A5072686619- Artificial Intelligence in Healthcare and Education
- Ethics and Social Impacts of AI
- Ethics in Clinical Research
- Healthcare Quality and Management
- Innovations in Medical Education
- Healthcare Policy and Management
- Biomedical Text Mining and Ontologies
- Global Healthcare and Medical Tourism
- Autoimmune and Inflammatory Disorders
- Noise Effects and Management
- Adversarial Robustness in Machine Learning
- Medical Research and Practices
- Transport and Logistics Innovations
- Explainable Artificial Intelligence (XAI)
- Diversity and Career in Medicine
- linguistics and terminology studies
- Geriatric Care and Nursing Homes
- Pharmacovigilance and Adverse Drug Reactions
- Healthcare Technology and Patient Monitoring
- Sarcoidosis and Beryllium Toxicity Research
- Healthcare Systems and Challenges
- Biomedical Ethics and Regulation
- Pharmaceutical industry and healthcare
- Digital Imaging in Medicine
- Clinical practice guidelines implementation
Morgan State University
2023-2025
Acoustics (Norway)
2023
Artificial intelligence (AI) and machine learning (ML) technology design development continues to be rapid, despite major limitations in its current form as a practice discipline address all sociohumanitarian issues complexities. From these emerges an imperative strengthen AI ML literacy underserved communities build more diverse workforce engaged health research. has the potential account for assess variety of factors that contribute disease improve prevention, diagnosis, therapy. Here, we...
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Abstract Research in ethical AI has made strides quantitative expression of values such as fairness, transparency, and privacy. Here we contribute to this effort by proposing a new family metrics called “decisional value scores” (DVS). DVSs are scores assigned system based on whether the decisions it makes meet or fail particular standard (either individually, total, ratio average over made). Advantages DVS include greater discrimination capacity between types ethically relevant facilitation...
<sec> <title>BACKGROUND</title> Artificial intelligence (AI) and machine learning (ML) technology design development continues to be rapid, despite major limitations in its current form as a practice discipline address all sociohumanitarian issues complexities. From these emerges an imperative strengthen AI ML literacy underserved communities build more diverse workforce engaged health research. </sec> <title>OBJECTIVE</title> has the potential account for assess variety of factors that...