. GülerG.K. EroğluH. ÖnerL. Development and formulation of floating tablet formulation containing rosiglitazone maleate using Artifi-cial Neural Network.J. Drug Deliv. Sci. Technol.20173938539710.1016/j.jddst.2017.04.029
     [Google Scholar]
  30. IlićM. ĐurišJ. KovačevićI. IbrićS. ParojčićJ. In vitro – in silico – in vivo drug absorption model development based on mechanistic gastrointestinal simulation and artificial neural networks: Nifedipine osmotic release tablets case study.Eur. J. Pharm. Sci.20146221221810.1016/j.ejps.2014.05.030 24911992
     [Google Scholar]
  31. KolettiA.E. TsarouchiE. KapouraniA. KontogiannopoulosK.N. AssimopoulouA.N. BarmpalexisP. Gelatin nanoparticles for NSAID systemic administration: Quality by design and artificial neural networks implementation.Int. J. Pharm.202057811911810.1016/j.ijpharm.2020.119118 32032642
     [Google Scholar]
  32. León BlancoJ.M. González-RP.L. Arroyo GarcíaC.M. Artificial neural networks as alternative tool for minimizing error predic-tions in manufacturing ultradeformable nanoliposome formulations.Drug Dev. Ind. Pharm.201844113514310.1080/03639045.2017.1386201 28967285
     [Google Scholar]
  33. MalakarJ. SenS.O. NayakA.K. SenK.K. Formulation, optimization and evaluation of transferosomal gel for transdermal insulin deliv-ery.Saudi Pharm. J.201220435536310.1016/j.jsps.2012.02.001 23960810
     [Google Scholar]
  34. MalakarJ. NayakA.K. DasA. Modified starch (cationized)–alginate beads containing aceclofenac: Formulation optimization using central composite design.Stärke2013657-860361210.1002/star.201200231
     [Google Scholar]
  35. GuruP.R. NayakA.K. SahuR.K. Oil-entrapped sterculia gum–alginate buoyant systems of aceclofenac: Development and in vitro evalu-ation.Colloids Surf. B Biointerfaces201310426827510.1016/j.colsurfb.2012.11.027 23334180
     [Google Scholar]
  36. Kumar NayakA. Saquib HasnainM. MalakarJ. Development and optimization of hydroxyapatite-ofloxacin implants for possible bone delivery in osteomyelitis treatment.Curr. Drug Deliv.201310224125010.2174/1567201811310020008 23092295
     [Google Scholar]
  37. NayakA. PalD. HasnainM. Development, optimization and in vitro-in vivo evaluation of pioglitazone- loaded jackfruit seed starch-alginate beads.Curr. Drug Deliv.201310560861910.2174/1567201811310050012 23360248
     [Google Scholar]
  38. NayakA.K. PalD. Ionotropically-gelled mucoadhesive beads for oral metformin HCl delivery: Formulation, optimization and antidia-betic evaluation.J. Sci. Ind. Res.20137211522
     [Google Scholar]
  39. ChaibvaF. BurtonM. WalkerR.B. Optimization of salbutamol sulfate dissolution from sustained release matrix formulations using an artificial neural network.Pharmaceutics20102218219810.3390/pharmaceutics2020182 27721350
     [Google Scholar]
  40. NayakA.K. PalD. MalakarJ. Development, optimization, and evaluation of emulsion‐gelled floating beads using natural polysaccha-ride‐blend for controlled drug release.Polym. Eng. Sci.201353223825010.1002/pen.23259
     [Google Scholar]
  41. MalakarJ. DattaP.K. PurakayasthaS.D. DeyS. NayakA.K. Floating capsules containing alginate-based beads of salbutamol sulfate: In vitro–in vivo evaluations.Int. J. Biol. Macromol.20146418118910.1016/j.ijbiomac.2013.11.014 24296401
     [Google Scholar]
  42. GroverA.K. AshrafM.H. Digitization in supply chain management.In: Trends, Challenges and Solutions in Contemporary Supply Chain Management.SingaporeWorld Scientific2024
     [Google Scholar]
  43. GhoshA. ChakrabortyD. LawA. Artificial intelligence in Internet of things.CAAI Trans. Intell. Technol.20183420821810.1049/trit.2018.1008
     [Google Scholar]
  44. NgA.T. TamP.C. Current status of robot-assisted surgery.Hong Kong Med. J.2014203241250 24854139
     [Google Scholar]
  45. LambertonC. BrigoD. HoyD. Impact of Robotics, RPA and AI on the insurance industry: Challenges and opportunities.J Financ Perspect201741
     [Google Scholar]
  46. ZhouZ. ChenY. HuangF. FengY. AibinM. Medical imaging RPA system design.2023 IEEE Canadian Conference on Electrical and Computer Engineering (CCECE)Regina, SK, Canada202319219710.1109/CCECE58730.2023.10289020
     [Google Scholar]
  47. ChenB. ButteA.J. Leveraging big data to transform target selection and drug discovery.Clin. Pharmacol. Ther.201699328529710.1002/cpt.318 26659699
     [Google Scholar]
  48. KoutsoukasA. SimmsB. KirchmairJ. From in silico target prediction to multi-target drug design: Current databases, methods and applications.J. Proteomics201174122554257410.1016/j.jprot.2011.05.011 21621023
     [Google Scholar]
  49. TomczakK. CzerwińskaP. WiznerowiczM. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge.Contemp. Oncol.2015191AA68A77
     [Google Scholar]
  50. ShtivelmanE. HensingT. SimonG.R. Molecular pathways and therapeutic targets in lung cancer.Oncotarget2014561392143310.18632/oncotarget.1891 24722523
     [Google Scholar]
  51. CohenP. CrossD. JänneP.A. Kinase drug discovery 20 years after imatinib: Progress and future directions.Nat. Rev. Drug Discov.202120755156910.1038/s41573‑021‑00195‑4 34002056
     [Google Scholar]
  52. AldeaM. AndreF. MarabelleA. DoganS. BarlesiF. SoriaJ.C. Overcoming resistance to tumor-targeted and immune-targeted thera-pies.Cancer Discov.202111487489910.1158/2159‑8290.CD‑20‑1638 33811122
     [Google Scholar]
  53. WangM. ChenS. AoD. Targeting DNA repair pathway in cancer: Mechanisms and clinical application.MedComm20212465469110.1002/mco2.103 34977872
     [Google Scholar]
  54. GriebB.C. The function of MTBP in proliferation, tumorigenesis and tumor cell maintenance. Doctoral dissertation2014
     [Google Scholar]
  55. LuY. ZouR. GuQ. CRNDE mediated hnRNPA2B1 stability facilitates nuclear export and translation of KRAS in colorectal cancer.Cell Death Dis.202314961110.1038/s41419‑023‑06137‑9 37716979
     [Google Scholar]
  56. LuoC.W. HouM.F. HuangC.W. The CDK6-c-Jun-Sp1-MMP-2 axis as a biomarker and therapeutic target for triple-negative breast cancer.Am. J. Cancer Res.2020101243254341 33415002
     [Google Scholar]
  57. HanH. ChenY. ChengL. ProchownikE.V. LiY. microRNA-206 impairs c-Myc-driven cancer in a synthetic lethal manner by directly inhibiting MAP3K13.Oncotarget2016713164091641910.18632/oncotarget.7653 26918941
     [Google Scholar]
  58. DodhiawalaP.B. KhuranaN. ZhangD. TPL2 enforces RAS-induced inflammatory signaling and is activated by point mutations.J. Clin. Invest.202013094771479010.1172/JCI137660 32573499
     [Google Scholar]
  59. OokiA. YamaguchiK. The dawn of precision medicine in diffuse-type gastric cancer.Ther. Adv. Med. Oncol.2022141758835922108304910.1177/17588359221083049 35281349
     [Google Scholar]
  60. MusunuruK. SheikhF. GuptaR.M. Induced pluripotent stem cells for cardiovascular disease modeling and precision medicine: A scientific statement from the American heart association.Circ. Genom. Precis. Med.2018111e00004310.1161/HCG.0000000000000043 29874173
     [Google Scholar]
  61. LinX. LiX. LinX. A review on applications of computational methods in drug screening and design.Molecules2020256137510.3390/molecules25061375 32197324
     [Google Scholar]
  62. GuoT. DuanH. ChenJ. N6-Methyladenosine writer gene ZC3H13 predicts immune phenotype and therapeutic opportunities in kidney renal clear cell carcinoma.Front. Oncol.20211171864410.3389/fonc.2021.718644 34497769
     [Google Scholar]
  63. Gonatopoulos-PournatzisT. WuM. BraunschweigU. Genome-wide CRISPR-Cas9 interrogation of splicing networks reveals a mechanism for recognition of autism-misregulated neuronal microexons.Mol. Cell2018723510524.e1210.1016/j.molcel.2018.10.008 30388412
     [Google Scholar]
  64. ParkA. LeeY. NamS. A performance evaluation of drug response prediction models for individual drugs.Sci. Rep.20231311191110.1038/s41598‑023‑39179‑2 37488424
     [Google Scholar]
  65. KalininA.A. HigginsG.A. ReamaroonN. Deep learning in pharmacogenomics: From gene regulation to patient stratification.Pharmacogenomics201819762965010.2217/pgs‑2018‑0008 29697304
     [Google Scholar]
  66. BodalalZ. TrebeschiS. Nguyen-KimT.D.L. SchatsW. Beets-TanR. Radiogenomics: Bridging imaging and genomics.Abdom. Radiol.20194461960198410.1007/s00261‑019‑02028‑w 31049614
     [Google Scholar]
  67. Gürsoy ÇoruhA. YenigünB. UzunÇ. A comparison of the fusion model of deep learning neural networks with human observa-tion for lung nodule detection and classification.Br. J. Radiol.20219411232021022210.1259/bjr.20210222 34111976
     [Google Scholar]
  68. RashidJ. BatoolS. KimJ. An augmented artificial intelligence approach for chronic diseases prediction.Front. Public Health20221086039610.3389/fpubh.2022.860396 35433587
     [Google Scholar]
  69. SantangeloO.E. GentileV. PizzoS. GiordanoD. CedroneF. Machine learning and prediction of infectious diseases: A systematic review.Machine Learning and Knowledge Extraction20235117519810.3390/make5010013
     [Google Scholar]
  70. SchorkN.J. Artificial Intelligence and personalized medicine.Cancer Treat Res20191782658310.1007/978‑3‑030‑16391‑4_11
     [Google Scholar]
  71. PandaS.K. CheongH. TunT.A. Describing the structural phenotype of the glaucomatous optic nerve head using artificial intelli-gence.Am. J. Ophthalmol.202223617218210.1016/j.ajo.2021.06.010 34157276
     [Google Scholar]
  72. YapA. WilkinsonB. ChenE. Patients perceptions of artificial intelligence in diabetic eye screening.Asia-Pac. J. Ophthalmol.202211328729310.1097/APO.0000000000000525 35772087
     [Google Scholar]
  73. ScannellJ.W. BlanckleyA. BoldonH. WarringtonB. Diagnosing the decline in pharmaceutical R&D efficiency.Nat. Rev. Drug Discov.201211319120010.1038/nrd3681 22378269
     [Google Scholar]
  74. Van NormanG.A. Overcoming the declining trends in innovation and investment in cardiovascular therapeutics: Beyond EROOM’s Law.JACC Basic Transl. Sci.20172561362510.1016/j.jacbts.2017.09.002 30062175
     [Google Scholar]
  75. Gómez-BombarelliR. WeiJ.N. DuvenaudD. Automatic chemical design using a data-driven continuous representation of mole-cules.ACS Cent. Sci.20184226827610.1021/acscentsci.7b00572 29532027
     [Google Scholar]
  76. NussinovR. ZhangM. LiuY. JangH. AlphaFold, artificial intelligence (AI), and allostery.J. Phys. Chem. B2022126346372638310.1021/acs.jpcb.2c04346 35976160
     [Google Scholar]
  77. GuptaR. SrivastavaD. SahuM. TiwariS. AmbastaR.K. KumarP. Artificial intelligence to deep learning: Machine intelligence ap-proach for drug discovery.Mol. Divers.20212531315136010.1007/s11030‑021‑10217‑3 33844136
     [Google Scholar]
  78. MeloM.C.R. MaaschJ.R.M.A. de la Fuente-NunezC. Accelerating antibiotic discovery through artificial intelligence.Commun. Biol.202141105010.1038/s42003‑021‑02586‑0 34504303
     [Google Scholar]
  79. MarchantJ. Powerful antibiotics discovered using AI.Nature202010.1038/d41586‑020‑00018‑3 33603175
     [Google Scholar]
  80. ParvathaneniV. KulkarniN.S. MuthA. GuptaV. Drug repurposing: A promising tool to accelerate the drug discovery process.Drug Discov. Today201924102076208510.1016/j.drudis.2019.06.014 31238113
     [Google Scholar]
  81. WethF.R. HoggarthG.B. WethA.F. Unlocking hidden potential: Advancements, approaches, and obstacles in repurposing drugs for cancer therapy.Br. J. Cancer2024130570371510.1038/s41416‑023‑02502‑9 38012383
     [Google Scholar]
  82. GnsH.S. GrS. MurahariM. KrishnamurthyM. An update on drug repurposing: Re-written saga of the drug’s fate.Biomed. Pharmacother.201911070071610.1016/j.biopha.2018.11.127 30553197
     [Google Scholar]
  83. PrasadK. KumarV. Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.Curr Res Pharmacol Drug Discov2021210004210.1016/j.crphar.2021.100042 34870150
     [Google Scholar]
  84. DavenportT. KalakotaR. The potential for artificial intelligence in healthcare.Future Healthc. J.201962949810.7861/futurehosp.6‑2‑94 31363513
     [Google Scholar]
  85. McKinneyS.M. SieniekM. GodboleV. International evaluation of an AI system for breast cancer screening.Nature20205777788899410.1038/s41586‑019‑1799‑6 31894144
     [Google Scholar]
  86. EverettE. KaneB. YooA. DobsA. MathioudakisN. A novel approach for fully automated, personalized health coaching for adults with prediabetes: Pilot clinical trial.J. Med. Internet Res.2018202e7210.2196/jmir.9723 29487046
     [Google Scholar]
  87. RubeisG. The disruptive power of Artificial Intelligence. Ethical aspects of gerontechnology in elderly care.Arch. Gerontol. Geriatr.20209110418610.1016/j.archger.2020.104186 32688106
     [Google Scholar]
  88. ZhaoW. YangJ. SunY. 3D deep learning from CT scans predicts tumor invasiveness of sub-centimeter pulmonary adenocarci-nomas.Cancer Res.201878246881688910.1158/0008‑5472.CAN‑18‑0696 30279243
     [Google Scholar]
  89. ArdilaD. KiralyA.P. BharadwajS. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography.Nat. Med.201925695496110.1038/s41591‑019‑0447‑x 31110349
     [Google Scholar]
  90. ZhangY. JiangB. ZhangL. Lung nodule detectability of artificial intelligence-assisted CT image reading in lung cancer screening.Curr. Med. Imaging Rev.202218332733410.2174/1573405617666210806125953 34365951
     [Google Scholar]
  91. WangJ. DobbinsJ.T.III LiQ. Automated lung segmentation in digital chest tomosynthesis.Med. Phys.201239273274110.1118/1.3671939 22320783
     [Google Scholar]
  92. KimH.E. KimH.H. HanB.K. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: A retrospective, multireader study.Lancet Digit. Health202023e138e14810.1016/S2589‑7500(20)30003‑0 33334578
     [Google Scholar]
  93. BeckerA.S. MarconM. GhafoorS. WurnigM.C. FrauenfelderT. BossA. Deep learning in mammography: Diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer.Invest. Radiol.201752743444010.1097/RLI.0000000000000358 28212138
     [Google Scholar]
  94. LeeH. LeeE.J. HamS. Machine learning approach to identify stroke within 4.5 hours.Stroke202051386086610.1161/STROKEAHA.119.027611 31987014
     [Google Scholar]
  95. JaroszewskaA. BattsengelU. ZadoroznaZ. Reassessment of the underlying mechanisms that contribute to the neurological disorders linked to long-term COVID-19.Disaster Emerg Med J202492129130
     [Google Scholar]
  96. HormuthD.A.II FarhatM. ChristensonC. Opportunities for improving brain cancer treatment outcomes through imaging-based mathematical modeling of the delivery of radiotherapy and immunotherapy.Adv. Drug Deliv. Rev.202218711436710.1016/j.addr.2022.114367 35654212
     [Google Scholar]
  97. MallP.K. SinghP.K. SrivastavS. A comprehensive review of deep neural networks for medical image processing: Recent devel-opments and future opportunities.Healthc Anal20234100216
     [Google Scholar]
  98. MeshramP. BaraiT. TahirM. BodheK. The brain tumors identification, detection, and classification with AI/ML algorithm with cer-tainty of operations.International Conference on Image Processing and Capsule NetworksSingapore202361162810.1007/978‑981‑99‑7093‑3_41
     [Google Scholar]
  99. ThirunavukarasuR. C GPD, R G, Gopikrishnan M, Palanisamy V. Towards computational solutions for precision medicine based big data healthcare system using deep learning models: A review.Comput. Biol. Med.202214910602010.1016/j.compbiomed.2022.106020 36088715
     [Google Scholar]
  100. XuM. OuyangY. YuanZ. Deep learning aided neuroimaging and brain regulation.Sensors20232311499310.3390/s23114993 37299724
     [Google Scholar]
  101. MirbabaieM. StieglitzS. FrickN.R.J. Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction.Health Technol.202111469373110.1007/s12553‑021‑00555‑5
     [Google Scholar]
  102. RansohoffD.F. FeinsteinA.R. Problems of spectrum and bias in evaluating the efficacy of diagnostic tests.N. Engl. J. Med.19782991792693010.1056/NEJM197810262991705 692598
     [Google Scholar]
  103. LellaK.K. A literature review on COVID-19 disease diagnosis from respiratory sound dataarXiv:2112076702021
     [Google Scholar]
  104. AschF.M. AbrahamT. JankowskiM. Accuracy and reproducibility of a novel artificial intelligence deep learning-based algorithm for automated calculation of ejection fraction in echocardiography.J. Am. Coll. Cardiol.2019739Supplement 1144710.1016/S0735‑1097(19)32053‑4
     [Google Scholar]
  105. RetsonT.A. BesserA.H. SallS. GoldenD. HsiaoA. Machine learning and deep neural networks in thoracic and cardiovascular imag-ing.J. Thorac. Imaging201934319220110.1097/RTI.0000000000000385 31009397
     [Google Scholar]
  106. LeE.P.V. WangY. HuangY. HickmanS. GilbertF.J. Artificial intelligence in breast imaging.Clin. Radiol.201974535736610.1016/j.crad.2019.02.006 30898381
     [Google Scholar]
  107. EvansA.J. BauerT.W. BuiM.M. US Food and Drug Administration approval of whole slide imaging for primary diagnosis: A key milestone is reached and new questions are raised.Arch. Pathol. Lab. Med.2018142111383138710.5858/arpa.2017‑0496‑CP 29708429
     [Google Scholar]
  108. AwwaluJ. GarbaA.G. GhazviniA. AtuahR. Artificial intelligence in personalized medicine application of AI algorithms in solving personalized medicine problems.Int J Comput Theory Eng20157643944310.7763/IJCTE.2015.V7.999
     [Google Scholar]
  109. BlasiakA. KhongJ. KeeT. CURATE. AI: Optimizing personalized medicine with artificial intelligence.SLAS Technol.20202529510510.1177/2472630319890316 31771394
     [Google Scholar]
  110. DiasR. TorkamaniA. Artificial intelligence in clinical and genomic diagnostics.Genome Med.20191117010.1186/s13073‑019‑0689‑8 31744524
     [Google Scholar]
  111. HolleL.M. Boehnke MichaudL. Oncology pharmacists in health care delivery: Vital members of the cancer care team.J. Oncol. Pract.2014103e142e14510.1200/JOP.2013.001257 24618076
     [Google Scholar]
  112. VulajV. HoughS. BedardL. FarrisK. MacklerE. Oncology pharmacist opportunities: Closing the gap in quality care.J. Oncol. Pract.2018146e403e41110.1200/JOP.2017.026666 29298114
     [Google Scholar]
  113. MercerK. BurnsC. GuirguisL. Physician and pharmacist medication decision-making in the time of electronic health records: Mixed-methods study.JMIR Human Factors201853e2410.2196/humanfactors.9891 30274959
     [Google Scholar]
  114. M SegalE BatesJ FleszarSL Demonstrating the value of the oncology pharmacist within the healthcare team.J. Oncol. Pharm. Pract.20192581945196710.1177/1078155219859424 31288634
     [Google Scholar]
  115. ShickelB. TigheP.J. BihoracA. RashidiP. Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis.IEEE J. Biomed. Health Inform.20182251589160410.1109/JBHI.2017.2767063 29989977
     [Google Scholar]
  116. RajkomarA. DeanJ. KohaneI. Machine learning in medicine.N. Engl. J. Med.2019380141347135810.1056/NEJMra1814259 30943338
     [Google Scholar]
  117. LitjensG. A survey on deep learning in medical image analysis.Med. Image Anal.2017426088
     [Google Scholar]
  118. GoodingP. KariotisT. Ethics and law in research on algorithmic and data-driven technology in mental health care: Scoping review.JMIR Ment. Health202186e2466810.2196/24668 34110297
     [Google Scholar]
  119. AungY.Y.M. WongD.C.S. TingD.S.W. The promise of artificial intelligence: A review of the opportunities and challenges of artificial intelligence in healthcare.Br. Med. Bull.2021139141510.1093/bmb/ldab016 34405854
     [Google Scholar]
  120. CresswellK. MajeedA. BatesD.W. SheikhA. Computerised decision support systems for healthcare professionals: An interpretative review.Inform. Prim. Care2012202115128 23710776
     [Google Scholar]
  121. KaushalR. ShojaniaK.G. BatesD.W. Effects of computerized physician order entry and clinical decision support systems on medication safety: A systematic review.Arch. Intern. Med.2003163121409141610.1001/archinte.163.12.1409 12824090
     [Google Scholar]
  122. CireşanD.C. GiustiA. GambardellaL.M. SchmidhuberJ. Mitosis detection in breast cancer histology images with deep neural networks.Medical Image Computing and Computer-Assisted Intervention–MICCAI 2013: 16th International Conference Nagoya,. JapanSeptember 22-26, 2013411418
     [Google Scholar]
  123. EstevaA. KuprelB. NovoaR.A. Dermatologist-level classification of skin cancer with deep neural networks.Nature20175427639115118
     [Google Scholar]
  124. GulshanV. PengL. CoramM. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA2016316222402241010.1001/jama.2016.17216 27898976
     [Google Scholar]
  125. PoplinR. VaradarajanA.V. BlumerK. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learn-ing.Nat. Biomed. Eng.20182315816410.1038/s41551‑018‑0195‑0 31015713
     [Google Scholar]
  126. WalshC.G. RibeiroJ.D. FranklinJ.C. Predicting suicide attempts in adolescents with longitudinal clinical data and machine learning.J. Child Psychol. Psychiatry201859121261127010.1111/jcpp.12916 29709069
     [Google Scholar]
  127. MiottoR. LiL. KiddB.A. DudleyJ.T. Deep patient: An unsupervised representation to predict the future of patients from the electronic health records.Sci. Rep.2016612609410.1038/srep26094 27185194
     [Google Scholar]
  128. ChoiE. SchuetzA. StewartW.F. SunJ. Using recurrent neural network models for early detection of heart failure onset.J. Am. Med. Inform. Assoc.201724236137010.1093/jamia/ocw112 27521897
     [Google Scholar]
  129. ChengY. WangF. ZhangP. HuJ. Risk prediction with electronic health records: A deep learning approach.Proceedings of the 2016 SIAM International Conference on Data Mining (SDM)201643244010.1137/1.9781611974348.49
     [Google Scholar]
  130. MotwaniM. DeyD. BermanD.S. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: A 5-year multicentre prospective registry analysis.Eur. Heart J.2017387500507 27252451
     [Google Scholar]
  131. GaskinG.L. PershingS. ColeT.S. ShahN.H. Predictive modeling of risk factors and complications of cataract surgery.Eur. J. Ophthalmol.201626432833710.5301/ejo.5000706 26692059
     [Google Scholar]
  132. ChagantiS. NabarK.P. NelsonK.M. MawnL.A. LandmanB.A. Phenotype analysis of early risk factors from electronic medical records improves image-derived diagnostic classifiers for optic nerve pathology.Proc SPIE Int Soc Opt Eng20171013899105
     [Google Scholar]
  133. FraccaroP. NicoloM. BonettoM. Combining macula clinical signs and patient characteristics for age-related macular degenera-tion diagnosis: A machine learning approach.BMC Ophthalmol.20151511010.1186/1471‑2415‑15‑10 25623470
     [Google Scholar]
  134. SalehE. BłaszczyńskiJ. MorenoA. Learning ensemble classifiers for diabetic retinopathy assessment.Artif. Intell. Med.201885506310.1016/j.artmed.2017.09.006 28993124
     [Google Scholar]
  135. How 11 Big Pharma companies are using AI. 20232023Available from:https://www.pharmaceuticalprocessingworld.com/ai-pharma-drug-development-billion-opportunity/#:~text=Below%2C%20discover%20how%2011%20Big,discovery%2C%20clinical%20trials%20and%20manufacturing
  136. Exscientia and Sanofi establish strategic research collaboration to develop AI-driven pipeline of precisionengineered medicines2022Available from:https://www.sanofi.com/en/media-room/press-releases/2022/2022-01-07-06-00-00-2362917
  137. Happify health and sanofi sign global agreement to bring prescription digital mental health therapeutics to individuals with multiple sclerosis2019Available from:https://www.prnewswire.com/news-releases/happify-health-and-sanofi-sign-global-agreement-to-bring-prescription-digital-mental-health-therapeutics-to-individuals-with-multiple-sclerosis-300918901.html
  138. Patient Safety at Sanofi Patient Safety at SanofiAvailable from:https://www.sanofi.com/en/your-health/patient-engagement/patient-safety-at-sanofi
  139. IBM and Pfizer to accelerate immuno-oncology research with watson for drug discovery2016Available fromhttps://www.pfizer.com/news/press-release/press-release-detail/ibm_and_pfizer_to_accelerate_immuno_oncology_research_with_watson_for_drug_discovery
  140. Data and AI are helping to get medicines to patients faster2022Available from:https://www.ijert.org/ai-in-healthcare-enhancing-patient-engagement-through-virtual-assistants
  141. Roche receives FDA approval for the first companion diagnostic to identify patients with HER2-ultralow metastatic breast cancer eligible for ENHERTU2022Available from:https://diagnostics.roche.com/us/en/home.html
  142. Tempest therapeutics announces clinical collaboration with roche to advance TPST-1120 into a randomized combination study in first-line hepatocellular carcinoma2021Available from:https://ir.tempesttx.com/news-releases/news-release-details/tempest-therapeutics-announces-clinical-collaboration-roche
  143. Novartis empowers scientists with AI to speed the discovery and development of breakthrough medicines2021Available from:https://news.microsoft.com/source/features/digital-transformation/novartis-empowers-scientists-ai-speed-discovery-development-breakthrough-medicines/
  144. Biofourmis forms alliance with global healthcare company to offer digital medicine for patients with heart failure2019Available from:https://www.biofourmis.com/news-insights/biofourmis-to-offer-digital-medicine-heart-failure-patients
  145. AstraZeneca starts artificial intelligence collaboration to accelerate drug discovery2019Available from:https://www.astrazeneca.com/media-centre/press-releases/2019/astrazeneca-starts-artificial-intelligence-collaboration-to-accelerate-drug-discovery-30042019.html#
  146. Real-world evidence at ECCMID 2024 further substantiate the continued significant and disproportionate risk of severe COVID-19 facing immunocompromised individuals2024Available from:https://www.astrazeneca.com/media-centre/medical-releases/real-world-evidence-eccmid-2024-further-substantiate-continued-significant-dispropor- tionate-risk-severe-covid-19-facing-immunocompromised-individuals.html
  147. A new era in surgery is coming2024Available from:https://thenext.jnjmedtech.com/surgical-robotics
  148. Johnson Johnson logo2025Available from:https://www.jnj.com/
  149. Merck enters two strategic collaborations to strengthen AIdriven drug discovery.2023Available from:https://www.merckgroup.com/en/news/two-ai-partnerships-in-healthcare-20-09-2023.html
  150. Merck Research Laboratories Merck Research Laboratories:Where extraordinary breakthroughs happen.2025Available from:https://www.merck.com/research/
    [Google Scholar]
  151. How the 1st pharma chat bot came to life2017Available from:https://www.chiefhealthcareexecutive.com/view/how-the-1st-pharma-chat-bot-came-to-life
  152. HasanM.K. GhazalT.M. SaeedR.A. A review on security threats, vulnerabilities, and counter measures of 5G enabled Internet‐of‐Medical‐Things.IET Commun.202216542143210.1049/cmu2.12301
     [Google Scholar]
  153. HimeurY. SohailS.S. BensaaliF. AmiraA. AlazabM. Latest trends of security and privacy in recommender systems: A comprehen-sive review and future perspectives.Comput. Secur.202211810274610.1016/j.cose.2022.102746
     [Google Scholar]
  154. KhalidN. QayyumA. BilalM. Al-FuqahaA. QadirJ. Privacy-preserving artificial intelligence in healthcare: Techniques and applica-tions.Comput. Biol. Med.202315810684810.1016/j.compbiomed.2023.106848 37044052
     [Google Scholar]
  155. van de SandeD. Van GenderenM.E. SmitJ.M. Developing, implementing and governing artificial intelligence in medicine: A step-by-step approach to prevent an artificial intelligence winter.BMJ Health Care Inform.2022291e10049510.1136/bmjhci‑2021‑100495 35185012
     [Google Scholar]
  156. WiensJ. SariaS. SendakM. Do no harm: A roadmap for responsible machine learning for health care.Nat. Med.20192591337134010.1038/s41591‑019‑0548‑6 31427808
     [Google Scholar]
  157. ReddyS. RogersW. MakinenV.P. Evaluation framework to guide implementation of AI systems into healthcare settings.BMJ Health Care Inform.2021281e10044410.1136/bmjhci‑2021‑100444 34642177
     [Google Scholar]
  158. Decide-AI Decide-AI: New reporting guidelines to bridge the development-to-implementation gap in clinical artificial intelligence.Nat. Med.202127218618710.1038/s41591‑021‑01229‑5 33526932
     [Google Scholar]
  159. LiuX. Cruz RiveraS. MoherD. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension.Lancet Digit. Health2020210e537e54810.1016/S2589‑7500(20)30218‑1 33328048
     [Google Scholar]
  160. WolffR.F. MoonsK.G.M. RileyR.D. PROBAST Group†. PROBAST: A tool to assess the risk of bias and applicability of predic-tion model studies.Ann. Intern. Med.20191701515810.7326/M18‑1376 30596875
     [Google Scholar]
  161. MonganJ. MoyL. KahnC.E.Jr Checklist for artificial intelligence in medical imaging (CLAIM): A guide for authors and reviewers.Radiol. Artif. Intell.202022e20002910.1148/ryai.2020200029 33937821
     [Google Scholar]
  162. GooldS.D. LipkinM.Jr The doctor-patient relationship.J. Gen. Intern. Med.199914S1Suppl. 1S26S3310.1046/j.1525‑1497.1999.00267.x 9933492
     [Google Scholar]
  163. LuptonD. M-health and health promotion: The digital cyborg and surveillance society.Soc. Theory Health201210322924410.1057/sth.2012.6
     [Google Scholar]
  164. KluttzD. MulliganD.K. Automated decision support technologies and the legal profession.SSRN Electron J20193438539010.2139/ssrn.3443063
     [Google Scholar]
  165. RichardsonJ.P. CurtisS. SmithC. A framework for examining patient attitudes regarding applications of artificial intelligence in healthcare.Digit. Health202282055207622108908410.1177/20552076221089084 35355806
     [Google Scholar]
  166. RichardsonJ.P. SmithC. CurtisS. Patient apprehensions about the use of artificial intelligence in healthcare.NPJ Digit. Med.20214114010.1038/s41746‑021‑00509‑1 34548621
     [Google Scholar]
  167. KovarikC.L. Patient perspectives on the use of artificial intelligence.JAMA Dermatol.2020156549349410.1001/jamadermatol.2019.5013 32159724
     [Google Scholar]
  168. McCraddenM.D. BabaA. SahaA. Ethical concerns around use of artificial intelligence in health care research from the perspec-tive of patients with meningioma, caregivers and health care providers: A qualitative study.CMAJ Open202081E90E9510.9778/cmajo.20190151 32071143
     [Google Scholar]
  169. GundersenT. BærøeK. The future ethics of artificial intelligence in medicine: Making sense of collaborative models.Sci. Eng. Ethics20222821710.1007/s11948‑022‑00369‑2 35362822
     [Google Scholar]
  170. BjerringJ.C. BuschJ. Artificial intelligence and patient-centered decision-making.Philos. Technol.202134234937110.1007/s13347‑019‑00391‑6
     [Google Scholar]
  171. AmannJ. BlasimmeA. VayenaE. FreyD. MadaiV.I. Explainability for artificial intelligence in healthcare: A multidisciplinary perspec-tive.BMC Med. Inform. Decis. Mak.202020131010.1186/s12911‑020‑01332‑6
     [Google Scholar]
  172. KerasidouA. Artificial intelligence and the ongoing need for empathy, compassion and trust in healthcare.Bull. World Health Organ.202098424525010.2471/BLT.19.237198 32284647
     [Google Scholar]
  173. McDougallR.J. Computer knows best? The need for value-flexibility in medical AI.J. Med. Ethics201945315616010.1136/medethics‑2018‑105118 30467198
     [Google Scholar]
  174. WalshS. de JongE.E.C. van TimmerenJ.E. Decision support systems in oncology.JCO Clin. Cancer Inform.2019331910.1200/CCI.18.00001 30730766
     [Google Scholar]
  175. BirchJ. CreelK.A. JhaA.K. PlutynskiA. Clinical decisions using AI must consider patient values.Nat. Med.202228222923210.1038/s41591‑021‑01624‑y 35102337
     [Google Scholar]
  176. ButteA.J. Trials and tribulations—11 reasons why we need to promote clinical trials data sharing.JAMA Netw. Open202141e203504310.1001/jamanetworkopen.2020.35043 33507252
     [Google Scholar]
  177. Gomez-CabreroD AbugessaisaI MaierD Data integration in the era of omics: Current and future challengesBMC Syst Biol20148Suppl 2(Suppl. 2):I110.1186/1752‑0509‑8‑S2‑I125032990
     [Google Scholar]
  178. AhmadO.F. StoyanovD. LovatL.B. Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues.Techniques and Innovations in Gastrointestinal Endoscopy2020222808410.1016/j.tgie.2019.150636
     [Google Scholar]
  179. ChenJ. SeeK.C. Artificial intelligence for COVID-19: Rapid review.J. Med. Internet Res.20202210e2147610.2196/21476 32946413
     [Google Scholar]
  180. HeJ. BaxterS.L. XuJ. XuJ. ZhouX. ZhangK. The practical implementation of artificial intelligence technologies in medicine.Nat. Med.2019251303610.1038/s41591‑018‑0307‑0 30617336
     [Google Scholar]
  181. PesapaneF. VolontéC. CodariM. SardanelliF. Artificial intelligence as a medical device in radiology: Ethical and regulatory issues in Europe and the United States.Insights Imaging20189574575310.1007/s13244‑018‑0645‑y 30112675
     [Google Scholar]
  182. KorinekM.A. SchindlerM.M. StiglitzJ. Technological progress, artificial intelligence, and inclusive growth.2021Available from:https://www.imf.org/en/Publications/WP/Issues/2021/06/11/Technological-Progress-Artificial-Intelligence-and-Inclusive-Growth-460695
    [Google Scholar]
  183. UdegbeF.C. EbulueO.R. EbulueC.C. EkesiobiC.S.A. I’s impact on personalized medicine: Tailoring treatments for improved health outcomes.Eng Sci Technol J2024541386139410.51594/estj.v5i4.1040
     [Google Scholar]
  184. AI revolutionizes precision medicine: Benefits, Case study and future trends2024Available from:https://www.intuz.com/blog/generative-ai-in-precision-medicine
  185. JianY. PasquierM. SagahyroonA. AloulF. A Machine Learning approach to predicting diabetes complications.Health Care20219121712
     [Google Scholar]
  186. FanY. LongE. CaiL. CaoQ. WuX. TongR. Machine learning approaches to predict risks of diabetic complications and poor glyce-mic control in nonadherent type 2 diabetes.Front. Pharmacol.20211266595110.3389/fphar.2021.665951 34239440
     [Google Scholar]
  187. YinJ. NgiamK.Y. TeoH.H. Role of artificial intelligence applications in real-life clinical practice: Systematic review.J. Med. Internet Res.2021234e2575910.2196/25759 33885365
     [Google Scholar]
  188. AttiaZ.I. KapaS. Lopez-JimenezF. Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electro-cardiogram.Nat. Med.2019251707410.1038/s41591‑018‑0240‑2 30617318
     [Google Scholar]
  189. AttiaZ.I. KapaS. YaoX. Prospective validation of a deep learning electrocardiogram algorithm for the detection of left ventricular systolic dysfunction.J. Cardiovasc. Electrophysiol.201930566867410.1111/jce.13889 30821035
     [Google Scholar]
  190. AlsharqiM. WoodwardW.J. MumithJ.A. MarkhamD.C. UptonR. LeesonP. Artificial intelligence and echocardiography.Echo Res. Pract.201854R115R12510.1530/ERP‑18‑0056 30400053
     [Google Scholar]
  191. WengS.F. RepsJ. KaiJ. GaribaldiJ.M. QureshiN. Can machine-learning improve cardiovascular risk prediction using routine clinical data?PLoS One2017124e017494410.1371/journal.pone.0174944 28376093
     [Google Scholar]
  192. Regional strategy for patient safety in the WHO South-East Asia Region (2016-2025)2015Available from:https://www.who.int/publications/i/item/9789290224921
  193. Patient safety incident reporting and learning systems: Technical report and guidance2020Available from:https://www.who.int/publications/i/item/9789240010338
  194. BahlM. BarzilayR. YedidiaA.B. LocascioN.J. YuL. LehmanC.D. High-risk breast lesions: A machine learning model to predict pathologic upgrade and reduce unnecessary surgical excision.Radiology2018286381081810.1148/radiol.2017170549 29039725
     [Google Scholar]
  195. GuanM. ChoS. PetroR. ZhangW. PascheB. TopalogluU. Natural language processing and recurrent network models for identifying genomic mutation-associated cancer treatment change from patient progress notes.JAMIA Open20192113914910.1093/jamiaopen/ooy061 30944913
     [Google Scholar]
  196. LiQ. ZhaoK. BustamanteC.D. MaX. WongW.H. Xrare: A machine learning method jointly modeling phenotypes and genetic evi-dence for rare disease diagnosis.Genet. Med.20192192126213410.1038/s41436‑019‑0439‑8 30675030
     [Google Scholar]
  197. ChenH. EngkvistO. WangY. OlivecronaM. BlaschkeT. The rise of deep learning in drug discovery.Drug Discov. Today20182361241125010.1016/j.drudis.2018.01.039 29366762
     [Google Scholar]
  198. Sahli CostabalF. MatsunoK. YaoJ. PerdikarisP. KuhlE. Machine learning in drug development: Characterizing the effect of 30 drugs on the QT interval using Gaussian process regression, sensitivity analysis, and uncertainty quantification.Comput. Methods Appl. Mech. Eng.201934831333310.1016/j.cma.2019.01.033 32863454
     [Google Scholar]
  199. EkinsS. PuhlA.C. ZornK.M. Exploiting machine learning for end-to-end drug discovery and development.Nat. Mater.201918543544110.1038/s41563‑019‑0338‑z 31000803
     [Google Scholar]
  200. JiangF. JiangY. ZhiH. Artificial intelligence in healthcare: Past, present and future.Stroke Vasc. Neurol.20172423024310.1136/svn‑2017‑000101 29507784
     [Google Scholar]
  201. BanerjeeI. LiK. SeneviratneM. Weakly supervised natural language processing for assessing patient-centered outcome follow-ing prostate cancer treatment.JAMIA Open20192115015910.1093/jamiaopen/ooy057 31032481
     [Google Scholar]
  202. CiervoJ. ShenS.C. StallcupK. A new risk and issue management system to improve productivity, quality, and compliance in clinical trials.JAMIA Open20192221622110.1093/jamiaopen/ooz006 31984356
     [Google Scholar]
  203. RonquilloJ.G. Erik WinterhollerJ. CwiklaK. SzymanskiR. LevyC. Health IT, hacking, and cybersecurity: National trends in data breaches of protected health information.JAMIA Open201811151910.1093/jamiaopen/ooy019 31984315
     [Google Scholar]
  204. DalalA.K. FullerT. GarabedianP. Systems engineering and human factors support of a system of novel EHR-integrated tools to prevent harm in the hospital.J. Am. Med. Inform. Assoc.201926655356010.1093/jamia/ocz002 30903660
     [Google Scholar]
  205. QuaziS. Artificial intelligence and machine learning in precision and genomic medicine.Med. Oncol.202239812010.1007/s12032‑022‑01711‑1 35704152
     [Google Scholar]
  206. XuJ. YangP. XueS. Translating cancer genomics into precision medicine with artificial intelligence: Applications, challenges and future perspectives.Hum. Genet.2019138210912410.1007/s00439‑019‑01970‑5 30671672
     [Google Scholar]
  207. RumsfeldJ.S. JoyntK.E. MaddoxT.M. Big data analytics to improve cardiovascular care: Promise and challenges.Nat. Rev. Cardiol.201613635035910.1038/nrcardio.2016.42 27009423
     [Google Scholar]
  208. KakS.C. Quantum neural computing.Adv. Imaging Electron Phys.19959425931310.1016/S1076‑5670(08)70147‑2
     [Google Scholar]
  209. ReyesM. MeierR. PereiraS. On the interpretability of artificial intelligence in radiology: Challenges and opportunities.Radiol. Artif. Intell.202023e19004310.1148/ryai.2020190043 32510054
     [Google Scholar]
  210. BohrA. MemarzadehK. The rise of artificial intelligence in healthcare applications.Artificial Intelligence in healthcare.Academic Press2020256010.1016/B978‑0‑12‑818438‑7.00002‑2
     [Google Scholar]
  211. AhujaA.S. The impact of artificial intelligence in medicine on the future role of the physician.PeerJ20197e770210.7717/peerj.7702 31592346
     [Google Scholar]
  212. LeeD. YoonS.N. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges.Int. J. Environ. Res. Public Health202118127110.3390/ijerph18010271 33401373
     [Google Scholar]
  213. BarnesR. ZvarikovaK. Artificial intelligence-enabled wearable medical devices, clinical and diagnostic decision support systems, and Internet of Things-based healthcare applications in COVID-19 prevention, screening, and treatment.Am. J. Med. Res20218292210.22381/ajmr8220211
     [Google Scholar]
  214. JavaidM. HaleemA. SinghR.P. RabS. SumanR. KhanI.H. Evolutionary trends in progressive cloud computing based healthcare: Ideas, enablers, and barriers.Int J Cogn Comput Eng2022312413510.1016/j.ijcce.2022.06.001
     [Google Scholar]
  215. PrabhodK.J. The role of Artificial Intelligence in reducing healthcare costs and improving operational efficiency.QJETI2024924759
     [Google Scholar]
/content/journals/cppm/10.2174/0118756921371741250410152407
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Artificial Intelligence: The New Doctor in Personalized Medicine
Curr. Pharm. and. Per. Med. 22, E18756921371741 (2025); https://doi.org/10.2174/0118756921371741250410152407
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  • Article Type:     Review Article
Keyword(s): Artificial intelligence; lipidomics; machine learning; personalized medicine; predictive analysis; robotics

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