A new study published in The Lancet shows how an AI tool called Foresight (which fully analyses patient health records and makes digital twins of patients) could be used to predict the future of your health.
What Is Foresight?
The Foresight tool is described by the researchers as a “generative transformer in temporal modelling of patient data, integrating both free text and structured formats.” In other words, it’s a sophisticated AI system that’s designed to analyse patient health records over time.
What Does It All Mean?
The “generative transformer” type of AI is a machine learning / large language model (an ‘LLM’) that can generate new data based on what it has learned from previous data. The term “transformer” is a specific kind of model that’s very good at handling sequences of data, like sentences in a paragraph or a series of patient health records over time (temporal), i.e. a patient’s electronic health records (EHR).
Unlike other health prediction models, Foresight can use a much wider range of data in different formats. For example, Foresight can use everything from medical history, diagnosis, treatment plans, and outcomes, in both free text formats like (unorganised) doctors’ notes or radiology reports and more structured formats. These can include database entries or spreadsheets (with specific fields for patient age, diagnosis codes, or treatment dates).
Why?
The researchers say that the study is aimed to evaluate how effective Foresight is in the modelling of patient data and using it to predict a diverse array of future medical outcomes, such as disorders, substances (such as to do with medicines, allergies, or poisonings), procedures, and findings (including relating to observations, judgements, or assessments).
The Foresight Difference
The researchers say that the difference between Foresight and existing approaches to model a patient’s health trajectory focus mostly on structured data and a subset of single-domain outcomes is that Foresight can take a lot more diverse types and formats of data into account.
Also, being an AI model, Foresight can easily scale to more patients, hospitals, or disorders with minimal or no modifications, and like other AI models that ‘learn,’ the more data it receives, the better it gets at using that data.
How Does It Work? (The Method)
The method tested in a recent study involved Foresight working in several steps. In the research, the Foresight AI tool was tested across three different hospitals, covering both physical and mental health, and five clinicians performed an independent test by simulating patients and outcomes.
In the multistage process, the researchers trained the AI models on medical records and then fed Foresight new healthcare data to create virtual duplicates of patients, i.e. ‘digital twins’. The digital twins of patients could then be used to forecast different outcomes relating to their possible/likely disease development and medication needs, i.e. educated guesses were produced about any future health issues, like illnesses or treatments that might occur for a patient.
The Findings
The main findings of the research were that the Foresight AI tool and the use of digital twins can be used for real-world risk forecasting, virtual trials, and clinical research to study the progression of disorders, to simulate interventions and counterfactuals, and for educational purposes. The researchers said that using this method, they demonstrated that Foresight can forecast multiple concepts into the future and generate whole patient timelines given just a short prompt.
What Does This Mean For Your Business?
Using an AI tool that can take account of a wider range of patient health data than other methods, make a digital twin, produce simulations, and forecast possible health issues and treatments in the future, i.e. whole patient timelines until death could have many advantages. For example, as noted by the researchers, it could help medical students to engage in interactive learning experiences by simulating medical case studies. This could help them to practice clinical reasoning and decision-making in a safe environment, as well as helping them with ethical training by facilitating discussions on fairness and bias in medicine.
This kind of AI medical prediction-making could also be useful in helping doctors to alert patients to tests they may need to take to enable better disease-prevention as well as helping with issues such as medical resource planning. However, as many AI companies say, feeding personal and private details (medical records) into AI is not without risk in terms of privacy and data protection. Also, the researchers noted that more tests are needed to validate and test the performance of the model on long simulations. One other important point to remember is that regardless of current testing of the model, Foresight is currently predicting things long into the future for patients and, as such, it’s not yet known how accurate its predictions are.
Following more testing (as long as issues like security, consent, and privacy are adequately addressed) a fully developed method of AI-based health issue prediction could prove to be very valuable to medical professionals and patients and could create new opportunities in areas and sectors related to health, such as fitness, wellbeing, pharmaceuticals, insurance, and many more.