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Unauthorized Act 186 62 Medicine 188 63 Dentistry 191 64 Veterinary Medicine 194 65 Nursing 197 66 Midwifery 204 67 Pharmacy 209 Subchapter A. (...) Licensing of Wholesalers and Manufacturers 218 Subchapter E. Regulation of Hospital Dispensaries 219 Subchapter F. Oversight of Licensed Establishments 219 iv \\\DC - 090334/000005 - 3193277 v1 1 \\\DC - 090334/000005 - 3193277 v1 TITLE 33 PUBLIC HEALTH LAW PART I GENERAL PROVISIONS Chapter 1. (...) (dd) "Public building" means a building used or constructed or adapted to be used either ordinarily or occasionally as a place of public worship or as a hospital, college, school, theatre, public hall, or as a public place of assembly for persons admitted by tickets or otherwise, or used or adapted to be used for any public purpose.
Language:English
Score: 1156805.4 - https://www.wto.org/english/th..._e/lbr_e/WTACCLBR15_LEG_42.pdf
Data Source: un
Deliverable Updated initial draft editor Availability* 0 Overview of the FG-AI4H deliverables Shan Xu (CAICT, China) M-044 0.1 FG-AI4H terms and definitions Markus Wenzel (Fraunhofer HHI, Germany) M-032-R02 1 AI4H ethics considerations Andreas Reis (WHO) K-028 ( K-028-A01 ) 2 Overview of regulatory considerations on artificial intelligence for health Jackie Ma (Fraunhofer HHI, Germany), Khair ElZarrad & Rose Purcell (FDA, USA) M-052 2.1 Mapping of IMDRF essential principles to AI for health software Luis Oala (Fraunhofer HHI, Germany), Pradeep Balachandran (Technical Consultant eHealth, India), Pat Baird (Philips, USA), Thomas Wiegand (Fraunhofer HHI, Germany) G-038 , G-038-A01 2.2 Good practices for health applications of machine learning: Considerations for manufacturers and regulators Pradeep Balachandran (India) and Christian Johner (Johner Institut, Germany) M-053 3 AI4H requirement specifications Pradeep Balachandran (India) M-037 4 AI software life cycle specification Pat Baird (Philips, USA) J-033 ( L-046 ) 5 Data specification Marc Lecoultre (MLlab.AI, Switzerland) G-205 5.1 Data requirements [ Marc Lecoultre (MLlab.AI, Switzerland)]** I-044 5.2 Data acquisition Rajaraman (Giri) Subramanian (Calligo Tech, India), Vishnu Ram (India) G-205-A02 5.3 Data annotation specification Shan Xu (CAICT, China), Harpreet Singh (ICMR, India), Sebastian Bosse (Fraunhofer HHI, Germany) M-045 5.4 Training and test data specification Luis Oala (Fraunhofer HHI, Germany), Pradeep Balachandran (India) I-034 ( L-045 ) 5.5 Data handling Marc Lecoultre (MLlab.AI, Switzerland) I-045 5.6 Data sharing practices Ferath Kherif (CHUV, Switzerland), Banusri Velpandian (ICMR, India), WHO Data Team L-044 6 AI training best practices specification Xin Ming Sim and Stefan Winkler (AI Singapore) K-037 7 AI for health evaluation considerations Markus Wenzel (Fraunhofer HHI, Germany) M-036 7.1 AI4H evaluation process description Sheng Wu (WHO) G-207-A01 7.2 AI technical test specification Auss Abbood (Robert Koch Institute, Germany) I-027 ( L-051 ) 7.3 Data and artificial intelligence assessment methods (DAISAM) reference Luis Oala (Fraunhofer HHI, Germany) K-045 ( L-052 ) 7.4 Clinical evaluation of AI for health Naomi Lee (Lancet, UK), Eva Weicken (Fraunhofer HHI, Germany), Shubhanan Upadhyay (ADA Health, Germany) M-040 8 AI4H scale-up and adoption Sameer Pujari (WHO), Yu ZHAO and Javier Elkin [Previously: Robyn Whittaker (New Zealand)] – ( K-052 ) 9 AI4H applications and platforms Manjeet Chalga (ICMR, India) L-050 9.1 Mobile applications Khondaker Mamun (UIU, Bangladesh), Manjeet Chalga (ICMR, India) I-048 9.2 Cloud-based AI applications Khondaker Mamun (UIU, Bangladesh) I-049 10 AI4H use cases: Topic description documents Eva Weicken (Fraunhofer HHI, Germany) M-031 10.1 Cardiovascular disease management (TG-Cardio) Benjamin Muthambi (Watif Health, South Africa) M-006-A01 10.2 Dermatology (TG-Derma) Weihong Huang (Xiangya Hospital Central South University, China) NOTE – Maria Vasconcelos (Fraunhofer, Portugal) resigned from the role. M-007-A01 10.3 Diagnosis of bacterial infection and anti-microbial resistance (TG-Bacteria) Nada Malou (MSF, France) M-008-A01 10.4 Falls among the elderly (TG-Falls) Pierpaolo Palumbo (University of Bologna, Italy); Inês Sousa (Fraunhofer Portugal) M-012-A01 10.5 Histopathology (TG-Histo) Frederick Klauschen (LMU Munich & Charité Berlin, Germany) M-013-A01 10.6 Malaria detection (TG-Malaria) Rose Nakasi (Makerere University, Uganda) M-014-A01 10.7 Maternal and child health (TG-MCH) Raghu Dharmaraju (Wadhwani AI, India) and Alexandre Chiavegatto Filho (University of São Paulo, Brazil) M-015-A01 10.8 Neurological disorders (TG-Neuro) Marc Lecoultre (MLlab.AI, Switzerland) M-016-A01 10.9 Ophthalmology (TG-Ophthalmo) Arun Shroff (MedIndia) M-017-A01 10.10 Outbreak detection (TG-Outbreaks) Auss Abbood (Robert Koch Institute, Germany) and Stéphane Ghozzi (HZI, Germany) M-018-A01 10.11 Psychiatry (TG-Psy) Nicolas Langer (ETH Zurich, Switzerland) M-019-A01 10.12 AI for radiology (TG-Radiology) Darlington Ahiale Akogo (minoHealth AI Labs, Ghana) M-023-A01 10.13 Snakebite and snake identification (TG-Snake) Rafael Ruiz de Castaneda (UniGE, Switzerland) M-020-A01 10.14 Symptom assessment (TG-Symptom) Henry Hoffmann (Ada Health, Germany) M-021-A01 10.15 Tuberculosis (TG-TB) Manjula Singh (ICMR, India) M-022-A01 10.16 Volumetric chest CT (TG-DiagnosticCT) Kuan Chen (Infervision, China) M-009-A01 10.17 Dental diagnostics and digital dentistry (TG-Dental) Falk Schwendicke and Joachim Krois (Charité Berlin, Germany); Tarry Singh (deepkapha.ai, Netherlands) M-010-A01 10.18 Falsified Medicine (TG-FakeMed) Franck Verzefé (TrueSpec-Africa, DRC) M-011-A01 10.19 Primary and secondary diabetes prediction (TG-Diabetes) Andrés Valdivieso (Anastasia.ai, Chile) M-024-A01 10.20 AI for endoscopy (TG-Endoscopy) Jianrong Wu (Tencent Healthcare, China) M-025-A01 10.21 AI for musculoskeletal medicine (TG-MSK) Peter Grinbergs (EQL, UK), Yura Perov (UK) M-026-A01 10.22 AI for human reproduction and fertility (TG-Fertility) Susanna Brandi , Eleonora Lippolis , (Merck KGaA, Darmstadt, Germany) M-027-A01 10.23 AI in sanitation for public health (TG-Sanitation) Khahlil Louisy (Institute for Technology & Global Health, ITGH, US), Alexander Radunsky (ITGH, US) M-028-A01 10.24 AI for point-of care diagnostics (TG-POC) Nina Linder , University of Helsinki, Finland M-029-A01 NOTES * The document numbers indicated reflect the status as of the end of the e-meeting J.
Language:English
Score: 1153636 - https://www.itu.int/en/ITU-T/f...ocuments/all/FGAI4H-M-200.docx
Data Source: un
Deliverable Updated initial draft editor Availability* 0 Overview of the FG-AI4H deliverables Shan Xu (CAICT, China) L-039 1 AI4H ethics considerations Andreas Reis (WHO) K-028 ( K-028-A01 ) 2 AI4H regulatory best practices Jackie Ma (Fraunhofer HHI, Germany), Khair ElZarrad & Rose Purcell (FDA, USA) L-047 2.1 Mapping of IMDRF essential principles to AI for health software Luis Oala (Fraunhofer HHI, Germany), Pradeep Balachandran (Technical Consultant eHealth, India), Pat Baird (Philips, USA), Thomas Wiegand (Fraunhofer HHI, Germany) G-038 , G-038-A01 2.2 Good practices for health applications of machine learning: Considerations for manufacturers and regulators Pradeep Balachandran (India) and Christian Johner (Johner Institut, Germany) L-037 3 AI4H requirement specifications Pradeep Balachandran (India) L-038 4 AI software life cycle specification Pat Baird (Philips, USA) J-033 ( L-046 ) 5 Data specification Marc Lecoultre (MLlab.AI, Switzerland) G-205 5.1 Data requirements [ Marc Lecoultre (MLlab.AI, Switzerland)]** I-044 5.2 Data acquisition Rajaraman (Giri) Subramanian (Calligo Tech, India), Vishnu Ram (India) G-205-A02 5.3 Data annotation specification Shan Xu (CAICT, China), Harpreet Singh (ICMR, India), Sebastian Bosse (Fraunhofer HHI, Germany) K-048 5.4 Training and test data specification Luis Oala (Fraunhofer HHI, Germany), Pradeep Balachandran (India) I-034 ( L-045 ) 5.5 Data handling Marc Lecoultre (MLlab.AI, Switzerland) I-045 5.6 Data sharing practices Ferath Kherif (CHUV, Switzerland), Banusri Velpandian (ICMR, India), WHO Data Team L-044 6 AI training best practices specification Xin Ming Sim and Stefan Winkler (AI Singapore) K-037 7 AI for health evaluation considerations Markus Wenzel (Fraunhofer HHI, Germany) L-036 7.1 AI4H evaluation process description Sheng Wu (WHO) G-207-A01 7.2 AI technical test specification Auss Abbood (Robert Koch Institute, Germany) I-027 ( L-051 ) 7.3 Data and artificial intelligence assessment methods (DAISAM) reference Luis Oala (Fraunhofer HHI, Germany) K-045 ( L-052 ) 7.4 Clinical evaluation of AI for health Naomi Lee (Lancet, UK), Eva Weicken (Fraunhofer HHI, Germany), Shubhanan Upadhyay (ADA Health, Germany) L-040 8 AI4H scale-up and adoption Sameer Pujari (WHO), Yu ZHAO and Javier Elkin [Previously: Robyn Whittaker (New Zealand)] – ( K-052 ) 9 AI4H applications and platforms Manjeet Chalga (ICMR, India), Aveek De (CMS, India) L-050 9.1 Mobile applications Khondaker Mamun (UIU, Bangladesh), Manjeet Chalga (ICMR, India) I-048 9.2 Cloud-based AI applications Khondaker Mamun (UIU, Bangladesh) I-049 10 AI4H use cases: Topic description documents Eva Weicken (Fraunhofer HHI, Germany) L-004 10.1 Cardiovascular disease management (TG-Cardio) Benjamin Muthambi (Watif Health, South Africa) L-006-A01 10.2 Dermatology (TG-Derma) Weihong Huang (Xiangya Hospital Central South University, China) NOTE – Maria Vasconcelos (Fraunhofer, Portugal) resigned from the role. L-007-A01 10.3 Diagnosis of bacterial infection and anti-microbial resistance (TG-Bacteria) Nada Malou (MSF, France) L-008-A01 10.4 Falls among the elderly (TG-Falls) Pierpaolo Palumbo (University of Bologna, Italy); Inês Sousa (Fraunhofer Portugal) L-012-A01 10.5 Histopathology (TG-Histo) Frederick Klauschen (LMU Munich & Charité Berlin, Germany) L-013-A01 10.6 Malaria detection (TG-Malaria) Rose Nakasi (Makerere University, Uganda) L-014-A01 10.7 Maternal and child health (TG-MCH) Raghu Dharmaraju (Wadhwani AI, India) and Alexandre Chiavegatto Filho (University of São Paulo, Brazil) L-015-A01 10.8 Neurological disorders (TG-Neuro) Marc Lecoultre (MLlab.AI, Switzerland) L-016-A01 10.9 Ophthalmology (TG-Ophthalmo) Arun Shroff (MedIndia) L-017-A01 10.10 Outbreak detection (TG-Outbreaks) Auss Abbood (Robert Koch Institute, Germany) and Stéphane Ghozzi (HZI, Germany) L-018-A01 10.11 Psychiatry (TG-Psy) Nicolas Langer (ETH Zurich, Switzerland) L-019-A01 10.12 AI for radiology (TG-Radiology) Darlington Ahiale Akogo (minoHealth AI Labs, Ghana) L-023-A01 10.13 Snakebite and snake identification (TG-Snake) Rafael Ruiz de Castaneda (UniGE, Switzerland) L-020-A01 10.14 Symptom assessment (TG-Symptom) Henry Hoffmann (Ada Health, Germany) L-021-A01 10.15 Tuberculosis (TG-TB) Manjula Singh (ICMR, India) L-022-A01 10.16 Volumetric chest CT (TG-DiagnosticCT) Kuan Chen (Infervision, China) L-009-A01 10.17 Dental diagnostics and digital dentistry (TG-Dental) Falk Schwendicke and Joachim Krois (Charité Berlin, Germany); Tarry Singh (deepkapha.ai, Netherlands) L-010-A01 10.18 Falsified Medicine (TG-FakeMed) Franck Verzefé (TrueSpec-Africa, DRC) L-011-A01 10.19 Primary and secondary diabetes prediction (TG-Diabetes) Andrés Valdivieso (Anastasia.ai, Chile) L-024-A01 10.20 AI for endoscopy (TG-Endoscopy) Jianrong Wu (Tencent Healthcare, China) L-025-A01 10.21 AI for Musculoskeletal medicine (TG-MSK) Peter Grinbergs (EQL, UK), Yura Perov (UK) L-026-A01 10.22 AI for human reproduction and fertility (TG-Fertility) Susanna Brandi , Eleonora Lippolis , (Merck KGaA, Darmstadt, Germany) Proposal: L-034 (Merck KGaA, Darmstadt, Germany) 10.23 AI in sanitation for public health (TG-Sanitation) Khahlil Louisy (Institute for Technology & Global Health, ITGH, US), Alexander Radunsky (ITGH, US) Proposal: L035 (ITGI, US) 10.24 AI for point-of care diagnostics (TG-POC) Nina Linder , University of Helsinki, Finland Proposal: L033 (Helsinki Univ., Finland) NOTES * The document numbers indicated reflect the status as of the end of the e-meeting J.
Language:English
Score: 1153636 - https://www.itu.int/en/ITU-T/f...ocuments/all/FGAI4H-L-200.docx
Data Source: un
Six other groups are starting their activities: diagnoses of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, AI-based detection of falsified medicine, malaria detection, maternal and child health, and radiotherapy. (...) 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. (...) Falls are one of the most common health problems in the elderly population, about a third of community-dwelling adults aged 65 years or older fall each year, and these events represent more than 50% of the hospitalizations due to lesions in this age group. Falls are also considered one of the main causes for loss of independence and institutionalization.
Language:English
Score: 1151741 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-H-012-A02.docx
Data Source: un
I couldn’t speak Estonian as well as I do now, but I asked someone if he knew where the hospital was. He said, “Yeah, of course I know.” He took me by the hand, and brought me to the hospital. (...) Shorok wants to study dentistry at the University of Tartu. The last question came in via Facebook: “How much do you feel like a refugee?
Language:English
Score: 1151741 - https://www.unhcr.org/neu/4341...-opinion-festival-estonia.html
Data Source: un
Six other groups are starting their activities: diagnoses of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, AI-based detection of falsified medicine, malaria detection, maternal and child health, and radiotherapy. (...) 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. (...) Falls are one of the most common health problems in the elderly population, about a third of community-dwelling adults aged 65 years or older fall each year, and these events represent more than 50% of the hospitalizations due to lesions in this age group. Falls are also considered one of the main causes for loss of independence and institutionalization.
Language:English
Score: 1151741 - https://www.itu.int/en/ITU-T/f...ents/all/FGAI4H-I-012-A02.docx
Data Source: un
Six other groups are starting their activities: diagnoses of bacterial infection and anti-microbial resistance, dental diagnostics and digital dentistry, AI-based detection of falsified medicine, malaria detection, maternal and child health, and radiotherapy. (...) 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. (...) Falls are one of the most common health problems in the elderly population, about a third of community-dwelling adults aged 65 years or older fall each year, and these events represent more than 50% of the hospitalizations due to lesions in this age group. Falls are also considered one of the main causes for loss of independence and institutionalization.
Language:English
Score: 1151741 - https://www.itu.int/en/ITU-T/f.../Documents/tg/CfP-TG-Falls.pdf
Data Source: un
UNITED NATIONS
Annual nationwide mercury emissions from hospital and medical/infectious waste incinerators had been reduced from about 51 tons in 1990 to 0.2 tons in 2005. (...) In 2009, USEPA and the Marquette University’s School of Dentistry had developed an environmentally responsible dentistry teaching module to educate dental students on proper amalgam waste management. (...) The module highlighted ADA best management practices for amalgam waste and encouraged dental students to practice environmentally responsible dentistry. (f) Vehicles 64. In accordance with the EU proposal, vehicles placed on the market after 1 July 2012 should not contain mercury-containing materials and components exceeding 0.1 per cent mercury by weight in homogenous materials.
Language:English
Score: 1143132.2 - daccess-ods.un.org/acce...=ECE/EB.AIR/WG.5/2010/9&Lang=E
Data Source: ods
UNITED NATIONS EDUCATIONAL AND TRAINING PROGRAMME FOR SOUTHERN AFRICA : REPORT OF THE SECRETARY-GENERAL
African linguistics . . . Graphics Hospital administration, . . . . . Kine-Physiotherapy . laboratory techniques Mathenat i cs Medicine Nurs ing Personnel managenent Phamacy ? (...) l_ 1 1 Seience University studies Comput er science Physical education(post graduate ) Dentistry I'ledicine Econonics anil buginess atlnini st rat ion Nuclear physics (poet-grsduate ) Social ped.agoCy Pharnacy Architecture Civil engineering Dental surgery Medicine Microbiolory Vet erinary and animsl husbanttry . . (...) Southern Btrodesia (continued) Country of study Field of study Number of awards Total UNITED STATES ( continued) ZAMBIA Education (post-gractuate) . . . . .' Dentistry Blectronics Srgineering..t. Hutran lesources and martpover deveLopnent (post-graduate)' . . .
Language:English
Score: 1143132.2 - daccess-ods.un.org/acce...sf/get?open&DS=A/31/268&Lang=E
Data Source: ods
UNITED NATIONS. LAWS AND REGULATIONS. COMMUNICATED IN COMPLIANCE WITH THE TERMS OF THE COVENTION FOR LIMITING THE MANUFACTURE AND REGULATING THE DISTRIBUTION OF NARCOTIC DRUGS OF 13 JULY 1931. QUEENSLAND
'llospi tal" - (a) A hospital established by the Board of a Hospital~ District constituted under "The Hosp'i tals Acts, 1936 to 1946." (b) A base hospital within the meaning of "7he Hospi taZ. Acts, 1936 to 1946." (c) A hospital· to which Part IV. of "7he Hospitals Acts, 1936 · to 1946," applies; (d) A private hospit,al for which a license is granted under "The Health Acts, 1937 to 1946," ' "Inspector" means an inspector of the Department of ~ealth and Home Aff&irs duly appointed under the provisions of "7heHealthActs, 19::s7 ttJ 1946."
Language:English
Score: 1141897.1 - https://daccess-ods.un.org/acc...et?open&DS=E/NL.1948/83&Lang=E
Data Source: ods