Building AI Solutions for Life Sciences and Healthcare

Artificial intelligence (AI) has swiftly become a focal point in modern industry discussions. Organizations are transitioning from mere experimentation to actively integrating AI into their workflows. The AI market is experiencing rapid growth, with projections estimating it will reach $407 billion by 2027.

AI models are capable of a wide range of functions, including image and speech recognition, predictive analytics, and decision-making support. They excel at identifying patterns and making predictions based on large datasets, which is invaluable in data-intensive fields.

In the biotechnology sector, AI is making significant contributions by accelerating research and development processes. AI models assist in drug discovery by predicting how different compounds will interact at the molecular level, thereby identifying promising therapeutic candidates more efficiently. They also play a crucial role in genomics, analyzing genetic data to uncover patterns associated with diseases, which aids in the development of personalized medicine.

What would a roadmap for building AI software look like? Let’s walk through the main phases of development, discussing both the nuts and bolts of each stage, briefly, and some common challenges that tend to come up along the way.

1. Preview Phase: Setting the Stage

Building an AI system always starts with identifying a clear problem or area where AI can add value. This isn’t as straightforward as it sounds. One needs to ensure that the problem is well-defined, and that AI is the appropriate solution for it.

Activities to focus on:

Problem Identification and Goal Setting: Is AI supposed to identify patterns, predict future outcomes, or automate repetitive tasks? For example, if one is working with large datasets that are too unwieldy for traditional methods, AI could potentially detect trends that would be nearly impossible to spot manually.
Initial Solution Outlining: Potential AI techniques, such as supervised learning (where the model learns from labeled data to make predictions) or unsupervised learning (which identifies patterns without labeled input), are considered. The idea is to identify which type of model could work before diving into development.
Preliminary Time and Budget Estimates: Timelines and budgets are always in flux during the preview phase, but having a rough sketch can provide guidance.

The goal of this phase is to get everyone aligned on the problem and agree on what needs to be accomplished, laying a foundation for further exploration.

In the context of AI, a "model" is a mathematical framework or algorithm designed to identify patterns within data and make predictions or decisions based on those patterns. It learns from examples and adjusts its internal parameters over time to improve accuracy. For instance, an image recognition model trained on thousands of labeled pictures of different animals becomes capable of distinguishing between a cat and a dog in new images. Each time it processes new data, it refines its understanding, making it more precise. This iterative process continues until the model’s predictions closely align with the desired outcomes, enabling it to perform tasks such as classification, forecasting, or anomaly detection effectively.

2. Discovery Phase: Diving into the Details

Once there’s clarity on the overarching goal, it’s time to take a closer look at the data and tools required. This phase focuses on assessing what’s available, what’s missing, and what’s needed to build the AI solution.

Data Analysis and Feasibility Study: Start by examining the data—Is there enough of it? Is it clean? Does it represent all aspects of the problem? For instance, if a project is aimed at predicting system failures, and the historical data only includes successful operations, the model won’t learn what a failure looks like.
Competitor Analysis: Understanding how others are using AI in a similar context can highlight both opportunities and limitations. It’s about gaining insight into what’s already been done and identifying gaps that the AI solution can fill.
Implementation Roadmap: With data and feasibility sorted out, a detailed roadmap is developed, laying out the steps required to turn the idea into reality. This roadmap acts as a strategic blueprint, helping to keep the project on track and aligned with its goals.

The discovery phase can either validate the initial assumptions or highlight the need to refine the approach, making it a critical step before moving into full-scale development.

3. Data Preparation and Labeling: Preparing the Inputs

Data is the fuel for any AI system, but raw data is rarely in a format ready for immediate use. This phase involves transforming raw data into a structured format that can be processed by machine learning algorithms. Core activities at this stage would be:

Data Cleaning and Structuring: This step involves eliminating duplicates, standardizing formats, and ensuring consistency—like arranging books in a library, where each one needs to be properly sorted and shelved to make information easy to find and use.
Data Labeling: Annotating datasets with labels helps models understand what they’re supposed to look for. For instance, labeling cells in images as “healthy” or “anomalous” provides context for the model to differentiate between them.

Effective data preparation dramatically influences the success of the AI model. It is a key step that can save countless hours of troubleshooting down the line. High-quality data leads to more accurate and reliable models.

Algorithms vs. Models:

It's important to distinguish between algorithms and models:

Algorithms are procedures, often described in mathematical language, applied to datasets to achieve a specific function.

Models are the output of algorithms applied to data. In simple terms, an AI model uses algorithms to make predictions or decisions.

Notable mention: Foundation Models, or "base models"—are deep learning models pre-trained on large-scale datasets to capture general features. Instead of building from scratch, developers can adapt these models by tweaking parameters or modifying architectures, saving time and resources. Prompt-tuning, a newer technique, uses targeted inputs to guide models more efficiently.

See also: 19 Companies Pioneering AI Foundation Models in Pharma and Biotech

4. Development Phase: Building the Model

Once the data is ready, it’s time to build the actual AI model. Much like usual human learning—it’s all about repeated exposure and correction until the model learns to respond in a desirable way.

Key Concepts:

Machine Learning (ML): ML is a subset of AI that enables models to learn from data. ML models can automate decision-making, but only those capable of learning can autonomously optimize their performance over time.

Types of Learning:

Supervised Learning: The model learns from labeled data. A data scientist labels the training data, and the model uses these labels to learn patterns.

Unsupervised Learning: The model identifies patterns without labeled input, useful for clustering and association tasks.

Reinforcement Learning: The model learns through trial and error, receiving rewards for correct actions.

The model is trained by feeding it data repeatedly, adjusting its parameters like learning rate (how fast it learns from each example) and batch size (how many data samples it processes at a time). Another key aspect is avoiding overfitting, which happens when the model becomes so good at handling the training data that it struggles with new, unseen information, an example being a student who memorizes every textbook word but fails to apply the knowledge in an abstract way to solve real-world problems.

To avoid this, methods like regularization are used, which penalize the model for becoming too complex, ensuring it remains adaptable to fresh data. After training, the model is tested on a new set of data to see if it can make accurate predictions or decisions.

5. Deployment and Integration: Putting the Model to Work

Once the model is built, it’s time to put it into action. This phase involves integrating the AI system into the existing infrastructure and ensuring it performs as expected in real-world scenarios. Deployment isn’t just about hitting a “Start” button. It includes:

Integration: The AI model is seamlessly incorporated into existing systems without disrupting other processes.
Monitoring Performance: Continuous tracking of the model's accuracy and efficiency is essential. If performance drops, it may indicate changes in data patterns, necessitating adjustments or retraining.
Scaling and Updates: As the volume of data grows or tasks become more complex, the model is scaled accordingly. Updates are made to adapt to new insights or changes in the operational environment.

Deployment is not the end, but just the beginning of the model's life in production. Monitoring is key here, as the AI model might need adjustments over time to remain accurate and reliable. For instance, if the model’s accuracy starts dropping, it could indicate that the data patterns have changed, requiring a retraining of the model. Additionally, scaling the model to handle larger volumes or more complex tasks is also part of this phase.

Navigating Challenges and Implementing Best Practices

Despite AI's promise, several challenges often arise during development.

Common Challenges:

Data Quality Issues: Incomplete or biased data leads to unreliable predictions. Investing in high-quality data collection and preparation might be a good idea.
System Integration: Integrating AI into existing systems can be complex. Ensuring compatibility and minimizing disruptions is essential for smooth operation.
Scalability Concerns: Balancing computational power, data volume, and cost, especially as the model scales.

Best Practices:

Setting Clear Objectives: Defining what success looks like from the outset keeps the project aligned with its goals.
Investing in Data Quality: High-quality data is the foundation of the AI model's performance.
Staying Flexible: Being open to adjustments as new insights emerge allows for adaptation and improvement.
Addressing Ethical Considerations: Ensuring the model doesn't develop biases or make unfair decisions is important.

Success in a project like this isn’t just about technical prowess. Setting clear objectives from the start helps keep the project aligned with the envisioned goals. Investing in data quality early on can prevent many headaches down the line. And adopting flexible development methodologies may allow for rapid adjustments as new insights emerge. Overall, AI software development is more than just coding—it’s a systematic process that requires careful planning, high-quality data, and continuous improvement.