Artificial Intelligence and Healthcare: Will AI replace healthcare workers?
Artificial intelligence (AI) has been a hot topic in healthcare recently, sparking debates over AI taking up the roles of healthcare workers in the future. With the rapid growth of AI in various industries, including healthcare, it is understandable that healthcare professionals, particularly physicians, are worried about their job security.
AI refers to systems or machines that can perform tasks by mimicking human intelligence and learning from the data they acquire. Machine learning (ML) is a subset of AI that focuses on building systems that learn from data to solve problems. In the healthcare sector, data is collected and pre-processed before being passed onto machine learning models or algorithms that acquire information from this data to diagnose diseases and develop treatment plans.
Healthcare workers are licensed individuals who work to maintain or restore the physical, mental, and emotional well-being of patients. These professionals include medical students, frontline healthcare workers, and care providers who are not in direct patient contact.
AI applications in healthcare can be divided into three categories: patient-oriented, clinician-oriented, and administrative and operational-oriented. Examples of these categories include symptom matching, patient diagnosis and prognosis, medication discovery, and bot assistants that can translate languages, transcribe notes, and arrange images and files. The complexity of healthcare services and the potential for errors that could result in loss of lives means that the entire process from data collection to model deployment needs to be validated by a human.
Despite the many advantages of AI, there are still drawbacks and difficulties, particularly in developing or underdeveloped countries like Nigeria, where there is a paucity of data. Data is a crucial component for AI, and millions of different types of data are required to design and use ML models to address the problems they were built to solve. Bias also becomes a greater issue in industrialized countries where data availability is not as impaired as in developing countries.
The outcome of an ML model depends on the type of data used to train it. If a model is trained using data from a specific demographic, it will generate biased findings when applied to a different demographic. To prevent bias, data used to train models must be generalized in terms of age, gender, race, religion, etc. Data sharing is also a significant challenge because patient information must be kept confidential. Despite these drawbacks, AI has made significant advancements in the medical field, including the development of classification models for skin cancer, the early detection of diabetic retinopathy, and the discovery of new drugs for neurological conditions like Parkinson’s, Alzheimer’s, and ALS. AI also supports medical care, enhances patient experiences, and improves clinical workflows through the use of bot assistants.
So, will AI replace healthcare workers? At least for the time being, it seems unlikely. While some professions may become obsolete, healthcare professionals can grow with AI as the roles inevitably evolve. This may even lead to the creation of new job possibilities as AI models require data to be trained on a regular basis. Clinical imaging data, for example, will require labeling by experts to feed these models.
The key to successful AI implementation in healthcare is collaboration between data scientists and healthcare professionals. In addition to technical skills, domain expertise in all aspects of healthcare services is crucial to fine-tuning AI models to their optimal level. Healthcare professionals will also benefit from AI advancements since they are humans and will eventually require these services, and who doesn’t want the best? Collaboration between data scientists and healthcare professionals will advance healthcare services to a level that was once unimaginable.
Reference: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7640807/
