ARTIFICIAL INTELLIGENCE IN DRUG DEVELOPMENT, CLINICAL TRIALS, AND HEALTHCARE
Abstract
The use of artificial intelligence (AI) in drug development, clinical trials, and clinical practice represents a transformative advancement in healthcare. AI technologies offer unprecedented capabilities to analyze vast datasets, identify patterns, and generate actionable insights, thereby revolutionizing various aspects of the healthcare ecosystem. This review aims to offer a thorough overview of current research on AI applications in healthcare. In drug development, AI-driven approaches rationalize the process of identifying potential therapeutic compounds, accelerating the route from discovery to market approval. Within clinical trials, AI-powered analytics optimize trial design, reduce sample size, patient recruitment, and data analysis, increasing statistical power and efficiency. Moreover, in clinical practice AI applications empower healthcare providers with decision support systems, personalized treatment recommendations, and predictive analytics, leading to more effective and personalized patient care. While challenges such as ethical considerations and regulatory frameworks remain, the potential benefits of AI in driving medical innovation and improving patient outcomes are substantial, underlining the importance of continued research, collaboration, and responsible application of AI in healthcare.
References
Akbari H, Bakas S, Pisapia JM, Nasrallah MP, Rozycki M, Martinez-Lage M, et al. In Vivoevaluation of EGFRvIII Mutation in Primary Glioblastoma Patients via Complex Multiparametric MRI Signature. Neuro-Oncology 2018;20(8):1068–79. [CrossRef] [PubMed]
Amann J, Blasimme A, Vayena E, Frey D, Madai VI. Explainability for Artificial Intelligence in Healthcare: A Multidisciplinary Perspective. BMC Medical Informatics and Decision Making 2020;20(1):310. [CrossRef] [PubMed]
Aoyama T, Suzuki Y, Ichikawa H. Neural networks applied to structure-activity relationships. J Med Chem 1990;33(3):905–8. [CrossRef] [PubMed]
Arden NS, Fisher AC, Tyner K, Yu LX, Lee SL, Kopcha M. Industry 4.0 for pharmaceutical manufacturing: Preparing for the smart factories of the future. Int J Pharm 2021; 602:120554. [CrossRef] [PubMed]
Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-End Lung Cancer Screening with Three-Dimensional Deep Learning on Low-Dose Chest Computed Tomography. Nat Med 2019; 25(6):954–61. [CrossRef] [PubMed]
Athalye C, Arnaout R. Domain-guided data augmentation for deep learning on medical imaging. PLoS One 2023; 18(3): e0282532. [CrossRef] [PubMed]
Bakkar N, Kovalik T, Lorenzini I, Spangler S, Lacoste A, Sponaugle K, et al. Artificial intelligence in neurodegenerative disease research: use of IBM Watson to identify additional RNA-binding proteins altered in amyotrophic lateral sclerosis. Acta Neuropathol 2018;135(2):227-47. [CrossRef] [PubMed]
Basson R, Cerimele B, DeSante K, Howey D. Tmax: an unconfounded metric for rate of absorption in single dose bioequivalence studies. Pharm Res 1996; 13(2): 324–8. [CrossRef] [PubMed]
Bellini V, Guzzon M, Bigliardi B, Mordonini M, Filippelli S, Bignami E. Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization. Journal of Medical Systems 2019; 44 (1):20. [CrossRef] [PubMed]
Beneke F, Mackenrodt MO. Artificial Intelligence and Collusion. IIC - International Review of Intellectual Property and Competition Law 2018; 50(1): 109–34. [CrossRef]
Bielecki A. Models of Neurons and Perceptrons: Selected Problems and Challenges. 1st ed. Springer International Publishing; 2019. p. 156. [CrossRef]
Cao Z, Duan L, Yang G, Yue T, Chen Q. An Experimental Study on Breast Lesion Detection and Classification from Ultrasound Images Using Deep Learning Architectures. BMC Medical Imaging 2019;19(1):51. [CrossRef] [PubMed]
Chaddad A, Peng J, Xu J, Bouridane A. Survey of Explainable AI Techniques in Healthcare. Sensors (Basel) 2023;23(2):634. [CrossRef] [PubMed]
Chan HS, Shan H, Dahoun T, Vogel H, Yuan S. Advancing drug discovery via artificial intelligence. Trends Pharmacol Sci 2019;40(10):801. [CrossRef] [PubMed]
Chang P, Grinband J, Weinberg BD, Bardis M, Khy M, Cadena G, et al. Deep-Learning Convolutional Neural Networks Accurately Classify Genetic Mutations in Gliomas. AJNR Am J Neuroradiol 2018;39(7):1201–7. [CrossRef] [PubMed]
Checcucci E, Amparore D, De Luca S, Autorino R, Fiori C, Porpiglia F. Precision Prostate Cancer Surgery: An Overview of New Technologies and Techniques. Minerva Urol Nefrol 2019; 71(5): 487-501. [CrossRef] [PubMed]
Chen X, Lian C, Wang L, Deng H, Kuang T, Fung SH, et al. Diverse data augmentation for learning image segmentation with cross-modality annotations. Med Image Anal 2021; 71:102060. [CrossRef] [PubMed]
Cheney FW. The American Society of Anesthesiologists Closed Claims Project. Anesthesiology 1999; 91(2):552–6. [CrossRef] [PubMed]
Chollet F. Deep Learning with Python, Second Edition. Manning; 2nd edition (December 21, 2021).
Cicione A, De Nunzio C, Manno S, Damiano R, Posti A, Lima E, Tubaro A, Balloni F. An Update on Prostate Biopsy in the Era of Magnetic Resonance Imaging. Minerva Urology and Nephrology 2018, 70 (3):264-74. [CrossRef] [PubMed]
Cohen IG. Informed Consent and Medical Artificial Intelligence: What to Tell the Patient? SSRN Electronic Journal 2020. [CrossRef]
Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, et al. The Drug Repurposing Hub: a next-generation drug library and information resource. Nat Med 2017;23(4):405-8. [CrossRef] [PubMed]
Cunningham RJ, Loram ID. Estimation of Absolute States of Human Skeletal Muscle via Standard B-Mode Ultrasound Imaging and Deep Convolutional Neural Networks. J R Soc Interface 2020;17(162):20190715. [CrossRef] [PubMed]
Damiati SA, Martini LG, Smith NW, Lawrence MJ, Barlow DJ. Application of machine learning in prediction of hydrotropeenhanced solubilisation of indomethacin. Int J Pharm 2017;530(1–2):99–106. [CrossRef] [PubMed]
Damiati SA. Digital Pharmaceutical Sciences. AAPS PharmSciTech 2020;21(6):206. [CrossRef] [PubMed]
Davenport T, Kalakota R. The Potential for Artificial Intelligence in Healthcare. Future Healthc J 2019;6(2):94–8. [CrossRef] [PubMed]
Endrenyi L, Al-Shaikh P. Sensitive and specific determination of the equivalence of absorption rates. Pharm. Res 1995;12:1856–1864. [CrossRef] [PubMed]
Gadd C, Wade S, Shah AA. Pseudo-Marginal Bayesian Inference for Gaussian Process Latent Variable Models. Machine Learning 2021;110(6):1105–1143. [CrossRef]
Gaffney R, Rao B. Global Teledermatology. Global Dermatology 2016, 2 (5). [CrossRef]
Gaisford S, Saunders M. Essentials of pharmaceutical preformulation. 1st ed. Wiley-Blackwell; 2013. p. 268. [CrossRef]
Gallego V, Naveiro R, Roca C, Ríos Insua D, Campillo NE. AI in Drug Development: A Multidisciplinary Perspective. Mol Divers 2021; 25(3):1461–79. [CrossRef] [PubMed]
Garcia-Vidal C, Sanjuan G, Puerta-Alcalde P, Moreno-García E, Soriano A. Artificial intelligence to support clinical decision-making processes. EBioMedicine 2019; 46:27-9. [CrossRef] [PubMed]
Garg L, Basterrech S, Banerjee C, Sharma T. Artificial Intelligence in Healthcare (Advanced Technologies and Societal Change) 1st ed. 2022, Springer. [CrossRef]
Goceri E. Medical image data augmentation: techniques, comparisons and interpretations. Artif Intell Rev 2023; 20:1-45. [CrossRef] [PubMed]
Goergen SK, Frazer HM, Reddy S. Quality Use of Artificial Intelligence in Medical Imaging: What Do Radiologists Need to Know? J Med Imaging Radiat Oncol 2022;66(2):225–32. [CrossRef] [PubMed]
Grimm F, Edl F, Kerscher SR, Nieselt K, Gugel I, Schuhmann MU. Semantic Segmentation of Cerebrospinal Fluid and Brain Volume with a Convolutional Neural Network in Pediatric Hydrocephalus—Transfer Learning from Existing Algorithms. Acta Neurochir (Wien) 2020;162(10):2463–74. [CrossRef] [PubMed]
Gupta R, Srivastava D, Sahu M, Tiwari S, Ambasta RK, Kumar P. Artificial intelligence to deep learning: machine intelligence approach for drug discovery. Mol Divers 2021;25(3):1315-60. [CrossRef] [PubMed]
Hansch C, Maloney PP, Fujita T, Muir RM. Correlation of biological activity of phenoxyacetic acids with Hammett substituent constants and partition coefficients. Nature 1962;194(4824):178–80. [CrossRef]
Harris M. Artificial Intelligence. 1st ed. Cavendish Square Publishing; 2010. p.48 p.
Henderson H. Artificial Intelligence: Mirrors for the Mind (Milestones in Discovery and Invention). 1st ed. Chelsea House Pub; 2007. 176 p.
Hossain S, Kabedev A, Parrow A, Bergström C, Larsson P. Molecular simulation as a computational pharmaceutics tool to predict drug solubility, solubilization processes and partitioning. Eur J Pharm Biopharm 2019; 137:46–55. [CrossRef] [PubMed]
Hung AJ, Chen J, Che Z, Nilanon T, Jarc A, Titus M, et al. Utilizing Machine Learning and Automated Performance Metrics to Evaluate Robot-Assisted Radical Prostatectomy Performance and Predict Outcomes. J Endourol 2018;32(5):438–44. [CrossRef] [PubMed]
Kagian A, Dror G, Leyvand T, Meilijson I, Cohen-Or D, Ruppin E. A Machine Learning Predictor of Facial Attractiveness Revealing Human-like Psychophysical Biases. Vision Res 2008;48(2):235–43. [CrossRef] [PubMed]
Kaliyadan F, Ashique K. Use of Mobile Applications in Dermatology. Indian J Dermatol 2020;65 (5):371-6. [CrossRef] [PubMed]
Karalis V. Machine Learning in Bioequivalence: Towards Identifying an Appropriate Measure of Absorption Rate. Applied Sci. 2023;13:418. [CrossRef]
Karalis V. On the Interplay between Machine Learning, Population Pharmacokinetics, and Bioequivalence to Introduce Av-erage Slope as a New Measure for Absorption Rate. Applied Sci 2023; 13: 2257. [CrossRef]
Karalis VD. An in silico approach toward the appropriate absorption rate metric in bioequivalence. Pharmaceuticals (Basel) 2023; 16(5):725. [CrossRef] [PubMed]
Karalis VD. The integration of artificial intelligence into clinical practice. Applied Biosciences 2024;3(1):14–44. [CrossRef]
Kaul V, Enslin S, Gross SA. History of Artificial Intelligence in Medicine. Gastrointest Endosc 2020; 92(4):807–12. [CrossRef] [PubMed]
Khan AR, Khan S, Harouni M, Abbasi R, Iqbal S, Mehmood Z. Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification. Microsc. Res Tech 2021; 84(7):1389-99. [CrossRef] [PubMed]
Kusunose K. Steps to Use Artificial Intelligence in Echocardiography. J Echocardiogr 2020; 19(1): 21–7. [CrossRef] [PubMed]
Lavecchia A, Di Giovanni C. Virtual screening strategies in drug discovery: a critical review. Curr Med Chem 2013;20(23):2839-60. [CrossRef] [PubMed]
León MA, Räsänen J. Neural Network-Based Detection of Esophageal Intubation in Anesthetized Patients. J Clin Monit 1996;12(2):165–9. [CrossRef] [PubMed]
Liu G, Yang X, Zhong H. Molecular design of flotation collectors: a recent progress. Adv Colloid Interf Sci 2017;246:181–95. [CrossRef] [PubMed]
Lodwick GS, Keats TE, Dorst JP. The Coding of Roentgen Images for Computer Analysis as Applied to Lung Cancer. Radiology 1963;81(2):185–200. [CrossRef] [PubMed]
Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization Classification of Tumors of the Central Nervous System: A Summary. Acta Neuropathologica 2016;131(6):803–20. [CrossRef] [PubMed]
Maqsood S, Damaševičius R, Maskeliūnas R. Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients. Sensors (Basel) 2021; 21:3865. [CrossRef] [PubMed]
McCulloch WS, Pitts W. A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics 1943;5(4):115–33. [CrossRef] [PubMed]
Ota R, Yamashita F. Application of Machine Learning Techniques to the Analysis and Prediction of Drug Pharmacokinetics. J Control Release 2022;352:961–9. [CrossRef] [PubMed]
Papadopoulos D, Karalis VD. Introducing an artificial neural network for virtually increasing the sample size of bioequivalence studies. Appl Sci 14(7):2970. [CrossRef]
Papadopoulos D, Karalis VD. Variational autoencoders for data augmentation in clinical studies. Appl Sci 2023;13(15):8793. [CrossRef]
Patel L, Shukla T, Huang X, Ussery DW, Wang S. Machine Learning Methods in Drug Discovery. Molecules 2020;25(22):5277. [CrossRef] [PubMed]
Peleg M, Tu S. Decision support, knowledge representation and management in medicine. Yearb Med Inform 2006:72-80. [PubMed]
Pesteie M, Abolmaesumi P, Rohling RN. Adaptive Augmentation of Medical Data Using Independently Conditional Variational Auto-Encoders. IEEE Trans Med Imaging. 2019; 38(12):2807-20. [CrossRef] [PubMed]
Pillai N, Abos A, Teutonico D, Mavroudis PD. Machine learning framework to predict pharmacokinetic profile of small molecule drugs based on chemical structure. Clin Transl Sci 2024;17(5):e13824. [CrossRef] [PubMed]
Pires DE, Blundell TL, Ascher DB. pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures. J Med Chem 2015;58(9):4066-72. [CrossRef] [PubMed]
Pringle C, Kilday JP, Kamaly-Asl I, Stivaros SM. The Role of Artificial Intelligence in Paediatric Neuroradiology. Pediatric Radiology 2022; 52 (11): 2159–72. [CrossRef] [PubMed]
Proposal for a Regulation of The European Parliament and of The Council Laying Down Harmonised Rules on Artificial Intelligence (Artificial Intelligence Act) And Amending Certain Union Legislative Acts. Brussels: European Commission; 2021. [CrossRef] [PubMed]
Quon JL, Han M, Kim LH, Koran ME, Chen LC, Lee EH, et al. Artificial Intelligence for Automatic Cerebral Ventricle Segmentation and Volume Calculation: A Clinical Tool for the Evaluation of Pediatric Hydrocephalus. J Neurosurg Pediatr 2021;27(2):131–138. [CrossRef] [PubMed]
Rostami-Hodjegan A, Jackson P, Tucker G. Sensitivity of indirect metrics for assessing “rate” in bioequivalence studies: moving the “goalposts” or changing the “game”. J Pharm Sci 1994; 83(11):1554–7. [CrossRef] [PubMed]
Rozario N, Rozario D. Can Machine Learning Optimize the Efficiency of the Operating Room in the Era of COVID-19? Canadian Journal of Surgery 2020; 63(6): E527–9. [CrossRef] [PubMed]
Rubio DM, Schoenbaum EE, Lee LS, Schteingart DE, Marantz PR, Anderson KE, et al. Defining translational research: implications for training. Acad Med: J Assoc Am Med Coll 2010;85(3):470–5. [CrossRef] [PubMed]
Russell S, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson; 2021. p. 1136 p.
Russell S, Norvig P. Artificial Intelligence: A Modern Approach. 4th ed. Pearson; 2021. p.1136.
Sakpal TV. Sample size estimation in clinical trial. Perspect Clin Res 2010; 1(2): 67-9. [CrossRef] [PubMed]
Schall R, Luus H. Comparison of absorption rates in bioequivalence studies of immediate release drug formulations. Int J Clin Pharmacol Ther Toxicol 1992;30(5):153-9. [PubMed]
Schall R, Luus HG, Steinijans VW, Hauschke D. Choice of characteristics and their bioequivalence ranges for the com-parison of absorption rates of immediate-release drug formulations. Int J Clin Pharmacol Ther 1994; 32(7): 323-8. [PubMed]
Siegel RL, Miller KD, Jemal A. Cancer Statistics, 2018. CA Cancer J Clin 2018;68(1):7–30. [CrossRef] [PubMed]
Simões MF, Silva G, Pinto AC, Fonseca M, Silva NE, Pinto RMA, et al. Artificial neural networks applied to quality-by-design: From formulation development to clinical outcome. Eur J Pharm Biopharm 2020; 152:282-95. [CrossRef] [PubMed]
Steels L. and Brooks R. The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents. 1st ed. Routledge; 2018. p.300. [CrossRef]
Steels L. Lopez de Mantaras R. The Barcelona Declaration for the Proper Development and Usage of Artificial Intelligence in Europe. IOS Press 2018;485-94. [CrossRef]
Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA: A Cancer Journal for Clinicians 2021;71(3):209–49. [CrossRef] [PubMed]
Takshi S. Unexpected Inequality: Disparate-Impact from Artificial Intelligence in Healthcare Decisions. J Law Health 2021;34(2):215–51. [PubMed]
Tapak L, Hamidi O, Amini P, Poorolajal J. Prediction of Kidney Graft Rejection Using Artificial Neural Network. Healthc Inform Res 2017;23(4):277-84. [CrossRef] [PubMed]
Tavolara TE, Gurcan MN, Segal S, Niazi MK. Identification of Difficult to Intubate Patients from Frontal Face Images Using an Ensemble of Deep Learning Models. Computers in Biology and Medicine 2021; 136: 104737. [CrossRef] [PubMed]
Turing AM. On computable numbers, with an application to the entscheidungsproblem. Proc Lond Math Soc (3) [Internet]. 1937;s2-42(1):230–65. [CrossRef] [PubMed]
US FDA. Artificial Intelligence and Machine Learning in Software as a Medical Device.
US Food and Drug Administration. Proposed regulatory framework for modifications to artificial intelligence/machine learning (AI/ML)-based software as a medical device (SAMD): Discussion paper and request for feedback. 2019. [CrossRef] [PubMed]
van der Maaten L, Hinton G. Visualizing Non-Metric Similarities in Multiple Maps. Machine Learning 2011; 87(1):33–55. [CrossRef]
Veronese F, Branciforti F, Zavattaro E, Tarantino V, Romano V, Meiburger KM, et al. The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks. Diagnostics 2021;11(3):451. [CrossRef] [PubMed]
Widrow B, Lehr MA. 30 Years of Adaptive Neural Networks: Perceptron, Madaline, and Backpropagation. Proceedings of the IEEE 1990;78(9):1415–42. [CrossRef]
Xue B, Li D, Lu C, King CR, Wildes T, Avidan MS, et al. Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications. JAMA Netw Open 2021; 4(3): e212240. [CrossRef] [PubMed]
Yang Y, Ye Z, Su Y, Zhao Q, Li X, Ouyang D. Deep learning for in vitro prediction of pharmaceutical formulations. Acta Pharm Sin B 2019;9(1):177–85. [CrossRef] [PubMed]
Ye Z, Yang Y, Li X, Cao D, Ouyang D. An integrated transfer learning and multitask learning approach for pharmacokinetic parameter prediction. Mol Pharm 2018;16(2):533–41. [CrossRef] [PubMed]
Zhao C, Jain A, Hailemariam L, Suresh P, Akkisetty P, Joglekar G, et al. Toward intelligent decision support for pharmaceutical product development. JPI 2006; 1:23–35. [CrossRef]
Zhao W, Yang J, Sun Y, Li C, Wu W, Jin L, et al. 3D Deep Learning from CT Scans Predicts Tumor Invasiveness of Subcentimeter Pulmonary Adenocarcinomas. Cancer Res 2018;78(24):6881–9. [CrossRef] [PubMed]
Zhou Y, Wang F, Tang J, Nussinov R, Cheng F. Artificial intelligence in COVID-19 drug repurposing. Lancet Digit Health 2020;2(12):e667-76. [CrossRef] [PubMed]
Zhu H. Big Data and Artificial Intelligence Modeling for Drug Discovery. Annu Rev Pharmacol Toxicol 2020; 60:573-89. [CrossRef] [PubMed]
