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Biomedical Research Ethics, Integrity and Responsible AI
Published 10/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 2h 3m | Size: 471.69 MB
Consent, data, transparency and responsible AI through research cases and a semester ethics dossier
What you'll learn
Evaluate ethics problems by distinguishing principles, applicable rules and missing facts.
Critique consent, recruitment and inclusion without treating a signature as sufficient.
Propose accountable governance for data, samples, access, sharing and secondary use.
Design responsible AI workflows and defend a research-ethics dossier using evidence and an accountable decision trail.
Requirements
Basic research literacy and ability to read English research sources. Bilingual terminology and self-assessment tools are provided.
Description
This course contains the use of artificial intelligence.
How do you make a research decision that another person can inspect, challenge and understand? Join an intelligent male host and Shu Chen Hou PhD in original conversations about biomedical research ethics, academic integrity and responsible AI.
Across a sixteen-week pathway, examine scientific value, risk and benefit, consent, fair inclusion, data and sample governance, animal research, preregistration, outcome reporting, authorship, peer review and scholarly AI use. Fictional cases invite you to decide before the model reasoning is revealed. Genuine guidance and research are identified separately, including a guided journal-club appraisal.
Build a decision record, risk register, consent communication plan, data-access map, analysis log, contribution discussion, claim-to-source ledger and an integrated ethics dossier. Download worksheets, model reasoning and assessment rubrics.
The semester plan includes 32 structured-learning hours across video study, original-source reading, case workshops and revision. Recorded video totals approximately 2 hours 4 minutes. Self-paced learners use self-assessment and a recorded defence alternative; individual grading and university credit are not included.
The course uses an authorised AI-synthesized instructor voice and a male synthetic host. It is not a recording of a real interview. It is educational, independently developed, and not an ethics approval, professional registration, clinical qualification or legal determination. Original diagrams explain the reasoning; no confidential participant records or journal figures are redistributed.
Shu Chen Hou PhD earned a PhD in Medicine at the University of Bristol, within the Faculty of Medicine & Dentistry, School of Clinical Sciences.
侯淑真博士於英國布里斯托大學（University of Bristol）醫學與牙醫學院（Faculty of Medicine & Dentistry）臨床科學學系（School of Clinical Sciences）取得醫學領域博士學位。
Who this course is for
Upper-undergraduate and postgraduate biomedical and public-health students; early-career researchers seeking case appraisal and proposal planning.
Homepage
Code:
https://www.udemy.com/course/biomedical-research-ethics-integrity-and-responsible-ai/Code:
Biomedical Research Ethics, Integrity and Responsible AI
Published 10/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 2h 3m | Size: 471.69 MB
Consent, data, transparency and responsible AI through research cases and a semester ethics dossier
What you'll learn
Evaluate ethics problems by distinguishing principles, applicable rules and missing facts.
Critique consent, recruitment and inclusion without treating a signature as sufficient.
Propose accountable governance for data, samples, access, sharing and secondary use.
Design responsible AI workflows and defend a research-ethics dossier using evidence and an accountable decision trail.
Requirements
Basic research literacy and ability to read English research sources. Bilingual terminology and self-assessment tools are provided.
Description
This course contains the use of artificial intelligence.
How do you make a research decision that another person can inspect, challenge and understand? Join an intelligent male host and Shu Chen Hou PhD in original conversations about biomedical research ethics, academic integrity and responsible AI.
Across a sixteen-week pathway, examine scientific value, risk and benefit, consent, fair inclusion, data and sample governance, animal research, preregistration, outcome reporting, authorship, peer review and scholarly AI use. Fictional cases invite you to decide before the model reasoning is revealed. Genuine guidance and research are identified separately, including a guided journal-club appraisal.
Build a decision record, risk register, consent communication plan, data-access map, analysis log, contribution discussion, claim-to-source ledger and an integrated ethics dossier. Download worksheets, model reasoning and assessment rubrics.
The semester plan includes 32 structured-learning hours across video study, original-source reading, case workshops and revision. Recorded video totals approximately 2 hours 4 minutes. Self-paced learners use self-assessment and a recorded defence alternative; individual grading and university credit are not included.
The course uses an authorised AI-synthesized instructor voice and a male synthetic host. It is not a recording of a real interview. It is educational, independently developed, and not an ethics approval, professional registration, clinical qualification or legal determination. Original diagrams explain the reasoning; no confidential participant records or journal figures are redistributed.
Shu Chen Hou PhD earned a PhD in Medicine at the University of Bristol, within the Faculty of Medicine & Dentistry, School of Clinical Sciences.
侯淑真博士於英國布里斯托大學（University of Bristol）醫學與牙醫學院（Faculty of Medicine & Dentistry）臨床科學學系（School of Clinical Sciences）取得醫學領域博士學位。
Who this course is for
Upper-undergraduate and postgraduate biomedical and public-health students; early-career researchers seeking case appraisal and proposal planning.
Homepage[code]
https://www.udemy.com/course/biomedical-research-ethics-integrity-and-responsible-ai/[quote]Biomedical Research Ethics, Integrity and Responsible AI
Published 10/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English + subtitle | Duration: 2h 3m | Size: 471.69 MB
Consent, data, transparency and responsible AI through research cases and a semester ethics dossier
What you'll learn
Evaluate ethics problems by distinguishing principles, applicable rules and missing facts.
Critique consent, recruitment and inclusion without treating a signature as sufficient.
Propose accountable governance for data, samples, access, sharing and secondary use.
Design responsible AI workflows and defend a research-ethics dossier using evidence and an accountable decision trail.
Requirements
Basic research literacy and ability to read English research sources. Bilingual terminology and self-assessment tools are provided.
Description
This course contains the use of artificial intelligence.
How do you make a research decision that another person can inspect, challenge and understand? Join an intelligent male host and Shu Chen Hou PhD in original conversations about biomedical research ethics, academic integrity and responsible AI.
Across a sixteen-week pathway, examine scientific value, risk and benefit, consent, fair inclusion, data and sample governance, animal research, preregistration, outcome reporting, authorship, peer review and scholarly AI use. Fictional cases invite you to decide before the model reasoning is revealed. Genuine guidance and research are identified separately, including a guided journal-club appraisal.
Build a decision record, risk register, consent communication plan, data-access map, analysis log, contribution discussion, claim-to-source ledger and an integrated ethics dossier. Download worksheets, model reasoning and assessment rubrics.
The semester plan includes 32 structured-learning hours across video study, original-source reading, case workshops and revision. Recorded video totals approximately 2 hours 4 minutes. Self-paced learners use self-assessment and a recorded defence alternative; individual grading and university credit are not included.
The course uses an authorised AI-synthesized instructor voice and a male synthetic host. It is not a recording of a real interview. It is educational, independently developed, and not an ethics approval, professional registration, clinical qualification or legal determination. Original diagrams explain the reasoning; no confidential participant records or journal figures are redistributed.
Shu Chen Hou PhD earned a PhD in Medicine at the University of Bristol, within the Faculty of Medicine & Dentistry, School of Clinical Sciences.
侯淑真博士於英國布里斯托大學（University of Bristol）醫學與牙醫學院（Faculty of Medicine & Dentistry）臨床科學學系（School of Clinical Sciences）取得醫學領域博士學位。
Who this course is for
Upper-undergraduate and postgraduate biomedical and public-health students; early-career researchers seeking case appraisal and proposal planning.
Code:
https://rapidgator.net/file/d98d8fad3a41d145dd287bfa390be5b9/Biomedical_Research_Ethics,_Integrity_and_Responsible_AI.rar.html
https://nitroflare.com/view/4C77F95F9C9D039/Biomedical_Research_Ethics%2C_Integrity_and_Responsible_AI.rar