Four Data Obstacles in the Development of Medical AI
As healthcare and medicine approach the fourth industrial revolution, which is characterised by increased automation and the use of smart technology, artificial intelligence (AI) holds significant promise in areas such as drug discovery, patient diagnosis, and treatment. Despite constantly expanding demand, model development continues to face obstacles. These difficulties include those inherent in machine learning, such as the requirement to address edge cases, avoid AI bias and drift, and require a huge amount of human effort to train data and validate and enhance machine learning models regularly
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It is critical to overcome these obstacles in order to have a meaningful and measurable impact on patient outcomes and medical research. Additionally, the healthcare industry faces unique problems, which must be addressed in order to ensure the safe and timely implementation of AI in real-world scenarios. After all, there is little margin for error when the difference between sickness and wellness is at stake
1. Protection of Personal Information and Regulatory Compliance
All healthcare data must adhere to severe regulatory requirements, including HIPAA and HITECH in the United States and GDPR in Europe. These mandatory guidelines were implemented to protect patient privacy and to regulate the acquisition, management, and transmission of personally identifiable information (PII), such as healthcare records and medical images. Compliance in the development of medical AI is exacerbated by the fact that these data privacy standards predate AI and many of the other technologies on which healthcare relies today
To deliver on the promise of medical AI, massive amounts of data, such as MRI scans and X-rays, are required to build sufficient training data sets. As such, a transparent process must exist for individuals to opt in to contribute to development. Fortunately, in the majority of circumstances, it is easy to anonymize healthcare data for use in developing AI models without jeopardising its utility
For instance, V7 Labs and CloudFactory have collaborated to make available an annotated X-ray dataset to aid with COVID-19 research. V7 gathered 6,000 lung scans from several open-source datasets—a mix of patients with and without COVID-19—and assisted in training CloudFactory's managed workforce to optimise the data for machine learning by combining AI-driven auto-labeling and detailed human-led image annotation
2. Data Access and Collection
Additionally, the massive amounts of data required for medical AI training face logistical issues. Depending on the model's objective, this may include data collection from a variety of sources, including electronic health records, insurance claims, pharmacy information, and consumer-generated data via activity trackers and other wearable technology. Because this data is frequently fragmented across numerous systems, curating complete, high-quality data sets can be a significant undertaking
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Continued digitalization of healthcare records, in conjunction with a simple and transparent mechanism for patients to participate to AI development, is crucial for this to occur. Fortunately, new frameworks for treating health data as a public good while safeguarding patient privacy are being established, making it easier for research and development teams to obtain the data necessary to build effective models
3. Artificial Intelligence Bias
Another consideration for the data collection stage is the requirement for representative data sets. Without a diverse workforce, AI prejudice can become a severe problem, to the point of impairing patient care. For instance, AI developed to detect sickle cell disease must take into account the fact that the sickle cell trait is significantly more prevalent in African American and Central and South American ancestry communities. As a result, the data sets utilised to train the model must have an exact, matching representation of those populations
A similar issue with AI bias is the model itself, which is designed by humans and may contain prejudice. As Michael Li recently noted in his Harvard Business Review article, "the bias in our human-built AI is almost certainly due to a lack of diversity among the humans who built them." It has thus been noted that diversity within the engineering or data science team developing the model can be critical in reducing AI bias during development, even before training data is introduced
4. Expertise in the Industry and Quality Assurance
Quality control and quality assurance are critical in the development of medical AI. After all, artificial intelligence in healthcare is no longer completely passive. Along with assisting practitioners in their decision-making processes, AI may function in their place in some applications. As a result, the tolerance for errors is quite low. To earn the trust of patients and providers, medical AI models must be extensively trained and optimised. During these stages, model specialisation is frequently required
For instance, if AI is to interpret X-rays and MRIs in order to recommend a diagnosis and directly effect patient care, the images used for training should undergo a rigorous quality assurance/quality control process to verify labelling accuracy for each training image. On the other hand, if the project involves labelling data for preliminary, exploratory medical research, where speed and direction setting are paramount, a less rigorous quality assurance method may be more acceptable. The trick is to ensure that your quality assurance procedures are proportionate to the acceptable margin of error for your project. Having said that, it is critical to have a skilled staff that is capable of scaling quickly and possesses the aptitude, ability, and patience necessary to learn and work effectively without sacrificing quality
Scaling Medical Artificial Intelligence Through a Managed Workforce
While it may appear that the issues associated with medical data processing and labelling are unique to medical practitioners, this is not always the case. After instance, such an approach is difficult to scale, and it may have a negative impact on critical elements such as patient outcomes if it requires medical practitioners to divert their attention away from direct patient care or research
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Due of the stringent quality control and assurance requirements, typical business process outsourcing and crowdsourcing are rarely suited for the compilation of medical data. While it may appear that the only way to scale this highly specialised labelling is to hire personnel skilled in the specialty field to execute the labelling, this can create a significant bottleneck and financial burden. Collaboration with a skilled managed workforce adept in annotating highly specialised medical images, on the other hand, can relieve the strain without sacrificing quality or monitoring. Scaling effectively will require a rigorous approach that includes extensive vetting, skill development, and openness
Discover how our managed workforce can assist you in overcoming the scalability problems inherent in medical data annotation
