Artificial Intelligence in Healthcare

AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone

Academic Edition, Chapter 42 - References/Further Reading


  1. Facts & figures. Chem Eng News [Internet]. 1978 [cited 2021 Feb 19];56(19):48. Available from:
  2. Blanco-Montenegro I, De Ritis R, Chiappini M. Imaging and modelling the subsurface structure of volcanic calderas with high-resolution aeromagnetic data at Vulcano (Aeolian Islands, Italy). Bulletin of Volcanology. 2007 Apr 1;69(6):643-59.
  3. Zhang W, Yu Q, Siddiquie B, Divakaran A, Sawhney H. “snap-n-eat” food recognition and nutrition estimation on a smartphone. Journal of diabetes science and technology. 2015 Apr 21;9(3):525-33.
  4. Cvetković B, Janko V, Romero AE, Kafalı Ö, Stathis K, Luštrek M. Activity recognition for diabetic patients using a smartphone. Journal of medical systems. 2016 Dec 1;40(12):256.
  5. Ellahham S. Artificial Intelligence in Diabetes Care. The American Journal of Medicine. 2020 Apr 20.
  6. Jain P, Joshi AM, Mohanty SP. iGLU: An Intelligent Device for Accurate Noninvasive Blood Glucose-Level Monitoring in Smart Healthcare. IEEE Consumer Electronics Magazine. 2019 Dec 4;9(1):35-42.
  7. Will an artificial pancreas be a gamechanger for type 1 diabetes? - Medical Technology | Issue 28 | June 2020 [Internet]. [cited 2021 Jan 20]. Available from:
  8. The closest to an artificial pancreas that has been successfully produced is the pairing of an insulin pump with a continuous glucose monitor. Cambridge University is currently developing one of the leading artificial pancreas projects. [Internet]. Diabetes. 2019 [cited 2021 Jan 20]. Available from:
  9. Hu S, Liao Y, Chen L. Identification of key pathways and genes in anaplastic thyroid carcinoma via integrated bioinformatics analysis. Med Sci Monit. 2018 Sep 14;24:6438–48.
  10. Calligaris D, Feldman DR, Norton I, Olubiyi O, Changelian AN, Machaidze R, Vestal ML, Laws ER, Dunn IF, Santagata S, Agar NY. MALDI mass spectrometry imaging analysis of pituitary adenomas for near-real-time tumor delineation. Proceedings of the National Academy of Sciences. 2015 Aug 11;112(32):9978-83.
  11. Gubbi S, Hamet P, Tremblay J, Koch CA, Hannah-Shmouni F. Artificial intelligence and machine learning in endocrinology and metabolism: the dawn of a new era. Frontiers in endocrinology. 2019 Mar 28;10:185.