The healthcare system has faced unprecedented challenges over the past two years. From longer patient wait times and staff shortages to misinterpretation of symptoms and incorrect diagnoses, the recent healthcare crisis has highlighted several weaknesses in how the system works.
It is clear that the future is AI-powered, and the future of healthcare is no exception—the use of advanced technologies to solve a wide range of problems has become evident in many medical fields and specialties. Whether assisting in drug discovery and development, improving the patient experience, delivering personalized solutions, or supporting healthcare professionals, AI is transforming healthcare through solutions that can predict, understand, learn, and act.
Enhancing Patient and Healthcare Professionals’ Experience
Healthcare workers often struggle to keep up with the latest medical advances while providing high-quality, patient-centered care. One of the main reasons for this issue is the tremendous amount of medical records frequently stored as complex, unstructured data, making them difficult to access and interpret. This is where AI comes into play.
Tailored Solutions for Patients
Understanding language has been a goal of AI researchers since the 1950s. The field of natural language processing (NLP) includes functions such as speech recognition, text analysis, and translation. In healthcare, the dominant applications of NLP are the creation, understanding, and classification of clinical documentation and published research. NLP systems can analyze unstructured clinical notes about patients, generate reports, transcribe patient interactions, and conduct AI conversations.
When behavioral data is collected across multiple communication channels, it provides insights that expert systems can use. Through these insights, healthcare providers can learn about their patients, anticipate their needs, and proactively provide the right service or healthcare solution—leading to an overall improvement in the patients’ experience.
While many healthcare organizations already have an abundance of data to help them analyze the interactions of each patient, AI technology is instrumental in creating a seamless and tailored healthcare experience for patients.
Providing Support to Healthcare Professionals
With the healthcare system under more pressure than ever, clinicians need technology to help them manage increasing demands and provide appropriate solutions to patients. At the same time, it’s important to ensure that healthcare professionals have the tools to manage their workloads better, as this is crucial in reducing their own stress and avoiding burnout.
By analyzing patient progression, identifying trends, and providing improved insights, healthcare providers can do their jobs better and provide patients with the support they need. Given the time-efficient way in which AI can analyze large amounts of data, it can revolutionize the way support is delivered, improving the experience of both patients and healthcare workers.
Assisting in Drug Discovery and Development
Drug discovery and development is a lengthy, expensive, and complex process dependent on various factors. A report by CBInsights indicates that it costs an average of $1.3 billion to bring a drug through clinical trials, and only 10% of these drugs are successfully brought to market.
Thanks to significant technological advancements, biopharmaceutical companies are quickly taking notice of the efficiency, accuracy, and insights that AI can provide. Since most drug discovery and development processes heavily rely on historical data and statistics, the future of this field undoubtedly lies in the accurate prediction provided by machine learning (ML). In fact, a study published earlier this year by BCG showed that biotech companies using an AI-first approach have more than 150 small-molecule drugs in discovery and more than 15 already in clinical trials.
ML approaches offer a range of tools to improve discovery and decision-making for well-defined questions with a wealth of high-quality data. Opportunities to use ML arise at all stages of drug discovery and development. Target validation, lead optimization, prognostic biomarkers identification, and digital pathology data analysis in clinical trials are a few examples.
The context and methodology of AI and ML applications in drug discovery and development vary, with some approaches leading to more accurate predictions and lower costs. For example, Deep Genomics’ AI platform helps researchers find candidates for developmental drugs for neuromuscular and neurodegenerative disorders. Finding suitable candidates during the drug development process significantly increases the chances of successfully passing clinical trials while also reducing time and costs.
Another example is the biotechnology company Atomwise, which uses AI to predict bioactivity and identify patient characteristics for clinical trials. The company claims that its AI technology screens between 10 and 20 million genetic compounds daily and delivers results 100 times faster than traditional pharmaceutical companies.
However, there are still many challenges to applying ML technology to clinical research. These lie primarily in the lack of interpretability and repeatability of ML–generated results, which may limit their application. Systematic and comprehensive high-dimensional data has yet to be developed in all areas of clinical research. With ongoing efforts to address these challenges and raise awareness of the factors required to validate ML approaches, this emerging technology can promote data-driven decision-making and has the potential to accelerate drug discovery and development rates.
Improving Diagnosis and Treatment
Diagnosis and treatment of diseases are one of the main focuses of AI. ML and deep learning (DL) are already taking both processes to the next level.
The promise of improving the diagnostic process is one of the most exciting applications of AI in healthcare. Deep learning is increasingly being applied to radiomics, or the detection of clinically relevant features in imaging data beyond what the human eye can perceive. Both radiomics and DL are most commonly used in oncology-oriented image analysis. Their combination promises greater diagnostic accuracy than the previous generation of automated image analysis tools, known as computer-aided detection (CAD).
When patients are diagnosed, being able to predict which treatment procedures are likely to be successful is a major advance for many healthcare providers. Machine learning is an important aid in this regard. In healthcare, the most common application of ML is precision medicine—predicting which treatment protocols are likely to succeed on a patient based on individual variability in genes, environment, and lifestyle factors, as well as treatment context.
The Bottom Line
Although the adoption of AI in healthcare is still raising challenges, several applications are already being successfully deployed in a variety of fields—delivering numerous benefits to patients, clinicians, and healthcare stakeholders. By facilitating drug discovery and development, improving the patient experience, supporting healthcare professionals, and contributing to the diagnosis and treatment of various diseases, AI is sure to play a critical role in the future of healthcare.
