This week, Connected Health (CH) journal continues to share several latest articles related to artificial intelligence in healthcare.

1. Title: Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review

Authors: Ania Syrowatka, Wenyu Song, Mary G Amato, Dinah Foer, Heba Edrees, Zoe Co, Masha Kuznetsova, Sevan Dulgarian, Diane L Seger, Aurélien Simona, Paul A Bain, Gretchen Purcell Jackson, Kyu Rhee, David W Bates

Type: Review

Abstract:
Adverse drug events (ADEs) represent one of the most prevalent types of health-care-related harm, and there is substantial room for improvement in the way that they are currently predicted and detected. We conducted a scoping review to identify key use cases in which artificial intelligence (AI) could be leveraged to reduce the frequency of ADEs. We focused on modern machine learning techniques and natural language processing. 78 articles were included in the scoping review. Studies were heterogeneous and applied various AI techniques covering a wide range of medications and ADEs. We identified several key use cases in which AI could contribute to reducing the frequency and consequences of ADEs, through prediction to prevent ADEs and early detection to mitigate the effects. Most studies (73 [94%] of 78) assessed technical algorithm performance, and few studies evaluated the use of AI in clinical settings. Most articles (58 [74%] of 78) were published within the past 5 years, highlighting an emerging area of study. Availability of new types of data, such as genetic information, and access to unstructured clinical notes might further advance the field.

Access to this article:
https://www.sciencedirect.com/science/article/pii/S2589750021002296

2. Title: Explainability and artificial intelligence in medicine

Authors: Sandeep Reddy

Type: Comment

Summary:
Uptake of artificial intelligence (AI) is limited among healthcare professionals due to the scarce transparency associated with specific AI algorithms, especially black-box algorithms. Indeed, clinical medicine relies on transparency in decision making. If there is no medically explainable AI and the physician cannot reasonably explain the decision-making process, the patient's trust in them will erode. To address the transparency issue with certain AI models, explainable AI has emerged, as discussed in this article.

Access to this article:
https://www.thelancet.com/journals/landig/article/PIIS2589-7500(22)00029-2/fulltext

3. Title: Empowering Local Communities using Artificial Intelligence

Authors: Yen-ChiaHsu, Ting-Hao ‘Kenneth’ Huang, Himanshu Verma, Andrea Mauri, Illah Nourbakhsh, Alessandro Bozzon

Type: Perspective

Highlights:

• Co-creating AI systems can empower local communities to address regional concerns
• Designing AI for social impact is the key to linking AI research closer to local needs
• Curating data with local people can yield agency to them and facilitate AI research
• Explaining data patterns using AI can reveal local issues for public scrutiny

4. Title: The Critical Factors Affecting the Deployment and Scaling of Healthcare AI: Viewpoint from an Experienced Medical Center

Authors: Chung-Feng Liu, Chien-Cheng Huang, Jhi-Joung Wang, Kuang-Ming Kuo and Chia-Jung Chen

Type: Perspective

Abstract:
Healthcare Artificial Intelligence (AI) has the greatest opportunity for development. Since healthcare and technology are two of Taiwan's most competitive industries, the development of healthcare AI is an excellent chance for Taiwan to improve its health-related services. From the perspective of economic development, promoting healthcare AI must be a top priority. However, despite having many breakthroughs in research and pilot projects, healthcare AI is still considered rare and is broadly used in the healthcare setting. Based on a medical center in Taiwan that has introduced a variety of healthcare AI into practice, this study discussed and analyzed the issues and concerns in the development and scaling of medical AIs from the perspective of various stakeholders in the healthcare setting, including the government, healthcare institutions, users (healthcare workers), and AI providers. The present study also identified critical influential factors for the deployment and scaling of healthcare AI. It is hoped that this paper can serve as an important reference for the advancement of healthcare AI not only in Taiwan but also in other countries.

Access to this article:
https://www.mdpi.com/2227-9032/9/6/685

We hope our sharing will inspire you. Looking forward to meeting you next time in CH Bulletin.