Santos DP and Baeßler B: Big data, artificial intelligence, and structured reporting. Eur Radiol Exp. 2(42)2018.PubMed/NCBI View Article : Google Scholar

Akyazi T, Goti A, Oyarbide A, Alberdi E and Bayon F: A guide for the food industry to meet the future skills requirements emerging with industry 4.0. Foods. 9(492)2020.PubMed/NCBI View Article : Google Scholar

Favaretto M, Shaw D, De Clercq E, Joda T and Elger BS: Big data and digitalization in dentistry: A systematic review of the ethical issues. Int J Environ Res Public Health. 17(2495)2020.PubMed/NCBI View Article : Google Scholar

Etienne H, Hamdi S, Le Roux M, Camuset J, Khalife-Hocquemiller T, Giol M, Debrosse D and Assouad J: Artificial intelligence in thoracic surgery: Past, present, perspective and limits. Eur Respir Rev. 29(200010)2020.PubMed/NCBI View Article : Google Scholar

McCarthy J, Minsky ML, Rochester N and Shannon CE: A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Magazine. 27(12)2006.

Samuel AL: Some studies in machine learning using the game of checkers. IBM J Res Dev. 3:210–229. 1959.

Wied GL, Bahr GF, Oldfield DG and Bartels PH: Computer-assisted identification of cells from uterine adenocarcinoma. A clinical feasibility study with TICAS. I. Measurements at wavelength 530 nm. Acta Cytol. 12:357–370. 1968.PubMed/NCBI

Ishiyama T, Tsubura E, Hirao F, Yamamura Y, Takeda S, Tateisi K, Yamamoto M, Uemura M, Yaida K, Hayakawa F, et al: A study of the automation of cytodiagnosis. Med Biol Eng. 7:297–306. 1969.PubMed/NCBI View Article : Google Scholar

Shortliffe EH, Axline SG, Buchanan BG, Merigan TC and Cohen SN: An artificial intelligence program to advise physicians regarding antimicrobial therapy. Comput Biomed Res. 6:544–560. 1973.PubMed/NCBI View Article : Google Scholar

Miller RA, Pople HE and Myers JD: INTERNIST-1, An experimental computer-based diagnostic consultant for general internal medicine. N Engl J Med. 307:478–486. 1982.PubMed/NCBI View Article : Google Scholar

Dechter R: Learning while searching in constraint-satisfaction-problems. In: Proceedings of the Fifth National Conference on Artificial Intelligence. American Association for Artificial Intelligence, Philadelphia, pp178-183, 1986.

LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W and Jackel LD: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1:541–551. 1989.

Kwoh YS, Hou J, Jonckheere EA and Hayati S: A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans Biomed Eng. 35:153–160. 1988.PubMed/NCBI View Article : Google Scholar

Jacobs LK, Shayani V and Sackier JM: Determination of the learning curve of the AESOP robot. Surg Endosc. 11:54–55. 1997.PubMed/NCBI View Article : Google Scholar

Sung GT and Gill IS: Robotic laparoscopic surgery: A comparison of the DA Vinci and Zeus systems. Urology. 58:893–898. 2001.PubMed/NCBI View Article : Google Scholar

Weinstein GS, O'malley BW Jr and Hockstein NG: Transoral robotic surgery: Supraglottic laryngectomy in a canine model. Laryngoscope. 115:1315–1319. 2005.PubMed/NCBI View Article : Google Scholar

Kurumbail RG, Stevens AM, Gierse JK, McDonald JJ, Stegeman RA, Pak JY, Gildehaus D, Miyashiro JM, Penning TD, Seibert K, et al: Structural basis for selective inhibition of cyclooxygenase-2 by anti-inflammatory agents. Nature. 384:644–648. 1996.PubMed/NCBI View Article : Google Scholar

Molnár L and Losonczy H: Tyrosine kinase inhibitor STI571: New possibility in the treatment of chronic myeloid leukemia. Orv Hetil. 143:2379–2384. 2002.PubMed/NCBI(In Hu).

Lanfranco AR, Castellanos AE, Desai JP and Meyers WC: Robotic surgery: A current perspective. Ann Surg. 239:14–21. 2004.PubMed/NCBI View Article : Google Scholar

Hinton GE, Osindero S and The YW: A fast learning algorithm for deep belief nets. Neural Comput. 18:1527–1554. 2006.PubMed/NCBI View Article : Google Scholar

Hinton GE and Salakhutdinov RR: Reducing the dimensionality of data with neural networks. Science. 313:504–507. 2006.PubMed/NCBI View Article : Google Scholar

Komura D and Ishikawa S: Machine learning approaches for pathologic diagnosis. Virchows Arch. 475:131–138. 2019.PubMed/NCBI View Article : Google Scholar

American College of Radiology. Cleared AI Algorithms. 2021. https://models.acrdsi.org. Accessed July 1, 2021.

U.S. Food and Drug Administration: FDA allows marketing of first whole slide imaging system for digital pathology. 2017. https://www.fda.gov/news-events/press-announcements/fda-allows-marketing-first-whole-slide-imaging-system-digital-pathology. Accessed July 1, 2021.

Mills AM, Gradecki SE, Horton BJ, Blackwell R, Moskaluk CA, Mandell JW, Mills SE and Cathro HP: Diagnostic efficiency in digital pathology: A comparison of optical versus digital assessment in 510 surgical pathology cases. Am J Surg Pathol. 42:53–59. 2018.PubMed/NCBI View Article : Google Scholar

The Medical Futurist: FDA-approved A.I.-based algorithms. 2021. https://medicalfuturist.com/fda-approved-ai-based-algorithms/. Accessed July 1, 2021.

Asai A, Koseki J, Konno M, Nishimura T, Gotoh N, Satoh T, Doki Y, Mori M and Ishii H: Drug discovery of anticancer drugs targeting methylenetetrahydrofolate dehydrogenase 2. Heliyon. 4(e01021)2018.PubMed/NCBI View Article : Google Scholar

Spangler S, Wilkins AD, Bachman BJ, Nagarajan M, Dayaram T, Haas P, Regenbogen S, Pickering CR, Comer A, Myers JN, et al: Automated hypothesis generation based on mining scientific literature. KDD'14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining: 1877-1886, 2014.

Doyle-Lindrud S: Watson will see you now: A supercomputer to help clinicians make informed treatment decisions. Clin J Oncol Nurs. 19:31–32. 2015.PubMed/NCBI View Article : Google Scholar

Tojo A: Clinical sequencing in leukemia with the assistance of artificial intelligence. Rinsho Ketsueki. 58:1913–1917. 2017.PubMed/NCBI View Article : Google Scholar : (In Japanese).

Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM and Thrun S: Dermatologist-level classification of skin cancer with deep neural networks. Nature. 542:115–118. 2017.PubMed/NCBI View Article : Google Scholar

Toratani M, Konno M, Asai A, Koseki J, Kawamoto K, Tamari K, Li Z, Sakai D, Kudo T, Satoh T, et al: A convolutional neural network uses microscopic images to differentiate between mouse and human cell lines and their radioresistant clones. Cancer Res. 78:6703–6707. 2018.PubMed/NCBI View Article : Google Scholar

Yanagisawa K, Toratani M, Asai A, Konno M, Niioka H, Mizushima T, Satoh T, Miyake J, Ogawa K, Vecchione A, et al: Convolutional neural network can recognize drug resistance of single cancer cells. Int J Mol Sci. 21(3166)2020.PubMed/NCBI View Article : Google Scholar

Tanos R, Tosato G, Otandault A, Al Amir Dache Z, Pique Lasorsa L, Tousch G, El Messaoudi S, Meddeb R, Assaf MD, Ychou M, et al: Machine learning-assisted evaluation of circulating DNA quantitative analysis for cancer screening. Adv Sci (Weinh). 7(2000486)2020.PubMed/NCBI View Article : Google Scholar

Im YR, Tsui DWY, Diaz LA Jr and Wan JCM: Next-generation liquid biopsies: Embracing data science in oncology. Trends Cancer. 7:283–292. 2021.PubMed/NCBI View Article : Google Scholar

Harada Y and Shimizu T: Impact of a Commercial Artificial Intelligence-Driven Patient Self-Assessment Solution on Waiting Times at General Internal Medicine Outpatient Departments: Retrospective Study. JMIR Med Inform. 8(e21056)2020.PubMed/NCBI View Article : Google Scholar

Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van Der Laak JA, van Ginneken B and Sánchez CI: A survey on deep learning in medical image analysis. Med Image Anal. 42:60–88. 2017.PubMed/NCBI View Article : Google Scholar

Khosravi P, Kazemi E, Imielinski M, Elemento O and Hajirasouliha I: Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine. 27:317–328. 2018.PubMed/NCBI View Article : Google Scholar

Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, Sündermann SH, Stamm C, Hofmann T, Falk V and Eickhoff C: Machine learning for real-time prediction of complications in critical care: A retrospective study. Lancet Respir Med. 6:905–914. 2018.PubMed/NCBI View Article : Google Scholar

Saeidi H, Opfermann JD, Kam M, Raghunathan S, Leonard S and Krieger A: A confidence-based shared control strategy for the smart tissue autonomous robot (STAR). Rep US. 2018:1268–1275. 2018.PubMed/NCBI View Article : Google Scholar

Brown N, Cambruzzi J, Cox PJ, Davies M, Dunbar J, Plumbley D, Sellwood MA, Sim A, Williams-Jones BI, Zwierzyna M and Sheppard DW: Big data in drug discovery. Prog Med Chem. 57:277–356. 2018.PubMed/NCBI View Article : Google Scholar

Vélez-Guerrero MA, Callejas-Cuervo M and Mazzoleni S: Artificial intelligence-based wearable robotic exoskeletons for upper limb rehabilitation: A review. Sensors (Basel). 21(2146)2021.PubMed/NCBI View Article : Google Scholar

Comendador B, Francisco B, Medenilla J, Nacion S and Serac T: Pharmabot: A pediatric generic medicine consultant chatbot. J Automat Control Eng. 3:137–140. 2015.

Wong V, Rosenbaum S, Sung W, Kaplan RM, Bott N, Platchek T, Milstein A and Shah NR: Caring for caregivers: bridging the gap between family caregiving policy and practice. NEJM Catal Innov Care Deliv. 2:2021.

Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K and Tekade RK: Artificial intelligence in drug discovery and development. Drug Discov Today. 26:80–93. 2021.PubMed/NCBI View Article : Google Scholar

AlQuraishi M: AlphaFold at CASP13. Bioinformatics. 35:4862–4865. 2019.PubMed/NCBI View Article : Google Scholar

Wang C and Zhang Y: Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest. J Comput Chem. 38:169–177. 2017.PubMed/NCBI View Article : Google Scholar

Durrant JD and McCammon JA: NNScore 2.0: A neural-network receptor-ligand scoring function. J Chem Inf Model. 51:2897–2903. 2011.PubMed/NCBI View Article : Google Scholar

Feinberg EN, Sur D, Wu Z, Husic BE, Mai H, Li Y, Sun S, Yang J, Ramsundar B and Pande VS: PotentialNet for molecular property prediction. ACS Cent Sci. 4:1520–1530. 2018.PubMed/NCBI View Article : Google Scholar

Yasuo N and Sekijima M: Improved method of structure-based virtual screening via interaction-energy-based learning. J Chem Inf Model. 59:1050–1061. 2019.PubMed/NCBI View Article : Google Scholar

Wallach I, Dzamba M and Heifets A: AtomNet: A deep convolutional neural network for bioactivity prediction in structure-based drug discovery. arXiv: 1510.02855, 2015.

Stork C, Chen Y, Šícho M and Kirchmair J: Hit dexter 2.0: Machine-learning models for the prediction of frequent hitters. J Chem Inf Model. 59:1030–1043. 2019.PubMed/NCBI View Article : Google Scholar

Wójcikowski M, Zielenkiewicz P and Siedlecki P: Open drug discovery toolkit (ODDT): A new open-source player in the drug discovery field. J Cheminform. 7(26)2015.PubMed/NCBI View Article : Google Scholar

Rifaioglu AS, Nalbat E, Atalay V, Martin MJ, Cetin-Atalay R and Doğan T: DEEPScreen: High performance drug-target interaction prediction with convolutional neural networks using 2-D structural compound representations. Chem Sci. 11:2531–2557. 2020.PubMed/NCBI View Article : Google Scholar

Xu Y, Ma J, Liaw A, Sheridan RP and Svetnik V: Demystifying multitask deep neural networks for quantitative structure-activity relationships. J Chem Inf Model. 57:2490–2504. 2017.PubMed/NCBI View Article : Google Scholar

Awale M and Reymond JL: Polypharmacology Browser PPB2: Target prediction combining nearest neighbors with machine learning. J Chem Inf Model. 59:10–17. 2019.PubMed/NCBI View Article : Google Scholar

Duvenaud DK, Maclaurin D, Iparraguirre J, Bombarell R, Hirzel T, Aspuru-Guzik A and Adams RP: Convolutional networks on graphs for learning molecular fingerprints. In: Advances in neural information processing systems 28. NIPS Foundation, Montreal. 2:2224–2232. 2015.

Putin E, Asadulaev A, Ivanenkov Y, Aladinskiy V, Sanchez-Lengeling B, Aspuru-Guzik A and Zhavoronkov A: Reinforced adversarial neural computer for de novo molecular design. J Chem Inf Model. 58:1194–1204. 2018.PubMed/NCBI View Article : Google Scholar

Olivecrona M, Blaschke T, Engkvist O and Chen H: Molecular de-novo design through deep reinforcement learning. J Cheminform. 9(48)2017.PubMed/NCBI View Article : Google Scholar

Mayr A, Klambauer G, Unterithiner T and Hochreiter S: DeepTox: Toxicity prediction using deep learning. Front Environ Sci. 3(80)2016.

Steiner S, Wolf J, Glatzel S, Andreou A, Granda JM, Keenan G, Hinkley T, Aragon-Camarasa G, Kitson PJ, Angelone D and Cronin L: Organic synthesis in a modular robotic system driven by a chemical programming language. Science. 363(eaav2211)2019.PubMed/NCBI View Article : Google Scholar

Coley CW, Rogers L, Green WH and Jensen KF: SCScore: Synthetic complexity learned from a reaction corpus. J Chem Inf Model. 58:252–261. 2018.PubMed/NCBI View Article : Google Scholar

Antunes DA, Devaurs D and Kavraki LE: Understanding the challenges of protein flexibility in drug design. Expert Opin Drug Discov. 10:1301–1313. 2015.PubMed/NCBI View Article : Google Scholar

Uehara S and Tanaka S: AutoDock-GIST: Incorporating thermodynamics of active-site water into scoring function for accurate protein-ligand docking. Molecules. 21(1604)2016.PubMed/NCBI View Article : Google Scholar

Jamal S, Ali W, Nagpal P, Grover S and Grover A: Computational models for the prediction of adverse cardiovascular drug reactions. J Transl Med. 17(171)2019.PubMed/NCBI View Article : Google Scholar

Walonoski J, Kramer M, Nichols J, Quina A, Moesel C, Hall D, Duffett C, Dube K, Gallagher T and McLachlan S: Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record. J Am Med Inform Assoc. 25:230–238. 2018.PubMed/NCBI View Article : Google Scholar

Choi E, Biswal S, Malin B, Duke J, Stewart WF and Sun J: Generating multi-label discrete patient records using generative adversarial networks. PMLR. 68:286–305. 2017.

Linardatos P, Papastefanopoulos V and Kotsiantis S: Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy (Basel). 23(18)2021.PubMed/NCBI View Article : Google Scholar