Insight of Artificial Intelligence Application in Healthcare

Author(s): Cedric Kuang

Ever since “Deep Blue” defeated the human champion chess player in 1997, artificial intelligence has caused extensive attention and discussion. With the development of computing and data storage technologies, artificial intelligence has been made great progress and applied in a wide range of fields. Here, we focus on several creative and novel applications of artificial intelligence in the healthcare field and also discuss their future trends.

1. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive. Ther. Allied. Technol. 2019, 28, 73–81.
2. Yokota H, Goto M, Bamba C, et al. Reading Efficiency Can Be Improved by Minor Modification of Assigned Duties: a Pilot Study on a Small Team of General Radiologists. Jpn. J. Radiol. 2017, 35, 262–268.
3. Thesmar D, Sraer D, Pinheiro L, Dadson N, Veliche R, Greenberg P. Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges. Pharmacoeconomics. 2019, 37, 745–752.
4. Birnbaum HG, Cremieux PY, Greenberg PE, LeLorier J, Ostrander J, Venditti L. Using Healthcare Claims Data for Outcomes Research and Pharmacoeconomic Analysis. Pharmacoeconomics. 1999, 16, 1–8.
5. Cadarette SM, Wong L. An Introduction to Health Care Administrative Data. Can. J. Hosp. Pharm. 2015, 68, 232–237.
6. Jeong E, Park N, Choi Y, Park RW, Yoon D. Machine Learning Model Combining Features From Algorithms with Different Analytical Methodologies to Detect Laboratory‑event‑related Adverse Drug Reaction Signals. PLoS. One. 2018, 13, 1–15.
7. Tulabandhula T, Rudin C. On Combining Machine Learning with Decision Making. Machine Learning. 2014, 97, 33–64.
8. LeCun Y, Bengio Y, Hinton, G . Deep Learning. Nature. 2015, 521, 436–444.
9. Saposnik G, Redelmeier D, Ruff CC, Tobler PN. Cognitive Biases Associated with Medical Decisions: a Systematic Review. BMC. Med. Inform. Decis. Mak. 2016, 16, 1–14.
10. Gibert K, Garcia‑Alonso C, Salvador‑Carulla L. Integrating Clinicians, Knowledge and Data: Expert‑based Cooperative Analysis in Healthcare Decision Support. Health Res. Policy Syst. 2010, 8, 1–16.
11. Seidman AD, Pilewskie ML, Robson ME, Kelvin JF, Zauderer MG, Epstein AE, et al. Integration of Multi‑modality Treatment Planning For Early Stage Breast Cancer (BC) into Watson for Oncology, a Decision Support System: Seeing the Forest and the Trees. J. Clin. Oncol. 2015, 33.
12. VoPham T, Hart JE, Laden F, Chiang YY. Emerging Trends in Geospatial Artificial Intelligence (GeoAI): Potential Applications for Environmental Epidemiology. Environ. Health. 2018, 17, 40.
13. Boulos MK, Peng G, VoPham T. An Overview of GeoAI Applications in Health and Healthcare. Int. J. Health Geogr. 2019, 18, 7.
14. McAllister K, Mechanic LE, Amos C, Aschard H, Blair IA, Chatterjee N, Conti D, Gauderman WJ, Hsu L, Hutter CM, et al. Current Challenges and New Opportunities for Gene-environment Interaction Studies of Complex Diseases. Am. J. Epidemiol. 2017, 186, 753 –761.
15. Davis MM, Shanley TP. The Missing-Omes: Proposing Social and Environmental Nomenclature in Precision Medicine. Clin. Transl. Sci. 2017, 10, 64–66.
16. Boulos MN, Le Blond J. On the Road to Personalised and Precision Geomedicine: Medical Geology and a Renewed Call for Interdisciplinarity. Int. J. Health Geogr. 2016, 15, 5.
17. Lovejoy C, Buch V, Maruthappu M. Technology and Mental Health: The Role of Artificial Intelligence. Eur. Psychiatry. 2018, 55, 1–3.
18. Place S, Blanch-Hartigan D, Rubin C, Gorrostieta C, Mead C, Kane J, Marx B, Feast J, Deckersbach T, Pentland A, Nierenberg A, Azarbayejani A. Behavioral Indicators on a Mobile Sensing Platform Predict Clinically Validated Psychiatric Symptoms of Mood and Anxiety Disorders. J. Med. Internet Res. 2017, 19, 3
19. Stentzel U, Van den Berg N, Schulze LN, Schwaneberg T, Radicke F, Langosch JM, Et al. Predictors of Medication Adherence Among Patients with Severe Psychiatric Disorders: Findings from the Baseline Assessment of a Randomized Controlled Trial (Tecla). BMC Psychiatry 2018, 18, 155.
20. Fitzpatrick KK, Darcy A, Vierhile M. Delivering Cognitive Behavior Therapy to Young Adults with Symptoms of Depression and Anxiety Using a Fully Automated Conversational Agent (Woebot): A Randomized Controlled Trial. JMIR Ment. Health. 2017, 4, 2.
21. Rockstroh M, Franke S, Hofer M, et al. OR.NET: Multi-perspective Qualitative Evaluation of an Integrated Operating Room Based on IEEE 11073 SDC. Int. J. CARS. 2017, 12, 1461–1469.
22. Saba L, Biswas M, Kuppili V, Cuadrado Godia E, Suri HS, Edla DR, Omerzu T, Laird JR, Khanna NN, Mavrogeni S, Protogerou A, Sfikakis PP, Viswanathan V, Kitas GD, Nicolaides A, Gupta A, Suri JS. The Present and Future of Deep Learning in Radiology. Eur. J. Radiol. 2019, 114, 14–24.
23. Nielsen A, Hansen MB, Tietze A, Mouridsen K. Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning. Stroke. 2019, 6, 1394–1401.
24. González G, Ash SY, Vegas-Sánchez-Ferrero G, Onieva JO, Rahaghi FN, Ross JC, Díaz A, Estépar RSJ, Washko GR. Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography. Am. J. Resp. Crit. Care. 2018, 2, 193–203.
25. Barham P, Chen J, Chen Z, Davis A, Dean J, Devin M, et al. TensorFlow: A System for Large-Scale Machine Learning. In. OSDI. 2016, 16, 265–283.
26. IBM Research Accelerating Discovery: Medical Image Analytics, 10/10/2013. Available Online. https://www.youtube.com/watch?v=0i11VCNacAE
27. Liew C. The Future of Radiology Augmented with Artificial Intelligence: A Strategy for Success. Eur. J. Radiol. 2018, 102, 152–156.
28. Nagar, Yiftach, Malone TW, De Boer P, Garcia ACB. Essays on Collective Intelligence. PhD diss., Massachusetts Institute of Technology. 2016
29. Gruson D, Helleputte T, Rousseau P, et al., Data Science, Artificial Intelligence, and Machine Learning: Opportunities for Laboratory Medicine and the Value of Positive Regulation. Clin. Biochem. 2019, 69, 1–7
30. Lee K, Kim B, Choi Y, Kim S, Shin W, Lee S, et al. Deep Learning of Mutation-Genedrug Relations from the Literature. BMC Bioinformatics [Internet]. 2018, 19, 21. Available from: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-018-2029-1
31. Rathi S, Tsui E, Mehta N. The Current State of Teleophthalmology in the United States. Ophthalmology. 2017, 124, 1729–1734.
32. Kappor R, Walters SP, Al-Aswad LA, The Current State of Artificial Intelligence in Ophthalmology. Surv. Ophthalmol. 2018, 64, 233
33. Organization WH. Global Report on Diabetes 2016. Available from: http://www.who.int/diabetes/globalreport/en/.
34. Gulshan V, Peng L, Voram M, et al. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016, 316, 2402–2410.