The potential for Artificial Intelligence (AI) in healthcare is still under-explored across the globe. From managing patient appointments, to triage, diagnosis, patient monitoring, and treatment administration, AI is proving to reshape the traditional clinical and surgical practice. Anecdotal reports indicate AI deployment in healthcare systems provides fast, more efficient, and cost-effective (in the long term) delivery of clinical and surgical services. Healthcare departments that currently report AI usage include;
Radiology – Through Machine Learning (ML) – deep learning (DL) & neural networks, AI is now being used to analyze radiological scans/frames and provide an accurate diagnosis of tumours and skin disorders – has shown to be as good as a doctor/radiologist or even better. This deployment has shown to also reduces medical errors associated with human flaws like competence level, practice experience, fatigue, or bias among others.
Precision Medicine – ML is being used to precisely/accurately predict or target the most effective treatment protocols. Moreover, some settings have deployed robotic surgeons that aid in prostate, neck, ophthalmic, and gynaecological surgical procedures.
Health Information Systems (HIS) – ML provides an opportunity for automation of how healthcare institutions interact with patients, collect, process, manage, or store data; collect revenues and process claims. Most importantly, Clinical Decision Support Systems utilise ML to generate clinical insights on the performance of the health facility, quality improvement(QI) measures as well as population health issues affecting the patients. All these aid in efficient hospital administration and overall healthcare management.
Patient Interaction and Health Messaging – as part of HIS, chatbots, for example, are now used to engage patients, and support telemedicine and health promotion messaging.
Drug Discovery and Development – Through simulation of the natural order of things and systems, AI provides an opportunity to significantly cut costs related to the time taken in the development and marketing of new drug assets.
Wearables – personal devices like smart watches for wellness and fitness tracking are increasingly penetrating the consumer market.
CONS
The downside of an AI system is how capital-intensive it can be to install, train, and fully optimize its capabilities. Other implications are around regulatory and ethical issues, patient safety, data privacy, and cybersecurity concerns that arise from the automation of healthcare management and service delivery. The risk of job losses (via workforce replacements) is somehow, still a debatable issue that is worth considering.
That said, AI is here to stay, however, there’s immense potential for lots of research and development to fully understand how AI can be fully utilized and optimized in healthcare delivery and consequently address global health challenges.
