59 TWENTYFOURSEVENBIOPHARMA Issue 1 / March 2026 ARTIFICIAL INTELLIGENCE chronic diseases, such as cancer, are still diagnosed too late. Addressing delays in diagnosis and ensuring timely access to treatment are essential steps in improving disease control worldwide. Early diagnosis allows for more effective treatment and serves as a key public health strategy in all settings. Overcoming barriers to early diagnosis-such as patient delays, lack of awareness, limited healthcare resources, and inadequate referral systems-can help improve outcomes. Traditional machine learning (ML) approaches, like random forests and support vector machines, and deep learning (DL) models, such as convolutional neural networks (CNNs), are increasingly used for early diagnosis in imaging and molecular diagnostics. The ability of Al to process complex datasets at scale is particularly useful in diagnostic imaging, where tools like Google Health’s deep learning algorithm for breast cancer detection and IBM Watson’s oncology platform are already being used to assist clinicians. These Al-powered solutions can analyze X-rays, CT scans, and MRIs to detect abnormalities early, such as tumors or plaques, and flag them for further review. The use of Al for early diagnosis is also expanding into other areas, such as genomics, where Al is used to predict an individual’s risk of developing conditions based on their genetic profile. Source: AAAI, European Association for Artificial intelligence, Canadian Artificial intelligence Association, IEEE, AIPAT, Data Science Association, IAAIL, Company Websites, and MarketsandMarkets Analysis.
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