AI Risks in Health Apps and Wearables: What You Need to Know
What risks are posed by health apps and wearables when incorporating AI, machine learning, and deep learning into patient care systems?
There are several risks posed by health apps and wearables that incorporate AI, machine learning, and deep learning:
- Data privacy and security
- Accurate diagnosis and treatment
- Unintended consequences
- Inappropriate use
- Privacy implications
How can the risks associated with health apps and wearables be managed?
The main risks of introducing AI, machine learning, and deep learning into health apps and wearables include potential exposure to cybercrime, infringement of privacy, and misuse of data.
As medtech and healthtech incorporate artificial intelligence, machine learning, and deep learning into patient care systems, various risks associated with health apps and wearables must be considered and managed. The primary risks involve the exposure of individuals to cybercrime and cyberwarfare. These threats have been underscored by several industry leaders in a Pew Research Center survey. Risks also encompass infringement on individual privacy and misuse of massive amounts of data for profit or other unscrupulous aims.
Another consideration is the potential for diminishing the technical, cognitive, and social skills that humans require to survive. Furthermore, intelligent machines that do not align with human values and safety could emerge, reflecting a mismatch between our technological abilities and ethical considerations.
Practical aspects of these risks can be managed by implementing established codes of ethics used by organizations, promoting legal transparency for artificial intelligence, and ensuring corporate responsibility towards potential dangers. Lastly, the usage and development of AI should be guided by the principles of being safe, sustainable, and responsible, as suggested by Bell in her TED Salon Talk.