Often thought of as one overarching technology, artificial intelligence (AI) is the name given to a collection of several technologies. From machine learning to natural language processing, most of these technologies have immediate relevance to the healthcare field, but the specific processes and tasks they support can vary greatly.
As a result, the potential role of intelligent machines within the healthcare system is uncertain as it varies between technologies. However, what is certain is the great potential AI technologies provide for the future of healthcare, transforming many aspects of patient care, administrative processes, care models, and payer and pharmaceutical organizations. And it's already happening.
The AI market in healthcare is expected to grow to $6.6 billion. We spoke to CSG’s Precision Medicine expert, Vicky Kerrigan, who explained, “AI development is coming across the board. Especially when looking at how data is read - AI processes read the data so much faster than the human eye. As a result, the role of the clinician is already starting to change with a greater focus on data analytics.”
“Just as machines made human muscles a thousand times stronger, machines will make the human brain a thousand times more powerful.”
Sebastian Thrum, computer scientist, the New Yorker.
AI Technologies Within Healthcare:
Machine Learning:
Machine learning is arguably one of the most prominent technologies that can be utilized within the health system. Using narrow AI to complete specific administrative tasks such as completing smart records or crunching large volumes of data to help healthcare professionals generate precise medicine solutions.
Fatima Paruk, CMO of Chicago-based Allscripts Analytics, reported: “AI will affect physicians and hospitals, as it will play a key role in clinical decision support, enabling earlier identification of disease and tailored treatment plans to ensure optimal outcomes.”
CSG’s Precision Medicine expert, Vicky Kerrigan, revealed, “A big thing at the minute is the Galleria test - A blood test that can screen for up to 50 types of cancer and inform the patient if they’re likely to develop cancer within the coming years. Backed by Melinda and Bill gates, these genetic biomarkers can be picked up and allow treatment before symptoms even develop!”
It's no surprise that many big names have recognised the potential machine learning provides, allowing us to problem solve and detect diseases quicker:
CSG’s Life Sciences expert, Lexie Stratford explained, “precision medicine is the ‘new hot thing’ used largely during the race to find a covid vaccine. All disease areas have now seen a huge increase in terms of production and development - there is lots more funding available now.”
Similarly, drug discovery and drug development are also large areas that pharmaceutical companies are focusing on the use of machine learning. Machine learning enables clinicians to accurately predict the way patients will respond to various drugs and identify which patients stand the greatest chance of benefiting from the drug. Certainly, AI, specifically machine learning, has huge potential to disrupt the entire care model, paving the way for a new, machine-led future of healthcare.
Natural Language Processing:
Making sense of human language has been a goal of AI researchers since the 1950s.
Natural language processing (NLP) covers a wide range of applications such as speech recognition, text analysis, translation and other goals related to language.
Although its intended goals may be simple, virtual assistants like Siri, Cortana, Google Assistant, and Alexa, VA’s are already connecting to medical information online. This allows patients to have a better understanding of medical terms and clinical language.
NLP has the power to help patients understand their specific anatomy, be more engaged with the care process, and adhere to medication. NLP also improves and streamlines the workload of health care practitioners. Through AI, NLP’s analyse unstructured clinical notes on patients and can prepare reports, transcribe patient interactions, and conduct conversational AI. In the UK, for example, this would save a tremendous amount of time for a stretched national health service and allow more patients to be seen by the clinician.
Physical Robots:
Surgical robots may seem to be the stuff of science fiction. However, surgeons have been aided by machine assistants for years, providing greater power and precision during operations. Approved by the USA in 2000, they improve surgeon visibility, create precise and minimally invasive incisions, stitch wounds and much more.
Of course, the decision-making process still requires human input, AI only completes a specific task within the process. Common surgical procedures using robotic surgery include gynaecologic surgery, prostate surgery and head and neck surgery.
But Is AI Reliable?
Without a doubt, AI is proving to be as dependable as physicians in diagnosing medical conditions.
Researchers from Oxford, UK, sought to test its accuracy and developed AI for predicting heart disease. According to their study, in 80% of the cases, the technology performed better than doctors in predicting cardiovascular diseases.
Similarly, researchers from Harvard Medical School, US, have developed a machine-learning-based microscope that can detect deadly blood infections with an astonishing 95% accuracy.
As noted in a recent journal by the royal healthcare of physicians, “today’s algorithms are already outperforming radiologists at spotting malignant tumors, and guiding researchers in how to construct cohorts for costly clinical trials”.
Our Outlook:
Without a doubt, the potential role artificial intelligence could play in the future of medicine is unlike anything we’ve ever seen before. From aiding surgeons during operations to processing genome sequencing at a rapid rate, AI and computer science can transform health care models as we know them.
However, the greatest challenge to AI in these healthcare domains is not whether these technologies are capable or accurate but rather the likelihood of their adoption in daily clinical practice.
Many healthcare establishments, such as hospitals and GP practices, are outdated. They simply do not have the systems in place to facilitate such a rapid deployment and integration of technology.
Furthermore, for widespread adoption to take place, AI systems must be approved by regulators, standardized to a sufficient degree, taught to clinicians and most importantly, funded by public or private payer organizations.
We must then turn to talent to develop the healthcare of the future. At CSG, our experts are already placing candidates into roles within precision medicine and medical devices, where they are having a direct impact on research and helping facilitate deployment of these technologies into healthcare practices.
If you want to find out more about our recruitment process or opportunities available within the healthcare space, please get in touch.
