Few fields are feeling the impact of artificial intelligence (AI) as much as health care—and in so many positive ways:
- Decision-making is getting faster and easier. In some studies, algorithms have actually outperformed radiologists at interpreting medical images. Algorithms can also flag areas of concern, focusing and accelerating the work of medical experts.
- AI’s advanced pattern recognition capabilities make it an ideal tool for automating high-volume, repetitive tasks. This has led to strides in precision diagnostics, such as in diabetic retinopathy.
- AI is pointing the way to improved patient self-medication, for instance by monitoring how patients use insulin pens or inhalers.
- AI and automation are being combined to detect, report, and prevent adverse drug effects faster and more accurately, a real productivity boon in the costly pharmacovigilance process.
- For end-of-life care, AI can be used to predict sooner when palliative care is advisable, then aid in prognosis and resource allocation.
- AI augments and amplifies human expertise in multiple ways, including workflow in healthcare settings, record keeping, information sharing, and actual healthcare delivery.
We are only now getting a sense of all the ways AI can make us more productive and effective.
Machine learning
Machine learning—a subset of AI—is a big part of how AI is driving healthcare productivity. It refers to computers learning on their own by identifying patterns in data. The key is to provide the RIGHT data. For instance, if you want the machine to learn how to analyze medical images, you provide it with thousands—or hundreds of thousands—of such images.
Today, machine learning is pervasive. There are many helpful applications in health care, including recommender systems that, like YouTube or Amazon but for much more serious purposes, provide individual users with personalized medical information based on their health profiles.
Natural language processing
Another area where machine learning is playing a big role is documentation. Pharmaceutical firms and healthcare organizations generate vast quantities of printed materials, including medical records, product literature, and more. Natural language processing systems developed through machine learning can transcribe patient interactions, analyze clinical notes, draw up reports, and provide conversational support through chatboxes.
Health care without borders
And of course, we must never forget that health care is a global concern with global consequences. That means healthcare stakeholders have frequent call for translation services. To derive the full benefit of AI in terms of speed, accuracy, and cost efficiency, you need a translation partner like TRSB that combines the power of large language models with the subject matter expertise of translators specialized in medicine and pharmaceuticals.
