IBM has asserted that quantum computing could contribute to diagnostic assistance through early, accurate, and efficient diagnosis. Current diagnostic tools include medical imaging techniques, such as CT, MRI, and X-ray scans. The margin of error with those tools remains significant, with 5-20% of diagnostic results incorrect. With the current technologies, traditional computing is approaching a plateau in its ability to help the healthcare industry. With the development of quantum computing, however, misdiagnoses can be avoided with greatly improved medical image analysis (including edge detection and image matching) and enhanced cell classification. Quantum computing can leverage its information systems to discover and characterize biomarkers which “may necessitate analysis of complex ‘-omics’ datasets” (such as genomics, transcriptomics, proteomics, and metabolomics).
Advancing diagnostic methods would create much-needed benefits for low-income countries, where on average there are just 0.3 physicians per 1,000 people and only 0.7 hospital beds per 1,000 people. Upper middle-income countries have over five times that amount. Better tools could alleviate pressure on both physicians and hospital resources by providing earlier and more reliable diagnoses, and by avoiding the need for additional diagnostic testing.
There are indirect knock-on effects that can help the most vulnerable communities as well. For example, during the COVID-19 pandemic, quantum computing assisted diagnostics might have eliminated the need for repetitive visits to healthcare sites, helping communities minimize their exposure to contagion. Historical accounts “clearly demonstrate that poverty, inequality, and social determinants of health create conditions for the transmission of infectious diseases.” Through quantum computing, diagnostic advancement doesn’t present just an efficiency gain, but a chance to reduce global inequalities in the most vulnerable communities.
Quantum simulation can help expand our understanding of molecular and sub-molecular interactions, leading to prospective breakthroughs in chemistry, biology, healthcare, and nanotechnology. A great challenge in healthcare is accurately predicting molecular interactions with the limitless other factors involved in individualized care. The degree of complexity and interdependency results in less precise medicine designed for each patient. Quantum computing could be the bridge towards optimized treatment effectiveness. This would be further enhanced when preceded by diagnostic advancements. It is predicted that both supervised and unsupervised quantum-enhanced machine learning techniques can aid granular risk predictions, ultimately benefiting patients through earlier and more accurate results. “Eventually, medical practitioners might even have the tools to understand how an individual’s risk for any given condition changes over time.” This is revolutionary, as it immensely surpasses existing capabilities of traditional computing worldwide.
Medical professionals today try to assess optimal treatments for their patients based on self-reported symptoms, diagnoses (assumed to be from non-QC assisted processes), and historical data attached to medical conditions. As it currently stands, identifying the effectiveness of treatments based on historical outcomes is inaccurate given that medical care only has a relative contribution of 10-20% towards outcomes while health-related behaviors, socioeconomic status, and environmental factors account for the remaining 80-90%. This means that the industry must maximize its effectiveness of <20%, especially for patients subject to adverse environments. Experts compare existing medical diagnoses and treatments to “an umbrella diagnosis” that frequently fails. Applying QC to this field will replace the umbrella approach with precise medicine, thanks to insights from more extensive datasets and more complex analysis to match the plethora of contributing factors pertaining to an individual’s health status.
Accelerating research and development in drug discovery is a highly promising application for quantum computing as it enables investigations of disease effects on a molecular level. Pharmaceutical companies struggle to ascertain the exact molecular structure of most enzymes since they are complex and impossible for classical computers to model. Quantum computing can transform modeling and analysis to accurately predict the properties, structure, and reactivity of enzymes, contributing to the potential alleviation of the major diseases of our time.  Not to mention, this could be done in a matter of hours.
The dream of near instant computational drug design has never been achieved. Drug discovery is delayed by the inadequacy of even the fastest computers today, leaving it to trial and error in highly controlled labs around the world. The process of designing a vaccine, for instance, involves identifying the drivers of the disease and disease pathways in the body, screening millions of candidate activators, and extensive drug trial stages. This process is not only challenging and subject to several limitations in accuracy, but it can take years and costs pharmaceutical companies ~$2.7 billion to release one drug. Cost minimization stemming from more efficient drug discovery processes could benefit consumers in the form of more affordable prescriptions worldwide, contributing to more equitable access to medicine.
This article is one section of the report, “Quantum Impact — The Potential for Quantum Computing to Transform Everything.” Click here to learn more and access the full report.
Please see the PDF version of the full report for important disclosures.
 Exploring Quantum Computing Use Cases for Healthcare