Hello everyone! Welcome back to another one of my blogs. Today, I’ll be talking to you about how AI is being in used in my major, Biology. Let’s start!

Where and How AI is Currently Being Used:

I think that there are a lot of potential uses for AI in the biological field. For example, one study was experimenting with using AI to detect breast cancer in mammography scans. In addition, the study included more than 460,000 women. The authors concluded that the AI had a high breast-model cancer detection rate, which means that it was able to successfully detect breast cancer from the mammography scans. This is important because this means that in the future, AI will most likely have more implementations in the field of radiology, where it will be used to analyze various scans, such as MRI or X-ray. Link to the study mentioned: Link.

This is not the only way AI can be used in Biology. Another study was using AI to create various drug combinations against pancreatic cancer. The authors state that the AI sifted through 1.6 million combinations. Moreover, the authors claim that the AI was able to form synergistic and experimentally successful combinations with a high-rate of precision. This is important beause it shows how AI can be used in the future to create personalized treatment programs, where it analyzes the patient’s conditions to come up with a treatment plan with different drug combinations. Link to study: Link.

This is what I think, let me know what you think the future of AI in biology will look like. For this next section, I’ll be asking Perplexity AI the question “What are ways AI will be used in the field of biology?”.

Perplexity AI’s Response:

Key Ways AI Is Used in Biology

1. Drug Discovery and Development

• AI accelerates the identification of potential drug candidates by analyzing vast datasets of molecular properties and predicting how molecules will interact with biological targets, significantly reducing the time and cost of drug development

• Machine learning and deep learning models help prioritize genes or proteins crucial in disease pathways, aiding in the discovery of novel therapeutic targets

2. Protein Structure Prediction

• Tools like AlphaFold use AI to predict the 3D structures of proteins from their amino acid sequences, enabling breakthroughs in understanding protein function and aiding drug design

3. Genomic and Personalized Medicine

• AI analyzes large-scale genomic datasets to identify genetic predispositions to diseases, predict treatment responses, and support personalized healthcare recommendations

• Generative AI models can design new genetic sequences with specific properties, advancing personalized medicine and synthetic biology

4. Disease Diagnosis and Clinical Diagnostics

• AI algorithms process medical images and genetic data to assist in the early detection and diagnosis of diseases such as cancer and dementia, often with accuracy rivaling or surpassing human experts

• AI-based biomarkers help predict patient responses to treatment and survival outcomes

5. Gene Editing and Synthetic Biology

• AI optimizes gene-editing tools like CRISPR by predicting the outcomes of edits and designing guide RNAs for precision and efficiency

• AI models can design and synthesize DNA sequences for novel biological systems or therapies

6. Agriculture and Industrial Biotechnology

• AI-driven approaches improve crop yields, reduce waste, and streamline agricultural processes by analyzing plant and soil data

• AI optimizes microbial metabolic pathways for bio-based industries, increasing the efficiency and yield of industrial strains

7. Multi-Omics Data Integration

• AI helps integrate and analyze diverse biological data types (genomics, transcriptomics, proteomics, etc.), revealing hidden patterns and improving our understanding of complex biological systems

8. Imaging and Image Analysis

• AI automates and enhances the analysis of biological images, such as those from microscopy or radiography, enabling faster and more accurate research

AI is rapidly transforming biology by automating workflows, accelerating discovery, and enabling new ways to understand and engineer life

Link to chat: Link.

Interesting answers. However, let’s keep this in mind till the reflection section. Now, we’ll move onto the next section, where I provide you guys with a summary of an interview I made with an AUC professor from the Biology Department!

Interview with AUC Professor:

For this part, I interviewed Dr. Rehab Abdallah, who is a microbiology professor at AUC and MUST. In addition, she has a lot of experience in bioinformatics and microbial ecology. She is currently my thesis advisor! We both have a passion for environmental microbiology and bacteria ^_^.

When I asked Dr. Rehab about the uses of AI in the academic field. She said that she does not like to use AI in academics. However, she mentioned that AI is a tool that can have its benefits. For instance, Dr. Rehab told me that she had been using AI to generate questions for her slides. She mentioned that doing so helps her generate a variety of questions that help stimulate brain storming. However, she told me that AI-generated questions require revision, because AI can sometimes make questions that are either too easy or too difficult. Therefore, AI can have some benefits in academics.

When I asked Dr. Rehab about the use of AI in the professional field, Dr. Rehab mentioned that AI is evolving at rapid speeds in the field of biology, and particularly in the fields of microbiology and bioinformatics. Moreover, she told me that AI is facilitating research for microbiologists. For instance, she explained that microbiologists are required to deal with many unknown microbes that require the use of many software and programs in order to reach a phylogenetic tree, which is a tree of life that shows the evolutionary relationships of a species. She added that today, many researchers are creating their own pipelines, with the help of AI, to identify undiscovered and unknown microbes, where the AI examines different combinations of phylogenetic trees to find the most accurate one. This is important, she added, because using AI for the purposes of genetic identification of microbes makes the research work-load less time-intensive for microbiologists.

Second Interview:

For this interview, I interviewed a professional in the field of medicine, Dr. Hesham Abdelaziz, a professor of Dermatology at Damanhour Teaching Hospital and a member of both the American and European Academies of Dermatology. He is also a practicing dermatologist. He told me that the advances in AI offer a lot of promising potential for the field of dermatology. For example, he explained to me how clinical dermatology is a difficult profession that requires a lot of experience, because the dermatologist relies most of the time on a visual diagnosis of the clinical picture, the manifestations of the disease, to form a differential diagnosis – a list of probable diagnoses. He told me that he met with a group of researchers working on training AI using images of different dermatological diseases and their clinical pictures. The goal was to use the AI to help identify the disease present in the patient using its algorithm and datasets. This is important, Dr. Hesham explained, because it would make visual diagnoses easier and would help the doctor narrow down on the disease present. Moreover, he added, that it would be specifically beneficial for inexperienced dermatologists who are just starting the practice, and who do not posses a lot of experience. He stated that those inexperienced dermatologists find it difficult to diagnose patients, because they do not have a lot of practical experience that would help them diagnose patients – after all the clinical picture that doctors see in real does not necessarily match the one in the books. Therefore, having an AI tool that can narrow down their differential diagnosis would be helpful and pivotal to their practice. Overall, Dr. Hesham concluded that he is optimistic about the implementation of AI in the medical field, and he adds that it will help doctors save more lives.

Reflection:

Interesting answers from both Dr. Rehab and Dr. Hesham! I definitely found their statements exciting and promising. For example, Dr. Hesham mentioned that AI can be used to aid dermatologists in the future. This is important because, as Dr. Hesham highlighted, the help from AI would not only allow dermatologists to narrow down their diagnoses, but also it would allow them to save more lives and treat more patients. However, does that mean that in the future, AI will replace doctors? I think that I would find that thought scary – imagine being treated by a machine! Still, I do not think that this will ever happen, because medicine as a practice is something that requires years of experience to perfect, and, thus, I do not think any machine or AI will be able to replace human dexterity. Therefore, medical doctors will be able to use AI as a tool to aid in their diagnosis of disease.

I also think that Dr. Rehab’s informative answers were interesting. Since Dr. Rehab’s talk about AI touches upon the field of microbiology, I think I should keep track of what she said, because my thesis work is on bacteria. This is important because had I implemented AI during when I was identifying my bacterial samples, I would have saved a lot of time. Also, I’m interested in the medical field, as I am studying biology for the pre-medical track, and, thus, I should also stay updated on AI in the fields of medicine. Moreover, both Dr. Rehab and Dr. Hesham told me about things that I did not know previously. Therefore, I think that I should look more into the current prospects of AI in both the fields of microbiology and medicine.

That’s it for today’s blog. I hope you enjoyed it, and let me know what you think in the comments below. See you in the next blog!

 Header image credit: Link.