AI Cancer Precision Medicine

Revolutionizing Cancer Care: The Power of Artificial Intelligence

Karina Bhargava—McMaster Kinesiology 2026

Cancer refers to any one of a large number of diseases characterized by the development of abnormal cells that divide uncontrollably and have the ability to infiltrate and destroy normal body tissue as well as spread throughout your body.1 It is the second leading cause of death in the world.1 Cancer is caused by mutations to the DNA within cells. These mutations can instruct a healthy cell to allow rapid growth, fail to stop uncontrolled growth or make mistakes when repairing DNA errors.1

Cancer is a highly adaptable disease which causes it to endure the constantly changing microenvironments that its cells encounter2. Cancer cells have a certain degree of adaptive immune resistance, a process in which the cells change their phenotype in response to cytotoxic or proinflammatory immune response9. This response is triggered by the recognition of cancer cells by T cells, leading to the production of immune-activating cytokines9. Cancer cells then hijack mechanisms developed to limit inflammatory and immune responses and protect themselves from the T cell attack9. Because of this process, cancer continues to thwart patients, researchers, and clinicians despite significant progress in understanding its biological underpinnings.3

SOURCE: Future Processing Better Future

As more is learned about the disease itself, more can be learned about how tools can be useful in treatment plans. Currently, artificial intelligence is used in the detection of cancer as it effectively analyzes complex data from many modalities, including clinical text, genomic, metabolomic, and radiomic data (the extraction of mineable data from medical imaging)6. An example of artificial intelligence in cancer diagnosis is imaging tests, which allow your doctor to examine your bones and internal organs in a non-invasive way7. This may include a computerized tomography (CT) scan, bone scan, magnetic resonance imaging (MRI), positron emission tomography (PET) scan, ultrasound and X-ray7.

SOURCE: Stanford Medicine

Artificial intelligence is also used for cancer treatment through something called “precision medicine.”  Precision medicine uses specific information about a person’s tumor to help make a diagnosis, plan treatment, and/ or evaluate the effectiveness of treatment5. It involves testing DNA from a patient’s tumor to identify the mutations or other genetic changes that drive their cancer8. Doctors can select a treatment plan that is best suited for that specific patient. Because no two cancers are identical, precision medicine is important as each patient   has a unique combination of genetic changes2.

Overall, artificial intelligence provides a gateway to push the boundary of cancer treatment. Currently, it is used most in the detection of cancers through CT scans, bone scans, and PET scans, among others. However, as artificial intelligence is adopted into clinical oncology, its potential to redefine cancer treatment is becoming evident.


1. Cancer [Internet]. Mayo Clinic. Mayo Foundation for Medical Education and Research; 2021 [cited 2022Nov13]. Available from:

2. Nguyen LTS, Jacob MA, Parajon E, Robinson DN. Cancer as a biophysical disease: Targeting the mechanical-adaptability program [Internet]. Biophysical journal. U.S. National Library of Medicine; 2022 [cited 2022Nov13]. Available from:,the%20vascular%20system%20and%20body

3. Linda WB, Hosney A, Schabath MB, Giger ML, Birkbak N, et al. Artificial Intelligence in cancer imaging … – wiley online library [Internet]. American Cancer Society. John Wiley & Sons; 2019 [cited 2022Nov13]. Available from:

4. Shreve JT, Khanani SA, Haddad TC. Artificial Intelligence in oncology: Current capabilities, future opportunities, and ethical considerations [Internet]. American Society of Clinical Oncology Educational Book. American Society of Clinical Oncology; 2022 [cited 2022Nov13]. Available from:,system%20capacity%20and%20allocating%20resources

5. NCI Dictionary of Cancer terms [Internet]. National Cancer Institute. [cited 2022Nov13]. Available from:

6. Hunter B, Hindocha S, Lee RW. The role of artificial intelligence in early cancer diagnosis [Internet]. National Library of Medicine. U.S. National Library of Medicine; 2022 [cited 2022Nov15]. Available from:

7. Cancer [Internet]. Mayo Clinic. Mayo Foundation for Medical Education and Research; 2021 [cited 2022Nov15]. Available from:

8. Precision cancer medicine: 5 things you should know [Internet]. Brigham Health Hub. Brigham and Women’s Hospital; 2020 [cited 2022Nov15]. Available from:,mutations%20in%20the%20tumor%20DNA

9. Ribas A. Adaptive immune resistance: How cancer protects from immune attack [Internet]. National Library of Medicine. U.S. National Library of Medicine; 2015 [cited 2022Nov25]. Available from:

10. Kornakiewicz A. Automation of Cancer Diagnostics and Treatment Using Artificial Intelligence [Internet]. Future Processing Better Future. Graylight Imaging; 2017 [cited 2023Jan3]. Available from:

11. Types of Magnetic Resonance Imaging Exams [Internet]. Stanford Medicine. Stanford Health Care; 2022 [cited 2023Jan3]. Available from:

AI Genomics Proteins

Unlocking the Secrets of Proteins with Alphafold2: A Breakthrough in Bioinformatics

Rubani Suri—McMaster Health Sciences 2026

The word “protein” has an ever-changing definition throughout our lives. As children, we often unknowingly consume protein in the form of nuggets or hamburgers. As we reach adolescence, proteins often appear on food guides and in our biology classes through the form of polypeptides and amino acids. However, not until the recent developments of AlphaFold2 Artificial Intelligence have proteins been defined as a complex three-dimensional network of amino acid residues.

To understand the significance of AlphaFold2, we must first understand the “protein folding problem.” Three-dimensional proteins are more than amino acid chains and are known for having multiple side chains on their structure. These side chains have the capacity of interacting with one another, creating configurational changes to the structure of the protein. As a result, it becomes nearly impossible to determine the structure of a three-dimensional protein due to side chain complexities (1).

That’s where AlphaFold2 comes in.

Using the power of Artificial Intelligence (AI), AlphaFold2 has mastered the technique of homology modeling: using evolutionary history to find proteins with known structure that are genetically similar to the “target protein,” and use them to deduce structural similarities with the target protein (2). Using comprehensive databases, AlphaFold2 uses AI to predict target protein structure through the following steps (3):

  1. The input sequence (genome of target protein) is inputted
  2. Multiple Sequence Alignments (MSAs), which are amino acid sequences that share evolutionary similarities with the target protein, are inputted into Alpha Fold machinery to create predictions for the structure based on evolutionary relatedness
  3. Protein database structures, which are similar in structure to the target, are also used as templates for target protein structures
  4. The input sequence is paired with itself in a matrix to produce an array of numbers that represents all the potential pairs of amino acid sequences in the target protein.
  5. The pair representations are put into “EvoFormer” technology, which collates all this data to analyze relationships between individual amino acids, to gain an understanding of the structures that specific amino acids would form when bonded to one another
  6. These predicted relationships are then put through a Structure Module technology, which builds a geometric protein model.
  7. This protein model is then analyzed, and the rotation and angle of each amino acid is calculated, creating a three-dimensional protein model.
  8. Side chains are predicted using a technology that detects ‘chi angles’ (angles between intersecting planes) on the three-dimensional residue structure.
  9. The bond lengths and angles are finalized by running the final structure through a relaxation step, which removes any inconsistencies within the protein structure.
  10. The final accuracy is then improved by running the predicted protein chain through the network three times more.
  11. Along with the predicted structure, the Alpha Fold technology creates two confidence matrices which provides a ‘confidence score’ for each angle between the residues by analyzing the predicted error in the predicted structure.

Figure 1. SOURCE: AlphaFold Protein Structure Database

Figure 1 depicts a structural prediction for a target protein once all the steps above are complete. The protein depicted in Figure 1 is hemoglobin, a globular transport protein found in erythrocytes.

Figure II. SOURCE: AlphaFold Protein Structure Database

Figure II depicts the confidence score of Hemoglobin, as determined in step 11 of the process shown above.

Although in its developmental stages, AlphaFold2 is a technological advancement that has the capacity to revolutionize both the pharmaceutical and biochemical world. This innovation has been groundbreaking, especially for pharmaceutical companies. This has been crucial as they are interested in the structure prediction of allosteric sites where small molecules can bind to produce cell responses such as inflammation, itching, and pain. Understanding the structure of these protein binding sites will allow drug developers to create specific inhibitors for these binding sites, preventing small molecules from binding and creating a painful response (4). The understanding of binding site structure will allow for the possibility of “structure-based drug design” (4), a technique that is estimated to accelerate the research and development of drugs from “years to months” (4).

In conclusion, the publicly accessible nature of AlphaFold2 protein structure data allows drug development companies to have readily available protein information at their fingertips, accelerating drug development and efficacy. Through its continued success, AlphaFold2 has the ability to revolutionize the pharmacological world, allowing for the accessibility of effective, fast-acting medications around the world.


  1. Singh J. The history of the protein folding problem: A seventy year symbiotic relationship between… [Internet]. Medium. Medium; 2020 [cited 2022Nov27]. Available from:
  2. Alessia David Person Envelope Suhail Islam Evgeny Tankhi levich Michael J.E.Sternberg, Highlights AlphaFold, et al. The alphafold database of protein structures: A biologist’s guide [Internet]. Journal of Molecular Biology. Academic Press; 2021 [cited 2022Nov27]. Available from:
  3. Callaway E. What’s next for alphafold and the AI protein-folding revolution [Internet]. Nature News. Nature Publishing Group; 2022 [cited 2022Nov27]. Available from:,the%20PDB%20and%20other%20databases.
  4. Mullard A. What does alphafold mean for drug discovery? [Internet]. Nature News. Nature Publishing Group; 2021 [cited 2022Nov27]. Available from:
AI Precision Medicine

Precision Medicine and AI: the Future is Here

Stephanie Chung—McMaster University Honours Life Sciences 2023

With all the advancements in the scientific community and precision medicine, artificial intelligence (AI) has become a reality. Precision medicine is health care tailored to an individual based upon characteristics such as genes, lifestyle, and environment, according to Hodson (1), a Supplements Editor for Nature Outlook supplements. Artificial intelligence is investigating intelligence agents and systems that are capable of solving complex goals (2). 


Description automatically generated

Source: National Aeronautics and Space Administration 

Precision medicine requires using all medical therapies/techniques that have been developed, clinical trials, along with individuals to order to create a customized treatment plan for a patient. Precision medicine has the goal of moving away from a general one-size-fits-all approach and into tailor-made programs for individuals with the same conditions and similar characteristics. In order to do so, extensive data must be collected from a large population. In 2015, Barack Obama announced an initiative to have over a million people enrolled in the All of Us Research Program (3). The resulting data contained personally reported information, digital health technologies, electronic medical records, and sequencing. In the future, the goal of precision medicine is to shift the focus of health care to assessing health, proactive management of disease risks and prevention (3). In order to do so, volunteers (anonymously) are going to be required to allow permission of their health records and genetic codes, as precision medicine requires patient data (1). The issue at hand is getting the public to trust precision medicine researchers with such personal (valuable) information.  

A person looking at a screen

Description automatically generated with low confidence

Source: Corporate Finance Institute

Healthcare is already being influenced and shifted due to artificial intelligence. Some of the achievements made so far are in cancer and cardiovascular diseases (4). This is done through an integrating new and existing learning approaches, along with using the data gathered from artificial intelligence to benefit the patient, as well as advancing the scientific field (4). Artificial intelligence also has the ability to change the world and our everyday lives. Companies may use artificial intelligence to provide benefits for consumers, through wearable devices for health monitoring, smart household products that offer peace of mind, and voice-activated devices for assistance (5). These are all making our daily lives much more convenient and have become part of our daily routines. However, through these devices, data capturing is required, which may result in consumers feeling threatened by an invasion of privacy. This is technology most of us do not understand and in order to feel more secure, there need to be rules and regulations set on what companies can and cannot collect. Companies could also aid in this through actively educating consumers on the benefits of artificial intelligence along with what data they are recording (5). Artificial intelligence may result in less white-collar employees and qualified jobs (6). An example of this trend can be seen with physicians being outperformed with image recognition tools to detect skin cancer (6). However, we must acknowledge that as time passes, the job market is bound to change, however it is not certain where the workforce will shift to in the future (6). Therefore, we must cherish the jobs that cannot be automated, and regulations may need to be in place that require set businesses to allocate a certain amount of money to train the employees of non-automated jobs (6). 

In conclusion, precision medicine and artificial intelligence both require information collected from the public in order to keep advancing. As a society, it is our responsibility to keep up to date with what is being collected from us. It is still uncertain how artificial intelligence will impact our world; however, all we know is that in order to keep it from drastically changing our society, we must be aware of what it does and its limitations. 


  1. Hodson R. Precision medicine. Nature [Internet]. 2016 Nov [cited 2021 Feb 16]; 537(S49). Available from:
  1. Reddy S. Artificial intelligence applications in healthcare delivery. Boca Raton FL: Routledge; 2021. 4 p.  
  1. Ginsburg GS, Phillips KA. Precision medicine: from science to value. Health Aff [Internet]. 2018 May [cited 2021 Feb 16];37(5). Available from:
  1. Uddin M, Wang Y, Woodbury-Smith M. Artificial intelligence for precision medicine in neurodevelopmental disorders. Npj Digital Medicine. 2019 November [cited 2021 Mar 8]; 2(112). Available from:
  1. Puntoni S, Reczek RW, Giesler M, Botti S. Consumers and artificial intelligence: an experiential perspective. J Mark [Internet]. 2020 Oct [cited 2021 Feb 16];85(1):131-151. Available from:
  1. Haenlein M, Kaplan A. A brief history of artificial intelligence: on the past, present, and future of artificial intelligence. Calif Manage Rev [Internet]. 2019 July [cited 2021 Feb 16]; 61(4):5-14. Available from:

Reference list for images: 

  1. National Aeronautics and Space Administration. Precision medicine: announcement of the next workshop for NHHPC members. [Image on internet]. 2017 [update 2017 Aug 6; cited 2021 Feb 16]. Available from:
  1. Corporate Finance Institute. Artificial intelligence (AI). [Image on internet]. Available from: