AI in Healthcare: Ethical and Privacy Challenges

  title={AI in Healthcare: Ethical and Privacy Challenges},
  author={Ivana Bartoletti},
  • I. Bartoletti
  • Published in AIME 26 June 2019
  • Medicine, Political Science, Computer Science
The deployment of Artificial Intelligence in healthcare is extremely promising and although AI is no panacea, harnessing patient data will lead to precision medicine, help detect disease before they manifest and support independent living for the elderly, amongst many other things. However, this progress will not be without challenges from both an ethical and privacy standpoint. These issues need understanding from policy makers and developers alike for AI to be embraced responsibly. 
The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review
It is stressed how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.
NHS AI Lab: Why We Need to Be Ethically Mindful About AI for Healthcare
The NHS AI Lab should create an Ethics Advisory Board and monitor, analyse, and address the normative and overarching ethical issues that arise at the individual, interpersonal, group, institutional and societal levels in AI for healthcare.
The ethics of AI in health care: A mapping review.
What Makes Artificial Intelligence Exceptional in Health Technology Assessment?
A systematic review of the literature on the challenges posed by AIHTs in HTA and health regulation finds thatAIHTs are perceived as exceptional because of their technological characteristics and potential impacts on society at large.
Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding
AI-driven technology research and development for health care outpaces the creation of supporting AI governance globally, and policy recommendations were developed to support GDHP member countries in overcoming core AI governance barriers and establishing common ground for international collaboration.
Issues in Information Systems
This paper aims to present the digital healthcare and privacy that applies to the healthcare industry and also to discuss the advancements that are being implemented in digital health, the challenges they face with the data while shifting it, and moreover concerned with data breaches and privacy.
A Comprehensive Survey on Data Utility and Privacy: Taking Indian Healthcare System as a Potential Case Study
The utility and privacy factors of the Indian healthcare data are presented and the utility aspect and privacy problems concerning Indian healthcare systems are discussed and the case study of the NITI Aayog report is presented to explain how reformation occurs inIndian healthcare systems.
Lightweight Mutual Authentication and Privacy-Preservation Scheme for Intelligent Wearable Devices in Industrial-CPS
A lightweight mutual authentication scheme based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities and the privacy risk associated with a patient data is predicted using an AI-enabled hidden Markov model is proposed.
Principle-based recommendations for big data and machine learning in food safety: the P-SAFETY model
A set of principle-based recommendations is proposed by adapting high-level principles enshrined in institutional documents about Artificial Intelligence to the realm of food safety risk assessment by adopting Safety, Accountability, Fairness, Explainability, Transparency and Transparency as core principles (SAFETY), whereas Privacy and data protection are used as a meta-principle.


Of Regulating Healthcare AI and Robots
  • N. Terry
  • Medicine, Political Science
    SSRN Electronic Journal
  • 2019
The article suggests the imperatives for a new regulatory structure that relies less on the senses that the authors know the “practice of medicine” or “device” when they see it, and more on generally accepted normative principles, which include quality, safety, cost-effectiveness, improved data protection, protections against discrimination and in support of health equity.
Robots and Privacy
This chapter breaks the effects of robots on privacy into three categories — direct surveillance, increased access, and social meaning — with the goal of introducing the reader to a wide variety of issues.