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The topic groups are: AI and cardiovascular disease management, Dermatology, falls among the elderly, Histopathology, neurological disorders, outbreak detection, Ophthalmology, Psychiatry, snakebite and snake identification, symptom assessment, Tuberculosis, volumetric chest computed tomography, diagnosis of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, primary and secondary Diabetes prediction, detection of falsified medicine, Malaria detection, maternal and child health, Radiology and AI for Endoscopy. (...) In addition, the data must originate from a variety of sources so that it can be determined whether an AI algorithm can generalize across different conditions, locations, or settings (e.g. across different people, hospitals, and/or measurement devices). The format/properties of the data serving as input to the AI and of the output expected from the AI, as well as the benchmarking metrics are agreed upon and specified by the topic group.
Language:English
Score: 1213014.6 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-J-025-A02.docx
Data Source: un
The topic groups are: AI and cardiovascular disease management, Dermatology, falls among the elderly, Histopathology, neurological disorders, outbreak detection, Ophthalmology, Psychiatry, snakebite and snake identification, symptom assessment, Tuberculosis, volumetric chest computed tomography, diagnosis of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, primary and secondary Diabetes prediction, detection of falsified medicine, Malaria detection, maternal and child health, Radiology and AI for Endoscopy. (...) In addition, the data must originate from a variety of sources so that it can be determined whether an AI algorithm can generalize across different conditions, locations, or settings (e.g. across different people, hospitals, and/or measurement devices). The format/properties of the data serving as input to the AI and of the output expected from the AI, as well as the benchmarking metrics are agreed upon and specified by the topic group.
Language:English
Score: 1213014.6 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-L-025-A02.docx
Data Source: un
The topic groups are: AI and cardiovascular disease management, Dermatology, falls among the elderly, Histopathology, neurological disorders, outbreak detection, Ophthalmology, Psychiatry, snakebite and snake identification, symptom assessment, Tuberculosis, volumetric chest computed tomography, diagnosis of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, primary and secondary Diabetes prediction, detection of falsified medicine, Malaria detection, maternal and child health, Radiology and AI for Endoscopy. (...) In addition, the data must originate from a variety of sources so that it can be determined whether an AI algorithm can generalize across different conditions, locations, or settings (e.g. across different people, hospitals, and/or measurement devices). The format/properties of the data serving as input to the AI and of the output expected from the AI, as well as the benchmarking metrics are agreed upon and specified by the topic group.
Language:English
Score: 1213014.6 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-N-025-A02.docx
Data Source: un
The topic groups are: AI and cardiovascular disease management, Dermatology, falls among the elderly, Histopathology, neurological disorders, outbreak detection, Ophthalmology, Psychiatry, snakebite and snake identification, symptom assessment, Tuberculosis, volumetric chest computed tomography, diagnosis of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, primary and secondary Diabetes prediction, detection of falsified medicine, Malaria detection, maternal and child health, Radiology and AI for Endoscopy. (...) In addition, the data must originate from a variety of sources so that it can be determined whether an AI algorithm can generalize across different conditions, locations, or settings (e.g. across different people, hospitals, and/or measurement devices). The format/properties of the data serving as input to the AI and of the output expected from the AI, as well as the benchmarking metrics are agreed upon and specified by the topic group.
Language:English
Score: 1213014.6 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-K-025-A02.docx
Data Source: un
The topic groups are: AI and cardiovascular disease management, Dermatology, falls among the elderly, Histopathology, neurological disorders, outbreak detection, Ophthalmology, Psychiatry, snakebite and snake identification, symptom assessment, Tuberculosis, volumetric chest computed tomography, diagnosis of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, primary and secondary Diabetes prediction, detection of falsified medicine, Malaria detection, maternal and child health, Radiology and AI for Endoscopy. (...) In addition, the data must originate from a variety of sources so that it can be determined whether an AI algorithm can generalize across different conditions, locations, or settings (e.g. across different people, hospitals, and/or measurement devices). The format/properties of the data serving as input to the AI and of the output expected from the AI, as well as the benchmarking metrics are agreed upon and specified by the topic group.
Language:English
Score: 1213014.6 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-M-025-A02.docx
Data Source: un
The topic groups are: AI and cardiovascular disease management, Dermatology, falls among the elderly, Histopathology, neurological disorders, outbreak detection, Ophthalmology, Psychiatry, snakebite and snake identification, symptom assessment, Tuberculosis, volumetric chest computed tomography, diagnosis of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, primary and secondary Diabetes prediction, detection of falsified medicine, Malaria detection, maternal and child health, Radiology and AI for Endoscopy. (...) In addition, the data must originate from a variety of sources so that it can be determined whether an AI algorithm can generalize across different conditions, locations, or settings (e.g. across different people, hospitals, and/or measurement devices). The format/properties of the data serving as input to the AI and of the output expected from the AI, as well as the benchmarking metrics are agreed upon and specified by the topic group.
Language:English
Score: 1213014.6 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-O-025-A02.docx
Data Source: un
Other health workers Includes a large number of occupations such as dieticians and nutritionists, medical assistants, occupational therapists, operators of medical and dentistry equipment, optometrists and opticians, physiotherapists, podiatrists, prosthetic/orthetic engineers, psychologists, respiratory therapists, speech pathologists, medical trainees and interns. (...) Associate professional nurse, Assistant nurse; Licensed practical nurse, Enrolled nurse Professional nurse-2221, Specialist nurse-2221, Associate professional midwife-3222, Community nurse attendant-3253, Nursing aide (hospital or clinic)-5321, Nursing aide (home)-5322 Occupations included in this category normally require formal training in nursing services. (...) Human Resources for Health  Country Profile Template 30 Occupation Code Definition Notes Examples of occupations included here Excluded occupations - classified elsewhere Additional comments Dentists 2261 Dentists apply the principles and procedures of modern dentistry in diagnosing, treating and preventing diseases, injuries and abnormalities of the teeth, mouth, jaws and associated tissues.
Language:English
Score: 1211484.9 - https://www.who.int/workforcea...ance/knowledge/resources/2.pdf
Data Source: un
Christian Lovis Chairman, Division of Medical Information Sciences, University Hospitals of Geneva (HUG) Christian is professor of clinical informatics at the university of Geneva and chairman of the division of medical information sciences at the university hospitals of Geneva. (...) Severence's clients include advising the C suite of the top 25 global Health Care Insurance and Hospital Groups, the top 20 Pharma companies and two of the top Global Investment Banks.  (...) For example, he is currently working with first-tier hospitals in China to investigate how to apply deep learning algorithms in clinical setups such as pathology labs.
Language:English
Score: 1208734.9 - https://www.itu.int/en/ITU-T/W...0180925/Pages/Biographies.aspx
Data Source: un
WHO | Elsevier offers 950 new health titles to Research4Life Access Home Alt+0 Content Alt+2 Search Search HINARI Submit Language عربي 中文 English Français Русский Español Menu Hinari About Hinari Access the content Eligibility Partners Training materials Promoting Hinari Contributions Elsevier offers 950 new health titles to Research4Life 18 October 2011 - Building on the existing Elsevier science and technology books collection already available, the new electronic books cover Clinical Medicine (438 titles), Health Professions (332 titles), Veterinary Medicine (174 titles), and Clinical Dentistry (24 titles). These include seminal works such as Clinical Gynecology, Cancer Pain, Pain Medicine, Spinal Cord Injuries, and Saunders Manual of Small Animal Practice and will be accessible to users by the end of the year.
Language:English
Score: 1206311.3 - https://www.who.int/hinari/new...th_titles_to_Research4Life/en/
Data Source: un
UNITED NATIONS EDUCATIONAL AND TRANING PROGRAMME FOR SOUTHERN AFRICA : REPORT OF THE SECRETARY-GENERAL
University studies 1 Economics and social science 1 Secretarial and English studies (Diploma) 2 Agronomy (Diploma) 1 Architecture 1 Civil engineering 3 Commercial studies 1 Economics 1 French and African linguistics 1 Geography 2 Hospital administration 3 Mathematics and physics 1 Medicine 9 Nursing 3 Psychology 1 Secondary education (Nursing) 3 Social studies 1 Topography 1 A/34/57l English Annex 11 Page 1 Total 1 1 1 2 32 TOTAL 37 / ..... (...) South Africa Field of study Advanced secondary education Commerce Economics Education Nursing Pharmacy (technical) Sciences Anplied psycholor,y (post-graduate) Business administration Education Education (post-graduate) English Financial manar;ement (post-graduate) International relations Pharmacology (post-graduate) Political science Science Sociology Computer science Dentistry Medicine Number of awards 1 4 2 1 1 Number of awards 1 1 4 2 2 1 4 1 1 1 1 1 1 2 1 2 2 1 2 1 32 Total 2- '[OTAL 9 Total 15 14 35 / ... (...) Social science (post-graduate) Veterinary science Aeronautical engineering Accounting Agricultural economics Aviation mechanics Biomedical engineering Business administration Business administration (post-graduate) Commerce Communications Comparative government (post-graduate) Computer science Development planning (post-graduate) Dentistry Number of awards 4 1 1 1 1 1 2 3 1 1 2 1 1 1 1 1 2 1 1 1 1 3 1 1 1 1 1 6 1 1 1 14 1 2 4 1 8 1 3 Total l~2 8 / ...
Language:English
Score: 1197397.9 - daccess-ods.un.org/acce...sf/get?open&DS=A/34/571&Lang=E
Data Source: ods