Personalising women’s healthcare with artificial intelligence

Personalising women’s healthcare with artificial intelligence

Tim Simpson, general manager, Hologic UK & Ireland explores the potential of AI to improve access to targeted and personalised diagnosis and explores what is needed to truly personalise healthcare with AI.

Innovation in technology is set to shape healthcare in the future and as we look to bounce back from COVID-19, we need to explore how we can harness the power of artificial intelligence (AI) to transform and personalise women’s healthcare.

The Government aims to put the UK at the forefront of the AI and data revolution in early diagnosis, innovation, prevention and treatment[i]. We need to take advantage of this focus on AI to understand how it can facilitate identifying women at high risk of health conditions such as cancer, to ensure speedy diagnosis and access to treatment.

Issues in women’s healthcare are not new, however the COVID-19 pandemic has accelerated the need for urgency in improving health outcomes for women, tackling health inequalities, and creating an improved patient pathway through personalised screening.

One size does not fit all

AI has the potential to improve access to targeted and personalised diagnosis. 

While AI has been adopted in some cancer screening programmes, what we now need to see is a move towards a prioritised, risk assessed screening system rather than a one size fits all approach.

Personalised screening will require the use of AI for risk stratification to identify high risk patients. We need to explore how to use AI to create a molecular profile to determine relative risk. For example, we know that women with dense breasts are at higher risk of developing breast cancer and that 40% of European women aged 40-74 years old have dense breasts.[ii] We need to ensure these women are identified early so that they can be prioritised for screening.

The PROCAS 1 and 2 study in Manchester has looked into the impact of creating a breast cancer risk score for women. The studies have found that there is patient demand to understand risk with 94% of those recruited wanting to know their risk score. The studies also found positive benefit of risk stratification as women were then inclined to act on the risk information.[iii]

It is encouraging to see investment in AI for personalised diagnosis, as shown recently in a research study at the University of Strathclyde, who are developing a new AI technology to calculate women’s risk of pre-eclampsia. The tool will look at large data sets of women who have participated in previous research projects taking into account a range of factors including ethnicity, socio-economic status and details of the woman’s current pregnancy.[iv]

Diverse data sets are key to truly personalise healthcare with AI

For AI to truly transform healthcare, datasets need to be inclusive, taking into account all risk factors and not focusing on one specific demographic.

Researchers at Loughborough University recently announced a new study using AI to reduce risks faced by pregnant black women. Collaborating with the Healthcare Safety Investigation Branch, the team will review hundreds of investigations into adverse outcomes during pregnancy and birth. They will use machine learning to identify risk factors and as a result design ways to improve care for pregnant women and babies.[v]

Access to diverse data sets allows deeper understanding of how disease progresses amongst different populations and is critical to ensure accurate and unbiased profiling of patients. The more data points you have, the bigger the database and the more accurate the AI.

While significant progress has been made in adopting AI to improve healthcare, as we build on innovations we must ensure collaboration between industry, clinicians, and researchers to unlock the full power of AI to radically improve women’s health and save lives.  

[i] GOV.UK [Internet] The Grand Challenge missions [cited 2022 April 22] Available from:

[ii] Berg, WA & Vourtsis A, authors. Using education to overcome unequal access to supplemental screening for women with dense breast [Internet] DI Europe: 2020. Available from: dense-breast-screening-vourtsis-berg-febmch2020-1.pdf (

[iii]  Manchester Cancer Research Centre [Internet] PROCAS and BC-PREDICT Predicting the risk of cancer at screening. [cited 2022 July 22] Available from:

[iv] University of Strathclyde Glasgow [Internet] Funding for AI technology used to calculate pre-eclampsia risk [cited April 26 2022] Available from:

[v] Loughborough University [Internet] New Loughborough research will use Artificial Intelligence to help reduce maternal harm amongst mothers from black ethnic groups [cited July 19 2022] Available from:

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