One of the most critical issues related to the social and economic impact of artificial intelligence is work automation – and hence the possible disappearance of human jobs. However, just as important is the interaction between human work and artificial agents, which was immediately found to be relevant in medical practice, as a result of the progress made by the application of machine learning algorithms for both diagnostic and prognostic purposes. The promise is usually that application of these technologies will be positive in terms of accuracy, speed, economy and quality of the decision-making process. On the other hand, initial results are controversial: while in some cases performance has improved, in others it has degraded.
Among the factors identified as negatively affecting AI-supported medical applications are the deskilling of medical personnel, the passive acceptance of computer decisions, security and privacy risks, inaccurate and distorted training data, and the inadequate treatment of uncertainty.
This Research Topic calls for contributions related to the design, testing, and evaluation of artificial intelligence systems aimed to support medical practice. Contributions should discuss the impact of the new methodologies, tools, models, interfaces, etc. on the conduct of medical work, and aim to improve the performance of teams made up of human workers and artificial agents.
Topics of interest include, but are not limited to:
• Methodologies for evaluating clinical performance of AI technology for medical diagnosis and prediction
• Performance Assessment Tools for AI-supported medical teams
• Models for machine learning in medicine
• Data selection criteria for machine learning in medicine
• Human Machine Interfaces for medical systems
• Socio-economic impacts of AI-supported medicine
• Ethical and legal issues of AI-supported medicine
One of the most critical issues related to the social and economic impact of artificial intelligence is work automation – and hence the possible disappearance of human jobs. However, just as important is the interaction between human work and artificial agents, which was immediately found to be relevant in medical practice, as a result of the progress made by the application of machine learning algorithms for both diagnostic and prognostic purposes. The promise is usually that application of these technologies will be positive in terms of accuracy, speed, economy and quality of the decision-making process. On the other hand, initial results are controversial: while in some cases performance has improved, in others it has degraded.
Among the factors identified as negatively affecting AI-supported medical applications are the deskilling of medical personnel, the passive acceptance of computer decisions, security and privacy risks, inaccurate and distorted training data, and the inadequate treatment of uncertainty.
This Research Topic calls for contributions related to the design, testing, and evaluation of artificial intelligence systems aimed to support medical practice. Contributions should discuss the impact of the new methodologies, tools, models, interfaces, etc. on the conduct of medical work, and aim to improve the performance of teams made up of human workers and artificial agents.
Topics of interest include, but are not limited to:
• Methodologies for evaluating clinical performance of AI technology for medical diagnosis and prediction
• Performance Assessment Tools for AI-supported medical teams
• Models for machine learning in medicine
• Data selection criteria for machine learning in medicine
• Human Machine Interfaces for medical systems
• Socio-economic impacts of AI-supported medicine
• Ethical and legal issues of AI-supported medicine