Artificial intelligence in healthcare
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Artificial intelligence can help improve healthcare in several ways: 1. It can help doctors make more accurate diagnoses by analyzing large amounts of medical data. 2. AI is already being used in areas like radiology to identify diseases in medical images. 3. It shows promise in personalized treatment recommendations by analyzing individual patient data. 4. In the future, AI may be able to perform some medical tasks like surgery more precisely than humans.
Artificial intelligence in healthcare
- 1. Guide: 1. Yamini D. Shah M. Pharm, Ph.D., Professor & H.O.D of Pharmaceutics and Pharmaceutical Technology ARTIFICIAL INTELLIGENCE IN HEALTHCARE Department of Pharmaceutics and Pharmaceutical Technology . M. College of Pharmacy, Opp. Gujarat University, Navrangpura, Ahmedabad, Gujarat, India Prepared by: Shailvi Soni Enrollment no.:192280820012
- 2. Table of content Introduction Introduction to healthcare data How AI can help in healthcare… Application of AI in healthcare Clinical care AI in Radiology AI in Dermatology AI in Pathology AI in Drug Development AI in Cardiology: AI in Medicine: AI in Neuroscience Ai in cancer Similar advance approaches to apply ARTIFICIAL INTELLIGENCE in healthcare
- 3. Introduction Artificial Intelligence is a human intelligence research and prototype where a wise specialist is a system that knows its situation and conducts activities that amplify its risk of development. Artificial intelligence is not new, but the field has recently made rapid progress. To a limited extent, this has been empowered by advances in registering power and the tremendous volume of currently generated computerized knowledge. Significant innovation organizations, including Google, Microsoft and IBM, are pouring resources into the development of AI for medicinal services and research.
- 4. We understand that machines will not be replaced by human doctors within a reasonable time period, but AI may help doctors decide on better clinical decisions or even supplant human judgement in a certain utilitarian zones of medicinal services (e.g., radiology or neurology). The rising accessibility of healthcare information and the rapid improvement of big data analysis methods have made AI's ongoing productive applications in medicinal services conceivable. Incredible AI techniques, led by critical clinical investigations, will open up clinically relevant data covered in the enormous amount of knowledge, Introduction
- 5. The use of AI (machine learning) ML strategies, including Artificial Neural Networks (ANNs), is another way to deal with AI. Using an ANN approach, computer commands create an artificial "neurons" decision-making system that works in ways comparable to organic sensory systems. In medical science healthcare, ML-fueled clinical decision support system (CDSSs) had been proposed for picture survey and translation for radiology, dermatology, and pathology, and for improving the extension and precision of biomedical sign interpretation. Introduction
- 7. Introduction to Healthcare data: Healthcare data should be 'trained' by knowledge generated from clinical activities, such as screening, commitment, treatment task, etc., before AI structures can be sent in healthcare applications, in order to learn comparable subjects meetings, relationships between subject highlights and premium outcomes. This clinical information often exists in the form of socioeconomics, restorative notes, electronic medical gadget accounts, physical examinations, and clinical laboratories and images, which are not yet limited.
- 8. How AI can help in healthcare… Better healthcare results: Enable doctors to be diagnosed with increasingly accurate diagnostics and concentrate on avoidance skills. (9) Assure medical drug safety: Empower pharmaceutical manufacturers to quickly assess the consistency, adequacy and protection of new product expectations. (9) Get new treatments for faster recovery and market growth: Clinical preliminaries of Quicken using sources of certifiable knowledge.(9) Control costs: Improve medical clinics' clinical presentation by improving staffing assets and making a change to reduce the number of
- 9. APPLICATIONS OF AI IN HEALTHCARE 1. CLINICAL CARE: The use of AI to examine clinical information, research publications, and expert rules could inform treatment choices as follows: Medical imaging – Therapeutic outputs have been collected and put away methodically for quite a while and are readily available for the preparation of AI systems. The cost and time associated with examining could be reduced by AI, conceivably allowing more likely target treatment. In identifying conditions like pneumonia, skin and breast cancer, and eye ailments, AI has shown promising results. Echocardiography – Tested in research, the Ultromics framework utilizes AI to investigate echocardiography
- 10. Screening for neurological conditions – AI systems are being created to analyze samples to predict insane scenes and detect and track side effects of neurological disorders, example used in Parkinson's infection. Surgery- AI-constrained automated tools have been used in research to complete complex tasks in keyhole surgery, such as tying knots to close injuries. Image Based Diagnosis: As of now the best field of medical AI applications is potentially computerized restorative picture analysis. Numerous restorative fortes depend on picture- APPLICATIONS OF AI IN HEALTHCARE
- 11. 2. AI in Radiology: APPLICATIONS OF AI IN HEALTHCARE
- 12. 3. AI in Dermatology: A major role in the diagnosis of various forms of skin injury like skin melanoma. Skin Image Analysis: Using AI and PC vision, organizations create applications and gadgets to analyses images to anticipate and prevent the occurrence of skin sickness. Skincare Treatment Personalization: Companies are building a suggestion engine to customized skincare treatment recommendations to the form of skin consumer. APPLICATIONS OF AI IN HEALTHCARE
- 13. 4. AI in Pathology: APPLICATIONS OF AI IN HEALTHCARE
- 14. 5. AI in Drug Development: : Some Applications of AI in Drug Development are as follows: AI in synthesis of drug-like compounds AI in drug repurposing AI in selection of a population for clinical trials AI in finding the hit or lead APPLICATIONS OF AI IN HEALTHCARE
- 15. 6. AI in Cardiology: APPLICATIONS OF AI IN HEALTHCARE
- 16. 7. AI in Medicine: Machine Learning (also known as Deep Learning) speaks to the virtual component that is addressed through numerical calculations that enhance learning through experience. There are three types of AI calculations: (i) unsupervised (capacity to discover designs), (ii) supervised (past model-dependent grouping and forecast algorithm), and (iii) fortification learning (utilization of arrangements of remunerations and disciplines to frame a technique for task in a particular issue space) APPLICATIONS OF AI IN HEALTHCARE
- 17. In an assortment of prescription courses, artificial intelligence may be used. Four models are given here: I. Clinical information analysis: The ability of artificial intelligence to process common language allows it to peruse clinical content from any source and distinguish therapeutic and social ideas from order and code. II. Knowledge for patient information: It explains the historical context of their consideration of patient’s problems and may provide a medical record with a psychological synopsis. III. Patient similarity: As compared to static patient partners, AI helps scientists to make complex patient accomplices. It also allows an appreciation of which treatment approach fits best for a particular group of patients. IV. Therapeutic section of knowledge: Experts can uncover material in unstructured medical writing with AI innovations to endorse hypotheses, helping to expose APPLICATIONS OF AI IN HEALTHCARE
- 18. 8. AI in Neurosciences: AI In neurological OT (Occupational Therapy): Currently, it is recognized that cerebral tumors cause a significant redesign of the functional system. Useful enrollment has been helped by simulated intelligence showing greater optimum percentage than anatomical enlistment. Brain tumor removal has been accepted as a perfect robotic surgery technique. It involves a view of the cell by means of an automated system and a subsequent adaptation of its behavior to evolving natural parameters. APPLICATIONS OF AI IN HEALTHCARE
- 19. 9. AI in Cancer: Artificial Intelligence (AI) take incredible parts in the subjective translation by master clinicians of malignant growth imaging, including volumetric outline of tumors after some time, extrapolation of the tumor genotype and natural course from its radiographic phenotype, clinical outcome prediction, and assessment of the impact of infection and treatment on contiguous organs. In the underlying translation of images, artificial intelligence will be in robotize forms but the executive decisions on whether to monitor a mediation, and the subsequent interpretation to a worldview that has yet to be conceived. APPLICATIONS OF AI IN HEALTHCARE
- 20. AI Applications in Cancer Imaging: APPLICATIONS OF AI IN HEALTHCARE
- 21. Similar advance approaches to apply ARTIFICIAL INTELLIGENCE
- 22. Individual Health Virtual Assistant: The overwhelming majority are approaching a mobile phone in the present age. Propelled AI equations, such as Cortana, Google Assistant, and Siri, influence colleagues. They can offer tremendous benefits to consumers at the moment when combined with healthcare applications. As an individual well-being associate, medicinal services applications will go around. They will also be used to provide drug warnings, and it will also be conceivable for human-like cooperation. Artificial intelligence will also help to assist patients when the clinical faculty is not available as an Similar advance approaches to apply ARTIFICIAL INTELLIGENCE
- 23. Artificial intelligence improves “The Quality of Sleep”: AI innovation is constantly trying to improve the general nature of rest, from AI controlled savvy bedding to infant screens, rest applications, Two bedtime songs were made by a medical services organization named AXA PPP, one by AI and the other by individuals. The AI framework could figure out mood and concordance with profound learning to bring about another organization. This piece was then transformed with the help of a Similar advance approaches to apply ARTIFICIAL INTELLIGENCE
- 24. Medicinal Imaging Analysis: Radiology is another significant field in medicinal services that utilizes AI. With analytical procedures, AI frameworks can help. It can look at therapeutic images such as X-beams, CT reviews, MRIs, and so on and can criticize what a human eye can skip. Therapeutic imaging research, along these lines, turns out to be significantly more accurate and viable. The odds of blunders are decreased. IBM Watson is a model for a livelihood. It can provide clinicians with proof-based treatment options for cancer patients in the field of oncology, Similar advance approaches to apply ARTIFICIAL INTELLIGENCE
- 25. Precision Medicine: Genomics is the part of atomic science that manages genome structure, growth, ability, and mapping. It searches for the connections from the data obtained from the DNA to the disease. It is conceivable to detect cancer and some vascular infections at the moment when combined with AI at an all-around starting period. It can also predict the medical issues that patients may face, depending on their characteristics. AI also assists in the machining of medicinal parts in this way, minimizing the chances of operation malfunction. Similar advance approaches to apply ARTIFICIAL INTELLIGENCE
- 26. Healthcare Bots: Innovation in artificial intelligence is also making inroads in the customer service space. Patients can almost probably interact through a talk window or by phone with these AI bots on the web. Social insurance bots can be used to arrange meetings with the provider of medicinal services for the patient. Bots may also support patients with their medication by providing 24 x 7 assistance, they will likewise boost customer service. Similar advance approaches to apply ARTIFICIAL INTELLIGENCE
- 27. References: Murali N., Sivakumaran N., “Artificial Intelligence in Healthcare – A Review.” , International Journal of Modern Computation, Information and Communication Technology, 2018,2581-5954. Pichika N., Mak K., Rao M.,“ Artificial intelligence in drug development: present status and future prospects”, Drug Discovery Today, Elsevier, 2018, 251-269. Kun-Hsing Yu, Andrew L., Kohane I. ,”Artificial intelligence in healthcare. USA”, Journal of Biomedical Engineering, 2018, 2, 8-27. Hosny A., Parmar C., Quackenbush J., Lawrence H. Aerts L “Artificial intelligence in radiology”. Nature Reviews Cancer, 2018, 1-19. Johnson K., Torres J., Glicksberg B., Shameer K., Riccardo Miotto R.,“Artificial Intelligence in Cardiology”, International Journal of Communation technology, 2018, 7 1 , 0735-1097.
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