Governance of automated image analysis and artificial intelligence analytics in healthcare

Abstract

The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-today clinical practice, but more AI applications are expected to be incorporated into the workflows in the foreseeable future. These applications could produce significant ethical and legal issues in healthcare if they cause abrupt disruptions to its contextual integrity and relational dynamics. Sustaining trust and trustworthiness is a key goal of governance, which is necessary to promote collaboration among all stakeholders and to ensure the responsible development and implementation of AI in radiology and other areas of clinical work. In this paper, the nature of AI governance in biomedicine is discussed along with its limitations. It is argued that radiologists must assume a more active role in propelling medicine into the digital age. In this respect, professional responsibilities include inquiring into the clinical and social value of AI, alleviating deficiencies in technical knowledge in order to facilitate ethical evaluation, supporting the recognition, and removal of biases, engaging the "black box" obstacle, and brokering a new social contract on informational use and security. In essence, a much closer integration of ethics, laws, and good practices is needed to ensure that AI governance achieves its normative goals.

Artificial intelligence: Who is responsible for the diagnosis?

“…In order to use artificial intelligence safely, as a support to the activity of the radiologist, it is necessary that it be trustworthy and validated in clinical practice [9].…”

Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice

“…Health care predictions can vary by race, genetics, and gender amongst other variations, and failure to take these into account might over or underestimate patient risk factors. As stated in the review by Ho et al concerning AI analytics in healthcare, it will become the responsibility of the clinician to ensure that AI algorithms are developed and applied appropriately [35]. It is imperative that healthcare continues to operate by ethically defined guidelines to sustain trustworthiness and that medicine continues to prioritize the good of the patient.…”

“…AI alone was shown to be superior to a single radiologist in detecting breast cancer. However, in practice, an individual radiologist reviews these images, and due to bias, might dismiss CAD suggestions [35]. Nonetheless, when two specialists review the image, it is most likely to undergo additional testing if the readers disagree with each other.…”

Application and Prospect of a Mobile Hospital in Disaster Response

“…37 Specific capabilities of MI include detection and classification of lesions, automated image segmentation, extraction of radiomic features and study triage, and image reconstruction. [38][39][40][41] The combination of a network and the medical field gave birth to telemedicine. The implementation of telemedicine can improve the uneven distribution of regional medical resources; reduce the working pressure of medical staff; shorten the distance between medical staff and patients and medical staff; and improve the timeliness of monitoring, diagnosis, and treatment.…”