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Volume 2, Issue 1
  • ISSN: 2950-3752
  • E-ISSN: 2950-3760

Abstract

Artificial intelligence (AI) falls under the purview of computer technology, which analyzes complex data and helps solve problems in different segments. Big Data, Machine Learning, and AI are currently being used by the major pharmaceutical industries to minimize time and costs and increase possibilities. Artificial intelligence is used in the pharmaceutical industry in diverse ways, such as drug discovery and development, clinical trials, disease diagnosis, and different stages in pharmaceutical manufacturing, data analysis, and supply management. Most of the cost and time are involved in drug discovery and clinical trials. Artificial intelligence can minimize human error in data processing, documentation, data integrity issues, and data selection throughout the journey. It works in descriptive, diagnostic, predictive, and prescriptive mode. Major pharmaceutical conglomerates like Pfizer, Roche, Novartis, and Johnson & Johnson have already applied Artificial Intelligence in different segments of pharmaceutical and medicinal science. Tech companies like IBM Watson, Catalia Health, Intel, Microsoft, and Google, in collaboration with pharmaceutical companies, are working in the different areas of drug discovery, early diagnosis, and personalized medicine. Further, AI finds application in the health sector for data management, scanning and evaluation of medical history reports, and finding optimum treatment strategies for chronic care patients. Though lots of research and development are being done on the utilization of artificial intelligence in the pharmaceutical industry, it is still in the nascent stage. This article is our endeavor to study, in detail, the present and future opportunities of machine learning and AI in the pharmaceutical industry as a whole.

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References

  1. MishraV. Artificial intelligence: the beginning of a new era in pharmacy profession.Asian J. Pharm.20181202
     [Google Scholar]
  2. AsimovI. Three laws of robotics. Asimov, I.Runaround19412
     [Google Scholar]
  3. KaulV. EnslinS. GrossS.A. History of artificial intelligence in medicine.Gastrointest. Endosc.202092480781210.1016/j.gie.2020.06.04032565184
     [Google Scholar]
  4. LiebmanM. The Role of Artificial Intelligence in Drug Discovery and Development.Chem. Int.2022441161910.1515/ci‑2022‑0105
     [Google Scholar]
  5. PaulD. SanapG. ShenoyS. KalyaneD. KaliaK. TekadeR.K. Artificial intelligence in drug discovery and development.Drug Discov. Today2021261809310.1016/j.drudis.2020.10.01033099022
     [Google Scholar]
  6. LeeD. YoonS.N. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges.Int. J. Environ. Res. Public Health202118127110.3390/ijerph1801027133401373
     [Google Scholar]
  7. SaxenaA.K. NessS. KhinvasaraT. The Influence of AI: The revolutionary effects of artificial intelligence in healthcare sector.J. Eng. Res. Rep.2024263496210.9734/jerr/2024/v26i31092
     [Google Scholar]
  8. GhafariM. MailmanD. HatamiP. PeytonT. YangL. DangW. A comparison of YOLO and mask-RCNN for detecting cells from microfluidic images in international conference on artificial intelligence in information and communication (ICAIIC).Jeju Island, KoreaRepublic of2022
     [Google Scholar]
  9. TranT.V. KhaleghianS. ZhaoJ. SartipiM. SIMCal: A high-performance toolkit for calibrating traffic simulation in IEEE BigData.Osaka, Japan2022
     [Google Scholar]
  10. SajedianA. EbrahimiM. JamialahmadiM. Two-phase Inflow performance relationship prediction using two artificial intelligence techniques: multi-layer perceptron versus genetic programming.Petrol. Sci. Technol.201230161725173610.1080/10916466.2010.509074
     [Google Scholar]
  11. BM Watson for Oncology.Available from: https://www.ibm.com/common/ssi/cgibin/ssialias?appname=skmwww&htmlfid=897%2FENUS5725-W51&infotype=DD&subtype=SM&mhsrc=ibmsearch_a&mh q=IBM%20WATSON%20ONcology#:~:text=IBM%20Watson%20for%20Oncology%2C%20software,Center%20physician
  12. GaurN. DharwadkarR. ThomasJ. Personalized therapy using deep learning advances. Deep Learning for Targeted Treatments: Transformation in Healthcare; Malviya, R.; Ghinea, G.; Dhanaraj, R.K.; Balusamy, B. SundramS. 202217119710.1002/9781119857983.ch6
     [Google Scholar]
  13. HaleemA. JavaidM. KhanI.H. Current status and applications of Artificial Intelligence (AI) in medical field: An overview.Curr. Med. Res. Pract.20199623123710.1016/j.cmrp.2019.11.005
     [Google Scholar]
  14. Deep Mind’s health team.Available from: https://www.deepmind.com/blog/deepminds-healthteam-joins-google-health [cited 2022 13 June].
  15. AI for Management of Medical Records!Available from: https://www.credo.health/technology/ai-for-management-ofmedical-records/
  16. IBM. Medical Sieve.Available from: https://researcher.watson.ibm.com/researcher/view_group.php?id=4384 [cited 2022 13 June].
     [Google Scholar]
  17. GulshanV. PengL. CoramM. StumpeM.C. WuD. NarayanaswamyA. VenugopalanS. WidnerK. MadamsT. CuadrosJ. KimR. RamanR. NelsonP.C. MegaJ.L. WebsterD.R. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA2016316222402241010.1001/jama.2016.1721627898976
     [Google Scholar]
  18. EL-Geneedy, M.; Moustafa, H.E.D.; Khalifa, F.; Khater, H.; AbdElhalim, E. An MRI-based deep learning approach for accurate detection of Alzheimer’s disease.Alex. Eng. J.20236321122110.1016/j.aej.2022.07.062
     [Google Scholar]
  19. BhosaleY.H. PatnaikK.S. PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates.Biomed. Signal Process. Control20238110444510.1016/j.bspc.2022.10444536466567
     [Google Scholar]
  20. LiC. ZhangY. WengY. WangB. LiZ. Natural language processing applications for computer-aided diagnosis in oncology.Diagnostics202313228610.3390/diagnostics1302028636673096
     [Google Scholar]
  21. NordinN. ZainolZ. Mohd NoorM.H. ChanL.F. An explainable predictive model for suicide attempt risk using an ensemble learning and Shapley Additive Explanations (SHAP) approach.Asian J. Psychiatr.20237910331610.1016/j.ajp.2022.10331636395702
     [Google Scholar]
  22. ChenY. LinY. XuX. DingJ. LiC. ZengY. XieW. HuangJ. Multi-domain medical image translation generation for lung image classification based on generative adversarial networks.Comput. Methods Programs Biomed.202322910720010.1016/j.cmpb.2022.10720036525713
     [Google Scholar]
  23. PaganoT.P. LoureiroR.B. LisboaF.V.N. PeixotoR.M. GuimarãesG.A.S. CruzG.O.R. AraujoM.M. SantosL.L. CruzM.A.S. OliveiraE.L.S. WinklerI. NascimentoE.G.S. Bias and unfairness in machine learning models: A systematic review on datasets, tools, fairness metrics, and identification and mitigation methods.Big Data and Cognitive Computing2023711510.3390/bdcc7010015
     [Google Scholar]
  24. LiX. JiangY. ZhangJ. LiM. LuoH. YinS. Lesion-attention pyramid network for diabetic retinopathy grading.Artif. Intell. Med.202212610225910.1016/j.artmed.2022.10225935346445
     [Google Scholar]
  25. Ghaffar NiaN. KaplanogluE. NasabA. Evaluation of artificial intelligence techniques in disease diagnosis and prediction.Discover Artificial Intelligence202331510.1007/s44163‑023‑00049‑5
     [Google Scholar]
  26. FeeneyT.J. SinhaM.S.A.I. Renaissance: Pharmaceuticals and diagnostic medicine.The George Washington Journal of Law and Technology (forthcoming 2025)Saint Louis U. Legal Studies Research Paper.2024Feb15(2024-04).
     [Google Scholar]
  27. JavanmardS. Revolutionizing Medical Practice: The impact of artificial intelligence (AI) on Healthcare.OA J Applied Sci Technol.2024210116
     [Google Scholar]
  28. MOLLY, THE VIRTUAL NURSE.Available from: http://adigaskell.org/2015/03/20/meetmolly-the-virtual-nurse/ [cited 2022 13 June].
  29. AiCure AiCure. The right dose for the right patient.Available from: https://aicure.com/ [cited 2022 13 June].
  30. Open AI Ecosystem.Available from: https://www.scientificamerican.com/article/open-aiecosystem-portends-a-personal-assistant-for-everyone/
  31. eInvoicing in The Netherlands. Available from: https://ec.europa.eu/digital-buildingblocks/wikis/display/DIGITAL/eInvoicing+in+The+Netherlands
  32. Chatbots. Medicine Delivery.Available from: https://hellotars.com/chatbottemplates/healthcare/Hk8N4h/medicine-ordering-chatbot
  33. Walgreen. Convenient virtual care.Available from: https://www.walgreens.com/findcare/category/acutetelehealth
  34. RazaM.A Artificial Intelligence (AI) in Pharmacy: An overview of innovations.Pharmacy practice and practice based research202213218
     [Google Scholar]
  35. Deep Genomics. Programming RNA Therapies Any Gene, Any Genetic Condition.Available from: https://www.deepgenomics.com[cited 2022 13 June].
  36. ShampoM.A. KyleR.A. Craig VenterJ. The human genome project.Mayo Clin. Proc.2011864e26e2710.4065/mcp.2011.016021584979
     [Google Scholar]
  37. The international conference on harmonization of technical requirements for registration of pharmaceuticals for human use (ICH);Quality Guideline Q8 Pharmaceutical Development2009
     [Google Scholar]
  38. PomerantsevA.L. RodionovaO.Y. Process analytical technology: A critical view of the chemometricians.J. Chemometr.201226629931010.1002/cem.2445
     [Google Scholar]
  39. EMA. Guideline on real time release testing (formerly Guideline on parametric release).European Medical Agency2012
     [Google Scholar]
  40. BarenjiR.V. AkdagY. YetB. OnerL. Cyber-physical-based PAT (CPbPAT) framework for Pharma 4.0.Int. J. Pharm.201956711844510.1016/j.ijpharm.2019.06.03631226474
     [Google Scholar]
  41. TilleyJ. Automation, Robotics, and the Factory of the Future.2017Available from: https://www. mckinsey.com/business-functions/operations/our-insights/automation-robotics-
    [Google Scholar]
  42. AndersonS. Making Medicines: A Brief History of Pharmacy and Pharmaceuticals.Pharmaceutical Press2005
     [Google Scholar]
  43. BerryI.R. NashR.A. Pharmaceutical process validation.Nash, Robert A., Wachter; Alfred, H., Ed.2003
     [Google Scholar]
  44. FDA. CDER Conversation: Assuring Drug Quality Around the Globe2019Available from: https://www.fda.gov/drugs/news-events-human-drugs/cder-conversation-assuring-drug
  45. FuhrT. GonceA. PositanoL. RuttenP. TepisV. Flawless—From Measuring Failure to Building Quality Robustness in Pharma.ILMcKinsey & Company Chicago2014
     [Google Scholar]
  46. JoshiA.V. Introduction to AI and ML.Machine learning and artificial intelligence. JoshiA.V. ChamSpringer International Publishing20203710.1007/978‑3‑030‑26622‑6_1
     [Google Scholar]
  47. Danish Medicines Agency (DKMA)Suggested criteria for using AI/ML algorithms in GxP.2021Available from: https://laegemiddelstyrelsen.dk/en/licensing/supervision-and-inspection/inspection-of-authorised-pharmaceuticalcompanies/usingaimlalgorithmsingxp/
    [Google Scholar]
  48. SalehinejadH. SankarS. BarfettJ. ColakE. ValaeeS. Recent advances in recurrent neural networks.arXiv:1801010782017
     [Google Scholar]
  49. DebnathB. ShakurM.S. BariA.B.M.M. SahaJ. PornaW.A. MishuM.J. IslamA.R.M.T. RahmanM.A. Assessing the critical success factors for implementing industry 4.0 in the pharmaceutical industry: Implications for supply chain sustainability in emerging economies.PLoS One2023186e028714910.1371/journal.pone.028714937319165
     [Google Scholar]
  50. TaoY. BaoJ. LiuQ. LiuL. ZhuJ. Application of deep-learning algorithm driven intelligent raman spectroscopy methodology to quality control in the manufacturing process of guanxinning tablets.Molecules20222720696910.3390/molecules2720696936296563
     [Google Scholar]
  51. NagyB. PetraD. GalataD.L. DémuthB. BorbásE. MarosiG. NagyZ.K. FarkasA. Application of artificial neural networks for Process Analytical Technology-based dissolution testing.Int. J. Pharm.201956711846410.1016/j.ijpharm.2019.11846431252145
     [Google Scholar]
  52. CarterA. BriensL. An application of deep learning to detect process upset during pharmaceutical manufacturing using passive acoustic emissions.Int. J. Pharm.20185521-223524010.1016/j.ijpharm.2018.08.05230253210
     [Google Scholar]
  53. AksuB. ParadkarA. de MatasM. ÖzerÖ. GüneriT. YorkP. Quality by design approach: application of artificial intelligence techniques of tablets manufactured by direct compression.AAPS PharmSciTech20121341138114610.1208/s12249‑012‑9836‑x22956056
     [Google Scholar]
  54. MoghaddamM.G. AhmadF.B.H. BasriM. RahmanM.B.A. Artificial neural network modeling studies to predict the yield of enzymatic synthesis of betulinic acid ester.Electron. J. Biotechnol.2010133410.2225/vol13‑issue3‑fulltext‑9
     [Google Scholar]
  55. ValizadehH. PourmahmoodM. MojarradJ.S. NematiM. Zakeri-MilaniP. Application of artificial intelligent tools to modeling of glucosamine preparation from exoskeleton of shrimp.Drug Dev. Ind. Pharm.200935439640710.1080/0363904080242208819016101
     [Google Scholar]
  56. FrancoV.G. PerínJ.C. MantovaniV.E. GoicoecheaH.C. Monitoring substrate and products in a bioprocess with FTIR spectroscopy coupled to artificial neural networks enhanced with a genetic-algorithm-based method for wavelength selection.Talanta20066831005101210.1016/j.talanta.2005.07.00318970424
     [Google Scholar]
  57. HasaniM. MoloudiM. Application of principal component-artificial neural network models for simultaneous determination of phenolic compounds by a kinetic spectrophotometric method.J. Hazard. Mater.2008157116116910.1016/j.jhazmat.2007.12.09618272286
     [Google Scholar]
  58. SalamiH. McDonaldM.A. BommariusA.S. RousseauR.W. GroverM.A. In situ imaging combined with deep learning for crystallization process monitoring: Application to cephalexin production.Org. Process Res. Dev.20212571670167910.1021/acs.oprd.1c00136
     [Google Scholar]
  59. Image based measurement of population growth rate for L-glutamic acid crystallization. Chen, S.; Liu, T.; Xu, D.; Huo, Y. Yang, Y., Eds.;Chinese Control Conference (CCC)2019273010.23919/ChiCC.2019.8866441
     [Google Scholar]
  60. DouY. SunY. RenY. RenY. Artificial neural network for simultaneous determination of two components of compound paracetamol and diphenhydramine hydrochloride powder on NIR spectroscopy.Anal. Chim. Acta20055281556110.1016/j.aca.2004.10.050
     [Google Scholar]
  61. WangB. LiuG. LiuS. FeiQ. RenY. Orthogonal projection to latent structures combined with artificial neural network for quantitative analysis of phenoxymethylpenicillin potassium powder.Vib. Spectrosc.200951219920410.1016/j.vibspec.2009.04.007
     [Google Scholar]
  62. MujumdarA. RobiP.S. MalikM. HorioM. Artificial neural network (ANN) model for prediction of mixing behavior of granular flows.Int. J. Comput. Methods Eng. Sci. Mech.20078314915810.1080/15502280701252495
     [Google Scholar]
  63. BehzadiS.S. KlockerJ. HüttlinH. WolschannP. ViernsteinH. Validation of fluid bed granulation utilizing artificial neural network.Int. J. Pharm.20052911-213914810.1016/j.ijpharm.2004.07.05115707740
     [Google Scholar]
  64. MurtoniemiE. YliruusiJ. KinnunenP. MerkkuP. LeiviskäK. The advantages by the use of neural networks in modelling the fluidized bed granulation process.Int. J. Pharm.1994108215516410.1016/0378‑5173(94)90327‑1
     [Google Scholar]
  65. BehzadiS.S. PrakasvudhisarnC. KlockerJ. WolschannP. ViernsteinH. Comparison between two types of Artificial Neural Networks used for validation of pharmaceutical processes.Powder Technol.2009195215015710.1016/j.powtec.2009.05.025
     [Google Scholar]
  66. SampatC. RamachandranR. Identification of granule growth regimes in high shear wet granulation processes using a physics- constrained neural network.Processes20219573710.3390/pr9050737
     [Google Scholar]
  67. PetrovićJ. ChansanrojK. MeierB. IbrićS. BetzG. Analysis of fluidized bed granulation process using conventional and novel modeling techniques.Eur. J. Pharm. Sci.201144322723410.1016/j.ejps.2011.07.01321839830
     [Google Scholar]
  68. KortebyY. KristóK. SoványT. RegdonG.Jr Use of machine learning tool to elucidate and characterize the growth mechanism of an in-situ fluid bed melt granulation.Powder Technol.201833128629510.1016/j.powtec.2018.03.052
     [Google Scholar]
  69. IsmailH.Y. SinghM. DarwishS. KuhsM. ShirazianS. CrokerD.M. KhraishehM. AlbadarinA.B. WalkerG.M. Developing ANN-Kriging hybrid model based on process parameters for prediction of mean residence time distribution in twin-screw wet granulation.Powder Technol.201934356857710.1016/j.powtec.2018.11.060
     [Google Scholar]
  70. IsmailH.Y. SinghM. ShirazianS. AlbadarinA.B. WalkerG.M. Development of high-performance hybrid ANN-finite volume scheme (ANN-FVS) for simulation of pharmaceutical continuous granulation.Chem. Eng. Res. Des.202016332032610.1016/j.cherd.2020.09.002
     [Google Scholar]
  71. RantanenJ. RäsänenE. AntikainenO. MannermaaJ.P. YliruusiJ. In-line moisture measurement during granulation with a four-wavelength near-infrared sensor: an evaluation of process-related variables and a development of non-linear calibration model.Chemom. Intell. Lab. Syst.2001561515810.1016/S0169‑7439(01)00108‑3
     [Google Scholar]
  72. KachrimanisK. KaramyanV. MalamatarisS. Artificial neural networks (ANNs) and modeling of powder flow.Int. J. Pharm.20032501132310.1016/S0378‑5173(02)00528‑812480269
     [Google Scholar]
  73. ZawbaaH.M. SchianoS. Perez-GandarillasL. GrosanC. MichrafyA. WuC.Y. Computational intelligence modelling of pharmaceutical tabletting processes using bio-inspired optimization algorithms.Adv. Powder Technol.201829122966297710.1016/j.apt.2018.11.008
     [Google Scholar]
  74. BeličA. ŠkrjancI. BožičD.Z. KarbaR. VrečerF. Minimisation of the capping tendency by tableting process optimisation with the application of artificial neural networks and fuzzy models.Eur. J. Pharm. Biopharm.200973117217810.1016/j.ejpb.2009.05.00519465122
     [Google Scholar]
  75. Atomwise. Artificial Intelligence for Drug Discovery.Available from: https://www.atomwise.com/
  76. ArnoldC. Inside the nascent industry of AI-designed drugs.Nat. Med.20232961292129510.1038/s41591‑023‑02361‑037264208
     [Google Scholar]
  77. How Artificial Intelligence is Revolutionizing Drug Discovery.Available from: https://blog.petrieflom.law.harvard.edu/2023/03/20/how-artificial-intelligence-is-revolutionizing-drug-discovery/
  78. ZhuH. Big data and artificial intelligence modeling for drug discovery.Annu. Rev. Pharmacol. Toxicol.202060157358910.1146/annurev‑pharmtox‑010919‑02332431518513
     [Google Scholar]
  79. ChanH.C.S. ShanH. DahounT. VogelH. YuanS. Advancing drug discovery via artificial intelligence.Trends Pharmacol. Sci.201940859260410.1016/j.tips.2019.06.00431320117
     [Google Scholar]
  80. PereiraJ.C. CaffarenaE.R. dos SantosC.N. Boosting docking-based virtual screening with deep learning.J. Chem. Inf. Model.201656122495250610.1021/acs.jcim.6b0035528024405
     [Google Scholar]
  81. CiallellaH.L. ZhuH. Advancing computational toxicology in the big data era by artificial intelligence: data-driven and mechanism-driven modeling for chemical toxicity.Chem. Res. Toxicol.201932453654710.1021/acs.chemrestox.8b0039330907586
     [Google Scholar]
  82. BrownN. In Silico Medicinal Chemistry: Computational Methods to Support Drug Design.Royal Society of Chemistry20151232
     [Google Scholar]
  83. Available from: https://deepmind.com/blog/alphafold
  84. PinheiroF. SantosJ. VenturaS. AlphaFold and the amyloid landscape.J. Mol. Biol.20214332016705910.1016/j.jmb.2021.16705934023402
     [Google Scholar]
  85. Neural Graph Fingerprints.Available from: https://github.com/HIPS/neural-fingerprint
  86. croningp/ChemputerSoftware: Chemputer first release. Available from: https://zenodo.org/record/1481731
  87. LuoM. FengY. WangT. GuanJ. Micro-/nanorobots at work in active drug delivery.Adv. Funct. Mater.20182825170610010.1002/adfm.201706100
     [Google Scholar]
  88. FuJ. YanH. Controlled drug release by a nanorobot.Nat. Biotechnol.201230540740810.1038/nbt.220622565965
     [Google Scholar]
  89. CalzolariD. BruschiS. CoquinL. SchofieldJ. FealaJ.D. ReedJ.C. McCullochA.D. PaternostroG. Search algorithms as a framework for the optimization of drug combinations.PLOS Comput. Biol.2008412e100024910.1371/journal.pcbi.100024919112483
     [Google Scholar]
  90. WilsonB. KmG. Artificial intelligence and related technologies enabled nanomedicine for advanced cancer treatment.Nanomedicine202015543343510.2217/nnm‑2019‑036631997697
     [Google Scholar]
  91. TsigelnyI.F. Artificial intelligence in drug combination therapy.Brief. Bioinform.20192041434144810.1093/bib/bby00429438494
     [Google Scholar]
  92. AbràmoffM.D. LavinP.T. BirchM. ShahN. FolkJ.C. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices.NPJ Digit. Med.2018113910.1038/s41746‑018‑0040‑631304320
     [Google Scholar]
  93. KanagasingamY. XiaoD. VignarajanJ. PreethamA. Tay-KearneyM.L. MehrotraA. Evaluation of artificial intelligence-based grading of diabetic retinopathy in primary care.JAMA Netw. Open201815e18266510.1001/jamanetworkopen.2018.266530646178
     [Google Scholar]
  94. BellemoV. LimZ.W. LimG. NguyenQ.D. XieY. YipM.Y.T. HamzahH. HoJ. LeeX.Q. HsuW. LeeM.L. MusondaL. ChandranM. Chipalo-MutatiG. MumaM. TanG.S.W. SivaprasadS. MenonG. WongT.Y. TingD.S.W. Artificial intelligence using deep learning to screen for referable and vision-threatening diabetic retinopathy in Africa: A clinical validation study.Lancet Digit. Health201911e35e4410.1016/S2589‑7500(19)30004‑433323239
     [Google Scholar]
  95. LiuY. KohlbergerT. NorouziM. DahlG.E. SmithJ.L. MohtashamianA. OlsonN. PengL.H. HippJ.D. StumpeM.C. Artificial intelligence-based breast cancer nodal metastasis detection: Insights into the black box for pathologists.Arch. Pathol. Lab. Med.2019143785986810.5858/arpa.2018‑0147‑OA30295070
     [Google Scholar]
  96. SteinerD.F. MacDonaldR. LiuY. TruszkowskiP. HippJ.D. GammageC. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer.Am. J. Surg. Pathol.201842121636164610.1097/PAS.0000000000001151
     [Google Scholar]
  97. LindseyR. DaluiskiA. ChopraS. LachapelleA. MozerM. SicularS. HanelD. GardnerM. GuptaA. HotchkissR. PotterH. Deep neural network improves fracture detection by clinicians.Proc. Natl. Acad. Sci.201811545115911159610.1073/pnas.180690511530348771
     [Google Scholar]
  98. NestorB. McDermottM.B.A. ChauhanG. NaumannT. HughesM.C. GoldenbergA. Rethinking clinical prediction: why machine learning must consideryear of care and feature aggregation. In: Machine Learning for Health (ML4H):NeurIPS. arXiv:1811.125832018Available from: https://arxiv.org/abs/1811.12583
    [Google Scholar]
  99. CrawfordK. CaloR. There is a blind spot in AI research.Nature2016538762531131310.1038/538311a27762391
     [Google Scholar]
  100. BarocasS. SelbstA.D. Big Data’s Disparate Impact.SSRN Electr. J.201667110.2139/ssrn.2477899
     [Google Scholar]
  101. FinlaysonS.G. BowersJ.D. ItoJ. ZittrainJ.L. BeamA.L. KohaneI.S. Adversarial attacks on medical machine learning.Science201936364331287128910.1126/science.aaw439930898923
     [Google Scholar]
  102. MandelJ.C. KredaD.A. MandlK.D. KohaneI.S. RamoniR.B. SMART on FHIR: A standards-based, interoperable apps platform for electronic health records.J. Am. Med. Inform. Assoc.201623589990810.1093/jamia/ocv18926911829
     [Google Scholar]
  103. HershW.R. WeinerM.G. EmbiP.J. LoganJ.R. PayneP.R.O. BernstamE.V. LehmannH.P. HripcsakG. HartzogT.H. CiminoJ.J. SaltzJ.H. Caveats for the use of operational electronic health record data in comparative effectiveness research.Med. Care2013518 Supplement 8 Suppl 3S30S3710.1097/MLR.0b013e31829b1dbd23774517
     [Google Scholar]
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Artificial Intelligence in Modernization of Pharmaceutical and Healthcare Industry: A Review
Curr Arti Intell 2, E270624231404 (2024); https://doi.org/10.2174/0129503752293246240614073740
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  • Article Type:     Review Article
Keyword(s): Artificial intelligence; cell biology; drug discovery; future prospects; healthcare; pharmaceutical industry

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