Machine learning is a powerful tool that could help the healthcare industry in several ways. As researchers, clinicians, and manufacturers seek to incorporate machine learning into daily practice, we take a look at what it is, how it can be used, and where the challenges remain.
ML is a type of artificial intelligence that enables systems or software applications to learn from historical data and patterns to predict outcomes without being explicitly programmed, essentially learning from experience. Today, clinicians, scientists, policy-makers, and regulatory agencies use machine learning to varying degrees to help improve patients’ health, reducing costs and driving efficiencies.
Drug discovery and development
New R&D methods that use machine learning can help us understand disease biology better and improve drug and disease targeting, potentially reducing drug development timelines and costs.
Each new drug can take 10 to 15 years from the initial discovery of the compound to commercialization at an average cost of $1-2 billion spent in R&D and clinical testing using more traditional approaches.7 Overall, 90% of clinical drug development fails. As a result, many pharmaceutical companies are now attempting to integrate machine learning into their drug discovery processes in a bid to decrease the time it takes to get new therapeutics into the clinic.
According to some research, there are several areas where machine learning implementation for drug discovery design can ramp up research efforts and improve hit-to-lead and clinical decision-making. Some areas include:
- Drug–protein interaction predictions
Drug efficacy
Discovery of safety biomarkers
Protein folding prediction
Virtual screening
Drug repurposing
Quantitative structure-activity relationship
Diagnosis and prognosis
Successfully diagnosing diseases remains challenging due to the variability in disease mechanisms and the diverse ways symptoms can manifest in individual patients. It is still a big problem for Health care industry the incorrect diagnoses: in the US, nearly 6% of emergency department visits result in misdiagnosis, and approximately 75% of serious medical events such as vascular incidents, infections, and cancers stem from diagnostic errors. According to a 2015 report by the National Academies of Sciences, Engineering, and Medicine, the majority of people will experience at least one diagnostic error in their lifetime.
Research indicates that machine learning offers a promising solution to improve the speed and accuracy of disease detection and diagnosis by learning patterns indicative of illness. For chronic diseases, machine learning has shown superior predictive capabilities compared to traditional methods. For instance, Rashid et al. developed an enhanced artificial intelligence technique that more reliably and efficiently predicted diagnoses like breast cancer, diabetes, heart attack, hepatitis, and kidney disease compared to conventional approaches.
Machine learning is increasingly vital in fields such as pathology and radiology.
In a systematic review and meta-analysis, Liu et al. demonstrated that medical imaging analysis by machine learning can achieve diagnostic classification performance comparable to healthcare professionals. Consequently, several imaging medical devices employing machine learning have gained approval from the US FDA, underscoring their potential benefits in clinical practice.
Machine learning is set to revolutionize the healthcare industry by enhancing drug discovery, improving diagnostic accuracy, and optimizing patient care. Despite the challenges, its integration promises significant advancements, reducing costs and increasing efficiency. As technology evolves, the potential for improved health outcomes and streamlined medical processes continues to grow.
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How do you think the healthcare industry will evolve in the next decade through technology inputs?
