Artificial intelligence in healthcare is an all-encompassing term employed to describe the use of machine-learning, deep-learning and related developments, or artificial intelligence (AI), to imitate human cognition in the analysis, presentation, comprehension of complex medical, and diagnosis and health care data. Specifically with AI-ability to automate many aspects of health-related activities.
AI provides prevention, diagnostic and prognostic. It makes patients’ life, doctors, and administers within the health system easier and cost-efficient. It also offers invaluable tools for related stakeholders such as the Pharmaceutical & Biotech Industry, health insurance and related health monitoring technologies.
AI application healthcare is relatively new; there are numerous unprecedented ethical issues related to its practice, such as privacy, automation of jobs, and representation biases.
It is one of the world’s highest-growth industries within the AI sector. AI is reinventing and reinvigorating 21st-century health care through a machine that enables predicting, comprehending, learning, and acting.
Papers’ theme are welcomed but are not limited to research in the following areas:
Artificial intelligence within Hospital System
- Diagnosis processes
- Treatment protocol development
- Support operational initiatives that increase cost saving
- Enhance patient satisfaction
- satisfy their staffing and workforce needs
- healthcare managers – improve business operations through increasing utilisation; decreasing patient boarding
- reducing length of stay and optimising staffing levels
Artificial intelligence within Pharmaceutical & Biotech Industry
- Data-Driven drug discovery
- Digital Drug Discovery and Development
- Small molecule drug design: molecules identification, drug re-purposing, clinical test and trials and precision medicine, target identification, modulation of protein-protein interaction and many other use cases.
- AI-powered collaborative platform
- AI-enabled drug discovery platform and the development of treatment for rare genetic diseases
- Map immune system with AI and single-cell analysis
Artificial intelligence impact on Health ethics
- Automated Decisions – Ethical and Legal Issues.
- Bias and inequality
- AI-enabled Health Insurance
Artificial intelligence within Medical Devices
- Analyse correlation between prevention or treatment techniques and patient outcomes
- AI in personalised medicine
- AI in patient monitoring and care
- Detecting retinopathy from Images of the eye fundus
- Counting and recognising certain cell types from Images of histological sections
- Diagnosis of heart infarctions, Alzheimer’s, cancer from Radiology images, e.g. CT, MRI
- Detecting depression from Speech, movement patterns
- Selection and dosage of medicines from Diagnoses, gene data
- Diagnosis of heart diseases, degenerative brain diseases from ECG or EEG signals
- Detecting epidemics from Internet searches
- Disease prognoses from Laboratory values, environmental factors
- Time-of-death prognosis for intensive care patients from Vital signs, laboratory values and other data in the patient’s records
- Detection, analysis and improvement of signals, e.g. weak and noisy signals
- Extraction of structured data from unstructured text
- Segmentation of tissues, e.g. for irradiation planning
Please check the submission procedures on the submission page.
General enquiries and submissions ro be addressed to the Conference Co-ordinator.
Symposium enquiries specific should be addressed to:
Professor of Machine Learning
Head of Artificial Intelligence and Digital Technology Research Cluster
wailok.woo (@) northumbria.ac.uk
LSBU, UK
banisse (@) lsbu.ac.uk