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Artificial intelligence and its applications in healthcare and pharmacy


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Artificial Intelligence- Introduction, Scope, Problems of AI, Approaches to overcome them. Applications of AI Healthcare and Pharmacy.

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Artificial intelligence and its applications in healthcare and pharmacy

  1. 1. Atul Adhikari M.Pharm, 2nd Semester Assam Down Town University, Guwahati
  2. 2. Introduction Intelligence of machines and the branch of computer science which aims to create it. “Machines will be capable, within 20 years, of doing any work a man can do.” –Herbert Simon, 1965(AI innovator) Three elements of AI Massive amount of data Sophisticated algorithms High performance parallel processors Three Steps Computers and programs The Turing test The Darmont Conference 2Artificial Intellegence
  3. 3. Problems of AI/ Challenges Reasoning, Problem Solving Knowledge representation Planning Learning Natural language processing Perception Motion manipulation Social Intelligence Creativity General Intelligence Approaches Cybernetics Symbolic Statistical Integrating the approaches Applications Healthcare and Medicines Automotive Finance and economic Video Games Heavy Industries Robotics 3Artificial Intellegence
  4. 4. AI in Healthcare Managing Medical Records and other data Doing repetitive jobs Treatment Design Digital Consultation Virtual Nurses Medication Management Drug Discovery Precision Medicine Healthcare Monitoring Healthcare System Analysis 4Artificial Intellegence
  5. 5. 0 10 20 30 40 50 Robot-assisted Surgery Virtual Nurshing… Adm. workflow Assistance Fraud detection Dosage error Detection Connected Machines CT Participant Identifier Preliminary Diagnosis Advance Image Diagnosis Cybersecurity Fig: Estimated potential annual benefit for each application by 2026(in billon USD) estimated potential annual benefit for each application by 2026(in billon USD) Source: Accenture Analysis Total= $150 Billions 5Artificial Intellegence
  6. 6.  Many big Pharmaceutical companies began investing in AI in order to develop better diagnostics or biomarkers, to identify drug targets and to design new drugs and products.  Merck partnership with Numerate in March 2012 focusing on generating novel small molecule drug leads for unnamed cardiovascular disease target.  In december, 2016 Pfizer and IBM announced partnership to accelerate drug discovery in immuno- oncology. Current Scenario 6Artificial Intellegence
  7. 7. Disease Identification  2015- Report by Pharmaceutical Research and Manufacturers of America- more than 800 drugs and vaccines are in trial phase to treat cancer.  Google’s DeepMind Health, announced multiple partnerships including some eye hospitals in which they are developing technology to address macular degeneration in aging eyes.  Oxford’s Pivital® Predicting Response to Depression Treatment (PReDicT) project is aiming to produce commercially-available emotional test battery for use in clinical setting. 7Artificial Intellegence
  8. 8. Personalized Treatment  Micro biosensors and devices, mobile apps with more sophisticated health-measurement and remote monitoring capabilities; these data can further be used for R&D.  DermCheck; app available in Google play store in which images are sent to dermatologists(human not machines) 8Artificial Intellegence
  9. 9. Drug Discovery/Manufacturing  From initial screening of drug compounds to predicted success rate based on biological factors.  R&D discovery technology; next-generation sequencing.  Previous experiments are used to train the model  Optimization softwares (example: FormRules)  Designing of the processes 9Artificial Intellegence
  10. 10. Clinical Trial Research  Machine learning- to shape, direct clinical trials  Advanced predictive analysis in identifying candidates for clinical trials  Remote monitoring and real time data access for increased safety; biological and other signals for any sign of harm or death to participants.  Finding best sample sizes for increased efficiency; addressing and adapting to differences in sites for patient recruitments; using electronic medical records to reduce data errors. 10Artificial Intellegence
  11. 11. Epidemic Outbreak Prediction  To predict malaria outbreaks, from data like temperature, average monthly rainfall, total number of positive cases, etc.  ProMED-mail is a internet based reporting program for monitoring emerging diseases and providing outbreak reports. 11Artificial Intellegence
  12. 12. Radiology and Radiotherapy  Google’s DeepMind Health is working with University College London Hospital (UCLH) to develop machine learning algorithms capable of detecting differences in healthy and cancerous tissues. 12Artificial Intellegence
  13. 13. Smart Electronic Health Records  AI to help diagnosis, clinical decisions, and personalized treatment suggestions.  Handwriting recognition and transforming cursive or other sketched handwriting into digitized characters. 13Artificial Intellegence
  14. 14. Regulating Use of Artificial Intelligence in Digital Health Products  Incomplete insight from US FDA for products utilizing AI.  Medical devices provisions of Federal Food, Drug and Cosmetic Act-1970s  FDA created Digital Health Program tasked with developing and implementing a new regulatory model for digital health technology.  Over the last five years different guidelines like Mobile Medical Applications Guidelines. 14Artificial Intellegence
  15. 15. AI in Clinical Research  Cutting costs  Improving trial quality  Improving trial time by almost half  Finding biomarkers and gene signatures that cause diseases  Recruiting trial patients in minutes  Reading volumes of text and data in seconds  On verse of discovering involving new diagnostic tools and treatments for Alzimer’s disease, cancer, and other chronic and terminal illness. 15Artificial Intellegence
  16. 16. References  B Aksu, A Paradkar; Quality by design approach: Application of Artificial Intellegence Techniques of Tablets Manufactured by Direct Compression; PharmsciTech; 2012; 13(4); 1138-1146  JA Dimasi, RW Hansen; The price of innovation: new estimates of drug development costs. J Health Econ; 2003;22(2);151-185  S Behjati and PS Tarpey; What is next generation sequencing?; Arch Dis Child Pract Ed;2013; 98(6); 236-238     16Artificial Intellegence