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Artificial intelligence dr bhanu ppt 13 09-2020

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Artificial intelligence dr bhanu ppt 13 09-2020

  1. 1. Department of Pharmacy, IEC College of Engineering & Technology, IEC Group of Institutions, Greater Noida, Uttar Pradesh, India Organizing a Webinar on “Artificial Intelligence : Innovative Approach for Pharmacy and Healthcare Development in India” Time & Date : 2-3 pm on September 13, 2020 (Sunday) by Prof. (Dr.) Bhanu P. S. Sagar Professor & Director DOP, IECGI, Greater Noida, Uttar Pradesh, INDIA
  2. 2. Innovations in Medical and Biological Engineering • 1950s and earlier • Artificial Kidney • X ray • Electrocardiogram • Cardiac Pacemaker • Cardiopulmonary bypass • Antibiotic Production technology • Defibrillator • 1960s • Heart valve replacement • Intraocular lens • Ultrasound • Vascular grafts • Blood analysis and processing • 1970s – Computer assisted tomography – Artificial hip and knee replacements – Balloon catheter – Endoscopy – Biological plant food engineering • 1980s – Magnetic resonance imaging – Laser surgery – Vascular grafts – Recombinant therapeutics • Present day • Genomic sequencing and microarrays • Positron Emission tomography • Image guided surgery
  3. 3. New generations of medical technology products are Combination of different technologies
  4. 4.  Any technique which enables computers to mimic human brain.  As per McCarthy, it is “The science and engineering of making intelligent machines”.  Artificial Intelligence is defined as a field that deals with the design and application of algorithms for analysis, learning & interpreting data, “Use of a computer to model intelligent behaviour with minimal human intervention”.  Machines & computer programs are capable of problem solving and learning, like a human brain.  Goals of AI system is to develop system capable of taking complex problems in ways similar to human logic and reasoning. What is Artificial Intelligence ?
  5. 5. Artificial Intelligence AI encompasses many branches of statistical and machine learning, pattern recognition, clustering, similarity-based methods, logics and probability theory, as well as biologically motivated approaches, such as neural networks and fuzzy modeling. Natural Language Processing (“NLP”) and translation, Pattern recognition, Visual perception Decision making. Machine Learning (“ML”), one of the most exciting areas for Development of computational approaches to automatically make sense of data Advantage of Machine Can retain information ; Becomes smarter over time ; Machine is not susceptible to Sleep deprivation; distractions; information overload and short-term memory loss.
  6. 6. ARTIFICIAL INTELLIGENCE  Artificial intelligence (AI) is the study of complex information which processes problems that have their roles in some aspect of biological information processing.  The main aim of the subject is to identify useful information processing.  Pharmaceutical drug manufacturing, from formulation development to finished product, is very complex. This process includes multivariate interactions between raw materials and process conditions. These interactions are very important for the process ability and quality of the finished product.  The use of artificial intelligence in pharmaceutical technology has increased over the years, and the use of technology can save time and money while providing a better understanding of the relationships between different formulation and process parameters.
  7. 7. CT Participant Identifier Connected Machines Dosage error Detection Fraud detection Adm. workflow Assistance Virtual Nurshing… Robot-assisted Surgery estimated potential Artificial Intellegence 7 annual benefit for each application by 2026(in billon USD) 0 10 20 30 40 50 Fig: Estimated potential annual benefit for each application by 2026(in billon USD) Source: Accenture Analysis Total= $150 Billions Cybersecurity Advance Image Diagnosis Preliminary Diagnosis
  8. 8. 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
  9. 9. Three Steps Three elements of AI Computers and programs Massive amount of data Sophisticated algorithms The Turing test High performance parallel processors The Darmont Conference Intelligence of machines and the branch of computer science which aims to create it. “Machines will be capable, within 50 years, of doing any work a man can do.” –Herbert Simon, 1965 (AI innovator)
  10. 10. Artificial Intelligence Representation: Machines can retain data or information and come to be keener in the future as like human beings.
  11. 11. Focus areas of AI in an organization
  12. 12. Advantages of Artificial Intelligence Technology: AI is complex in nature and use. It is a combination of dense mixture of mathematics, computer science and other sciences. Helps the machines to reproduce the cognitive abilities of human beings. Advantages of AI are: Error Reduction - AI helps to reduce error and increases the accuracy with more precision. For Example, Intelligent robots. Difficult Exploration - Used in mining and fuel exploration sectors. Robots can perform more hard work and laborious work easily without exhaust. Daily Application - AI is useful for the daily application purpose. For example GPS system (helpful in long drives), corrects the errors in spelling. For example, Lady SIRI and Cortana robots. When anyone is posting photographs on social media' like twitter, face-book, the AI program identifies and tags the person's face. Digital Assistants - AI systems ‘avatar’ which are models of digital assistants are used by advanced organizations to reduce the need for human resources.
  13. 13. Advantages of Artificial Intelligence Technology: Repetitive Jobs - Humans can do only one task at a time. Machines can perform multi- tasking and can think faster than human beings. No Breaks / Limitless functions - Humans can work 8 hours per day with 2 or 3 breaks. Machines can work continuously with constant output. Machines do everything better than humans No risk of harm – For Example working at the fire stations mishap causes harm to the personnel. While machines, they don’t feel and have emotions. Also, If machines are broken, it is possible to mantle the parts. Medical Applications - Nowadays, the physicians are assessing the patients and analyzing the health risks with the help of AI. Increase technological Growth Rate - AI technology helps to find new chemical compounds and entities. For example – CADD, QSAR. Act as aids - AI technology can be used to serve children with disability or the elders on a 24/7 basis. (source for teaching and learning; security alerts in fires, robbery and in difficult conditions).
  14. 14. Types of Artificial Intelligence Technology: AI is a wide-ranging concept and can be classified into a number of ways. Based upon their calibre, AI system is classified as follows: 1. Weak intelligence or Artificial narrow intelligence (ANI) – This system is designed and trained to perform a narrow task, such as facial recognition, driving a car, playing chess, traffic signalling. E.g.: Apple SIRI virtual personal assistance, tagging in social media. 2. Artificial General Intelligence (AGI) or Strong AI - It is also called as Human Level AI. It has the ability to simplify human intellectual abilities. Due to this, when it exposed to an unfamiliar task, it has the ability to find the solution. AGI can perform all the things as humans. 3. Artificial Super intelligence (ASI): It is a brain power, which is more active than smart humans drawing, mathematics, space, etc; in each and every field from science to art. It ranges from the computer just little than the human to trillion times smarter than humans.
  15. 15. Types of Artificial Intelligence Technology: Arend Hintze, AI scientist classified the AI technology based upon their presence. They are as follows: 1. Type 1: This type of AI system is called as Reactive machines. E.g. Deep Blue, aIBM chess program which hit the chess champion Garry Kasparov in the 1990s. Another example is Google's AlphaGo. 2. Type 2: This type of AI systems is called as “Limited memory systems” which has ability to use past experiences for present & future problems. 3. Type 3: This type of AI system is called as “theory of mind”. It means that all the humans have their own thinking, intentions and desires which impact the decisions they make. 4. Type 4: These are called as self-awareness. The AI systems having the sense of self and consciousness. If the machine has self-awareness, it understands the condition and uses the ideas present in other's brain.
  16. 16. AI in field of Pharmacy • It is one of the top technologies shaping the future of pharmacy. • Pharma industries has been developing cure & treatment for centuries. Traditionally the design & manufacturing of drug requires several years, lengthy clinical trials & huge costs. • With the rise of 21st century technologies, this has been changing. • In future we will see completely different drug designs, manufacture & clinical trials.
  17. 17. Why AI in Pharma is a good idea ? • Pharmaceutical industry can accelerate innovation by using AI technologies. • The recent technological advancement that comes to mind would be artificial advancement such as visual perception, speech recognition, decision-making & translation between languages. • An estimate by IBM shows that entire healthcare domain has approx. 161 billion GB of data as of 2011. • With humongous data available in this domain, AI can help in analysing the data & presenting results that would help out in decision making, saving human effort, time, money & thus help save lives.
  18. 18. Imagine a Future where • AI is able to design new drugs • Find new drug combination • Deliver clinical trials within minutes • Drugs are not tested on real humans or animals, but on virtual model that are engineered to mimic the physiology of organs. • Robots help in the manufacturing of medication as well as their distribution • Counterfeiting drugs become almost impossible. • Block-chain technology secures the entire distribution channel. • Local pharmacists 3D prints personalised drugs in any shape & desired doses.
  19. 19. Applications of AI  Disease Identification  Radiology And Radiotherapy  Clinical Trial Research  Drug Discovery  Personalized Medicine & Rare Disease Identification
  20. 20. AI in Clinical Research & Clinical Trial Research  Cutting costs (Predictive analysis in identifying candidates for clinical trials)  Improving trial quality (Machine learning- to shape, direct clinical trials)  Improving trial time by almost half  Finding biomarkers and gene signatures that cause diseases  Reading volumes of text and data in seconds  Discovering involving new diagnostic tools and treatments for Alzimer’s disease, cancer, and other chronic and terminal illness.  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.
  21. 21. Drug Discovery  A study published by the Massachusetts Institute of Technology (MIT) has found that only 13.8% of drugs successfully pass clinical trials.  Furthermore, a company can expect to pay between $161 million to $2 billion for any drug to complete the entire clinical trials process and get FDA approval.  With this in mind, pharma businesses are using AI to increase the success rates of new drugs while decreasing operational costs at the same time.  Ideally, this would also translate to lower drug costs for patients, all while offering them more treatment choices.
  22. 22. 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: Form Rules)  Designing of the processes.
  23. 23. 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.  Berg (US biopharma company) is using AI to research and develop diagnostics and therapeutics in the fields of oncology, endocrinology, and neurology.
  24. 24. 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. 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.  The goal is to improve the accuracy of radiotherapy planning while minimizing damage to healthy organs at risk.
  25. 25. 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.
  26. 26. 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.  Using AI, body scans can detect cancer and other diseases early, as well as predict health issues people might face based on their genetics.  IBM Watson for Oncology is currently the leader in AI for personalized treatment decisions in the oncology space. It uses each patient’s medical information and history to optimize the treatment decision- making.
  27. 27. APPLICATION OF AI IN PHARMACEUTICAL RESEARCH: In Formulation : a) Controlled release tablets:  The first work in the use of neural networks for modeling pharmaceutical formulations was performed by Hussain and co-workers at the University of Cincinnati (OH, USA).  In various studies they modeled the in vitro release characteristics of a range of drugs dispersed in matrices prepared from various hydrophilic polymers. b) Immediate release tablets:  The networks produced were used to prepare three-dimensional plots of massing time, compression pressure and crushing strength, or drug release, massing time and compression pressure in an attempt to maximize tablet strength or to select the best lubricant.  Comparable neural network models were generated and then optimized using genetic algorithms.
  28. 28. In Product Development:  The pharmaceutical product development process is a multivariate optimization problem. It involves the optimization of formulation and process variables.  One of the most useful properties of artificial neural networks is their ability to generalize. These features make them suitable for solving problems in the area of optimization of formulations in pharmaceutical product development.  ANN models showed better fitting and predicting abilities in the development of solid dosage forms in investigations of the effects of several factors (such as formulation, compression parameters) on tablet properties (such as dissolution).  ANNs provided a useful tool for the development of micro emulsion-based drug- delivery systems.  ANNs can also be used to simulate aerosol behavior, with a view to employing this type of methodology in the evaluation and design of pulmonary drug-delivery systems.
  29. 29. Risks & Disadvantage Associated with AI • Prof. Stephen Hawking had said that human efforts to create machines that can think are a huge threat to the existence of human race. Development of complete human AI could mean that the human race would come to an end in the future. • High cost - AI needs huge costs as they are complex machines • Unemployment - AI can cause unemployment. • No Match For Human Brain Intelligence • No Improvement With Experience • No Original Creativity
  30. 30. AI Applications 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
  31. 31. Artificial intelligence in medicine : The virtual branch The virtual component is represented by Machine Learning, (also called Deep Learning)- mathematical algorithms that improve learning through experience. Three types of machine learning algorithms: 1. Unsupervised (ability to find patterns) 2. Supervised (classification and prediction algorithms based on previous examples) 3. Reinforcement learning (use of sequences of rewards and punishments to form a strategy for operation in a specific problem space)
  32. 32. Use of robots to deliver treatment..robotic surgery Use of robots to monitor effectiveness of treatment
  33. 33. Growth drivers of AI in healthcare  Increasing individual healthcare expenses  Larger Geriatric population  Imbalance between health workforce and patients  Increasing Global Health care expenditure  Continuous shortage of nursing and technician staff. The number of vacancies for nurses will be 1.2 million by 2020  AI is and will help medical practitioners efficiently achieve their tasks with minimal human intervention, a critical factor in meeting increasing patient demand.
  34. 34. Growth drivers of AI in healthcare
  35. 35. Conclusion AI is a big thing for pharma and Companies that are more flexible and adopt AI faster will likely gain a strategic advantage. In fact, experts anticipate that implementing AI will soon be necessary to compete in the industry. However, the transformation will not happen overnight. Instead, it will gradually occur over the next 10 or 20 years. By then, AI is expected to be integrated into most Pharma R&D operations and this will improve the drug development success rate and streamline R&D efforts.