Erkin Ötleş

My name is Erkin Ötleş & I’m a physician-engineer (in perpetual training). This is my sporadically updated blog focused on engineering & medicine.

NB: this series is still a work in progress.

Healthcare AI Development

Welcome to the second post in our series on the healthcare AI lifecycle. To start at the beginning, go to the overview post on the healthcare AI lifecycle. Having established a general framework for the healthcare AI lifecycle, it’s time to cover some specifics. Without a better starting point1, this post focuses on what I perceive to be the “beginning” of the AI lifecycle: the development phase.

Development encompasses the various processes involved in creating an AI model. This phase is foundational, as the quality and success of the AI system largely depend on the quality of the development process. Each step, from selecting the right task to training a model and validating its performance, is crucial for ensuring that the AI tool will be effective and reliable in real-world clinical settings.

By the end of this post, you will have a comprehensive understanding of the critical steps in developing healthcare AI models and the challenges and considerations associated with each step.

    Healthcare AI development portion of the lifecycle. Development is the creation of models and involves predictive task selection, data access, data preparation, model training, and model validation.    
    Healthcare AI Development Portion of the Lifecycle.     The development phase encompasses all the steps necessary to create an AI model: task selection, data access, data preparation, model training, and culminating in model validation. This phase is crucial for building robust and effective AI tools that will be used in clinical care.    

Development Steps

The development phase of healthcare AI is a multifaceted process that starts with a target task and ends with a (hopefully 🤞🏽) robust and effective AI model. To provide a clear structure, I break down this phase into five discrete steps:

  • Task Selection
  • Data Access
  • Data Preparation
  • Model Training
  • Model Validation

As depicted in the figure above, it’s easiest to illustrate these steps as discrete and chronological. However, this linear representation is disingenuous and doesn’t fully capture the reality of the development process. These steps are semi-continuous and often non-linear. Model developers frequently jump back and forth between these steps or work on them concurrently. Despite this fluidity, these steps are generally present in all model development projects and tend to be finalized in the order presented.

This breakdown reflects my approach to structuring the development phase, providing a framework for understanding and navigating the complexities. Each step is crucial for building robust and effective AI models that will be used in clinical care. By understanding and addressing the nuances of these steps, we can ensure that the models developed are technically sound, clinically relevant, and reliable.

We will briefly discuss each development step, covering their key objectives and challenges.

Task Selection 

Choosing the right problem for AI to tackle is crucial. The journey begins with identifying the specific task or clinical problem we aim to address with an AI model. This step involves collaboration between clinicians and data scientists to ensure the model’s relevance and potential impact. It’s not just about finding a gap; it’s about making sure the AI solution can significantly improve outcomes or efficiency in a meaningful way. We’re looking for problems where AI can provide insights or automation that weren’t previously feasible.

Conducting thorough discussions with clinicians is essential to pinpoint where they feel the most pain or pressure and where they think AI could benefit them. Their firsthand experience and insights are invaluable in identifying tasks that truly matter.

Caution should always be exercised when someone says, “I just want an AI to predict/do X.” There may be deeper or related problems that should be uncovered before jumping directly in the initial direction. An excellent approach for overcoming this issue is to ask a series of probing questions. Some of my favorite lines of inquiry are:

  • Sequential “Why?” Asking “why?” (or “how?”) repeatedly is often a fast way of understanding the existing problem or system. This iterative questioning can uncover underlying issues that might not be immediately apparent.
  • Would magic help? Asking how a “perfect solution” would help (e.g., “If I could give you Y information with 100% accuracy, how would that help?”) gives you a sense of the maximum possible benefit of a solution. This helps us understand the potential impact and feasibility of the AI model.
  • Do you have data? If the answer is no, consider whether this project is feasible. Data availability is a fundamental prerequisite for any AI development, and its absence can significantly hinder progress.

In addition to these considerations, it’s essential to be mindful of potential biases in task selection. Suppose we choose a task such as predicting clinic no-shows (patients who do not attend a scheduled appointment). In that case, we must recognize that this could be problematic due to inherent systemic biases. Structural issues often prevent specific subpopulations from having consistent access to healthcare and building a model for this task might inadvertently propagate these biases. Instead of developing an AI model for predicting no-shows, it might be more beneficial to investigate other ways to address the root causes, such as creating programs to improve access to healthcare. In this case, the best AI model may be no AI model at all.

By selecting the right task and thoroughly understanding the problem, we set a solid foundation for the subsequent steps in the AI development lifecycle. This ensures that the AI model developed is technically sound, highly relevant, and impactful in real-world clinical settings.

Data Access

Getting the correct data is often the first big hurdle of AI development. The data needs to be comprehensive, clean(able), and relevant. This step frequently involves negotiating access to sensitive data, like medical records, while ensuring patient privacy and data security. You must consider the following:

  • Provenance: where is the data coming from? Who’s going to get it for you?
  • Protection: how will you ensure that the data are adequately protected? I recommend working directly in hospital IT systems or working with them to spec out compliant environments.
  • Prospective use: will you have this data available when using this system prospectively in the real world?  

Data Preparation

Having obtained data, model developers may realize that healthcare data, like healthcare itself, is complicated. Processing and transforming data for AI model development requires a unique mix of clinical and technical expertise. Preparing this data for AI involves:

  • Cleaning it.
  • Dealing with missing values.
  • Transforming it into a format that algorithms can work with.

This step is usually labor-intensive; 90% of the engineering time will be dedicated to data preparation. Tools can help automate data preparation. I made a tool called TemporalTransformer that can help you quickly convert EMR or claims data into a format ready for processing with neural networks/foundation models. I discuss it in the supplement of my paper on predicting return to work.

Model Training

Training the model may be the most exciting step for the technical folks. But it’s often one of the shortest parts of the project (in terms of wall time, not CPU/GPU time). In this step, We select algorithms, tune parameters, and iteratively improve the model based on its performance. This step is a mix of science, art, and a bit of luck. The goal is to develop a model that’s both performant and generalizable. There are many resources dedicated to model training, so I won’t cover the details here.

Model Validation

After being developed, models must be validated to assess whether they benefit patients, physicians, or healthcare systems. Validation means testing the model on new, unseen data to ensure it performs well in settings representative of intended real-world usage. Ultimately, it’s about making sure the model doesn’t just memorize the data it’s seen but can also make good predictions when used in practice.

This step often involves internal and external validation to ensure robustness. There are varying definitions for internal and external validation, but the distinction I like to use is based on the system generating the underlying data. If the data comes from the same system (e.g., the same hospital, just a different time span), then I would consider it internal validation data. A well-conducted external validation is a great way to assess whether a model will work in a given environment.  However, external validation may be challenging due to data-sharing restrictions. Despite this challenge, it is often a great place to engage with healthcare AI systems, especially for physicians. Here are some examples of external validation studies that I’ve worked on:

Wrapping Up

We’ve taken a closer look at the development phase of healthcare AI. Each step is filled with unique challenges and requires a blend of clinical insight and data science expertise. While we’ve covered a lot of ground here, each development step could merit a more detailed post; please let me know if that’s something you would be interested in reading. The discussions here have been intentionally brief to provide an overview and establish a foundation. 

Thank you for joining me on this exploration of healthcare AI development. The following post will cover the steps necessary for implementation.

Some of this content was adapted from the introductory chapter of my doctoral thesis, Machine Learning for Healthcare: Model Development and Implementation in Longitudinal Settings.

Cheers,
Erkin
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  1. “If you wish to make an apple pie from scratch, you must first invent the universe.” - Carl Sagan ↩

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