++ All booklets now selling at 70% discount and fully available for free through the KDP unlimited circuit. Get them while it lasts!
Tailored content can be provided upon request. Submit your technical specification through the contact form in the About page at https://www.tenproblems.com/about/ and get your quote.
Literature Review: Artificial Intelligence problem for Healthcare
This “Ten Problems for Healthcare in the 2020s” booklet identifies ten relevant areas from very recent contributions put forward at academic level in the form journal articles, conference proceedings and students’ theses. Ten freely accessible internet references have been selected for each area and direct links are provided at the end of each chapter for own consultation. Our selected references do not intend to mirror ranking indexes nor establish novel classifications. On the contrary, they are meant to represent peer-reviewed, diverse and scientifically-sound case studies for vertical dissemination aimed at non-specialist readers. They will also be able to scoop even more references through the bibliography that is reported at the end of each selected reference.
Without further ado, these are the ten problems that we are going to introduce in this booklet:
- artificial intelligence,
- special needs,
Each problem has its own dedicated chapter made of an introductory section, a short presentation of the ten selected references and a conclusions section.
The final chapter of this booklet will report the conclusions from each chapter again in order to provide a complete executive summary.
5 Artificial Intelligence
THE PROBLEM — Artificial Intelligence is rapidly becoming an important approach across biomedical discovery, clinical research, medical diagnostics / devices, and precision medicine. A system that is built on public trust is needed to achieve a desirable societal goal that artificial intelligence benefits everyone. That said, artificial intelligence, robotics and biosensors are already revolutionizing the methods and capabilities of healthcare research and practice.
CASE STUDIES — … buy this booklet from Amazon …
CONCLUSIONS — Machine intelligence approaches have the potential to improve health through the facilitation of more efficient and effective provision of care. The ethical and legal debate of artificial intelligence- driven health care revolves around four primary challenges, namely: informed consent to use, safety and transparency, algorithmic fairness and biases, and data privacy. However, implementation factors will prevent large-scale automation of healthcare professional jobs for a considerable period. Largely, evaluation studies to date have considered performance of artificial intelligence on specific tasks, but have neglected the wider impact on clinical systems. To unlock the potential of predictive analytics, while securing patient safety, regulatory authorities should ensure that proposed algorithms meet accepted standards of clinical benefit. Clinicians with data and computer scientists with analytics must assure a data-to-information continuum and a knowledge-to-intelligence transfer. Evaluation is a major challenge in clinical settings where it could be unsafe to follow reinforcement learning policies in practice. While specific technologies can be expected to advance the field of rehabilitation psychology, it is likely that the integration of these technologies will provide the most significant opportunities in the years ahead. Artificial intelligence is emerging as a promising technique for differential diagnosis, automatic lesion detection, and the generation of preliminary reports. There are still several technological and societal challenges to be addressed before smart biosensor systems are widely adopted.
TEN FREE REFERENCES FROM THE INTERNET — … buy this booklet from Amazon …