Algorithm

What is an algorithm in healthcare?

In healthcare, an algorithm is a set of rules trained on medical data to help predict, diagnose, or guide treatment for a patient's condition. Unlike the algorithm for turning flour, butter, and eggs into a cake, a healthcare algorithm turns medical data into information about a patient's condition.

These types of algorithms have a long history in medicine and aren't always digital. Clinical Practice Guidelines and medical calculators are both examples of algorithms, setting out a list of rules or steps for completing a task, like treating a condition or calculating BMI. Some are simple paper-based flowcharts or formulae a clinician can calculate mentally. In the context of AI, however, "algorithm" typically refers to a complex set of instructions written in a programming language for a computer to execute.

‍

What characteristics do all healthcare algorithms share?

Regardless of type, all algorithms share the same core characteristics:

• Lack of ambiguity: each step has a clear purpose and leads to only one type of output
• Specific inputs
• Specific outputs
• Finiteness: the algorithm must be capable of completing, even if it needs to run several times to reach a conclusion
• Feasibility: it must be able to complete its task using only the resources available to it
• Deterministic: the same input must always produce the same output
• Effectiveness: every step must do work necessary to complete the task

Beyond these shared traits, algorithm types vary considerably, and which one is used depends on the specific medical task: triage, classification, diagnosis, prognosis, risk prediction, treatment identification, or drug discovery.

‍

How is an AI healthcare algorithm built, step by step?

Building a healthcare algorithm follows five steps. Think of it as writing and testing a recipe.

Step 1: Gather the data. Almost all healthcare interactions are recorded in some way. A new diagnosis or prescription becomes a line in your electronic health record (EHR); an X-ray or MRI becomes a stored image. This recorded information is the "data," and it becomes the raw ingredients for the algorithm.

Step 2: Curate the data. Healthcare data is originally designed to help clinicians manage individual patients, not organized for machines to interpret. Before an algorithm can be trained on raw data, it needs to be cleaned: checked for accuracy and consistent formatting (for example, making sure dates follow the same format across records).

Step 3: Train the algorithm. A recipe doesn't just list ingredients, it specifies exact amounts. Training an algorithm works the same way: researchers determine how much "weight" each data point should carry. For an algorithm predicting Type 2 diabetes risk, body mass index might carry significant weight, while a history of ear infections carries almost none. The algorithm is trained repeatedly until it reliably gets this weighting right.

Step 4: Validate the algorithm. Researchers test whether the algorithm's "recipe" is correct by giving it new data it has never seen before, checking whether it can still accurately predict outcomes, for example, the likelihood of a new, unseen patient developing Type 2 diabetes.

Step 5: Test the algorithm. The final test isn't whether the original researchers can reproduce results. It's whether the algorithm works correctly for different clinicians in different hospitals, confirming it generalizes beyond the setting it was built in.

‍

Why do healthcare algorithms need to be validated and tested before use?

Validation and testing confirm an algorithm actually works, rather than just appearing to work on the data it was trained with. Validation checks accuracy on new, unseen patient data. Testing goes further, checking whether the algorithm holds up across different clinicians and hospitals, not just in the hands of the team that built it. This distinction matters: an algorithm that only performs well in its original research setting may fail to generalize to real-world clinical practice.

‍

What types of AI algorithms are used in healthcare?

Some of the most widely used AI algorithms in healthcare include:

Naïve Bayes Classifier. A supervised algorithm derived from Bayes' theorem, used for classification tasks such as predicting the probability that a new case falls into a specific diagnostic category (e.g., identifying a particular type of cancer).

K-Means algorithm. An unsupervised algorithm used for stratification or grouping. It divides a dataset into a set number of non-overlapping clusters, which can be used to group patients into subgroups, for example, based on genetic makeup.

Support vector machine. A supervised algorithm used for classification, often applied when working with incomplete datasets. This has made it particularly useful in drug discovery.

Artificial neural network (ANN). A reinforcement-learning-based algorithm composed of layered "nodes" designed to mimic the human brain. ANNs are widely used for medical image recognition, as well as text and speech recognition (such as free text in EHRs).

Case-based reasoning (CBR) classifier. A reasoning-based algorithm that, when presented with a new case, searches an existing database for similar past cases to classify it. This has been used to diagnose patients based on similar symptom profiles and to recommend treatments based on prior success.

‍

What's the difference between supervised, unsupervised, and reinforcement learning?

‍

What makes a healthcare algorithm successful?

How well any algorithm performs on a clinical task ultimately depends on how well it's designed by its human developers. Research shows the most successful healthcare algorithms share seven qualities: they are explainable, dynamic, precise, autonomous, fair, reproducible, and comprehensively evaluated — these are the same principles driving Owkin's own agentic AI platform, K Pro, built to bring biological reasoning to biopharma decision-making.