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Beyond Diagnosis: The XAI Era in Healthcare

XAI

Written by Steve Shi on Digilah (Student Tech Research)

My Bio:

I am a 1st year student at Nanyang Technological University (NTU). Through this article, I aim to discuss the integration of Explainable Artificial Intelligence (XAI) in healthcare to address transparency concerns in AI decision-making. Delving into the world of XAI, the ability to explain and interpret the decisions made by AI algorithms could bridge the gap between technological advancements and the human touch in healthcare.

Introduction:

In recent years, the integration of Artificial Intelligence (AI) and Machine Learning (ML) in healthcare has surged rapidly, presenting revolutionary opportunities for improving diagnostics, drug discovery, and patient outcomes. However, the escalating reliance on complex AI systems has raised concerns about transparency, accountability, and the interpretability of decision-making processes.

In response to these challenges, Explainable Artificial Intelligence (XAI) has emerged as a key player in making AI systems more transparent and understandable, particularly in the critical realm of healthcare.

In the healthcare sector, where high level of precision, reliability, and accountability are demanded, traditional AI systems are often viewed as “black boxes,” as the internal workings and processes that lead to a particular output or decision are not easily understandable or interpretable by humans, raising doubts about their decision-making processes.

In healthcare applications where lives are at stake, the lack of transparency in AI decision-making processes can lead to critical implications. This concern is addressed by XAI by providing insights into how AI systems arrive at their decisions, offering a level of transparency that is essential for fostering trust and collaboration between AI and healthcare professionals. In addition, XAI goes a step further by offering transparent explanations for medical diagnoses, unravelling the intricacies of drug discovery processes, and demystifying the decision-making landscape in clinical settings.

Figure 1: Healthcare Professional & AI

Medical Diagnosis:

One of the primary applications of XAI in healthcare is in medical diagnosis. AI systems are increasingly utilised to analyse medical images such as X-rays, CT scans, and MRIs to aid in disease detection.

However, the opacity of these systems raises questions about their accuracy and reliability. XAI steps in by generating explanations for the decisions made by AI algorithms, enabling medical professionals to verify and understand the reasoning behind the proposed diagnoses.

This transparency not only enhances the trustworthiness of the AI system but also facilitates collaboration, leading to more accurate diagnoses and improved patient outcomes.

Figure 2: Overall Pipeline of medical XAI application[1]

Drug Discovery and Development:

The drug discovery and development process are areas where XAI is poised to make a significant impact. AI systems play a crucial role in sifting through vast datasets to identify potential drug candidates for various diseases.

However, the lack of transparency in these systems makes it challenging for researchers to comprehend how a specific drug candidate was identified. XAI addresses this by providing clear explanations for the decisions made by the AI system.

This transparency empowers researchers to understand the underlying mechanisms and rationale for the drug discovery process, ultimately leading to the development of more effective and targeted treatments. The result is a streamlined drug development process that saves time and resources.

Figure 3: Illustration of AI model used in the lab[2]

Improving Patient Outcomes and Reducing Healthcare Costs:

Predicting health risks and optimizing treatment plans are vital aspects of healthcare that benefit from AI systems. XAI plays a crucial role in this context by offering transparency and understanding.

By providing clear explanations for the predictions made by AI systems, clinicians can comprehend the reasoning behind a particular decision or recommendation, enhancing clinical confidence, promote the widespread implementation of AI-based clinical decision support systems, and ultimately result in improved patient outcomes and better healthcare delivery. [3]Healthcare professionals can also make informed decisions and interventions, contributing to early disease detection, preventive measures, and ultimately, improved patient outcomes.

Additionally, the transparency afforded by XAI can contribute to reducing healthcare costs by enabling more efficient resource allocation and strategic decision-making.

Challenges in Implementing XAI in Healthcare:

While the potential benefits of XAI in healthcare are immense, implementation comes with its set of challenges. As AI systems become more complex, finding the right balance between simplicity and accuracy, is essential to ensure that the transparency offered by XAI does not compromise the reliability of AI systems.

Additionally, standardisation and guidelines for implementing XAI in healthcare[4] are also needed to guarantee the transparency and reliability of the AI systems. Furthermore, ensuring that explanations align with human models is an also ongoing area of research.

The Future of XAI in Healthcare:

As the integration of AI in healthcare continues to evolve, the role of XAI becomes increasingly pivotal. Its potential to enhance transparency, trust, and collaboration between humans and AI systems has far-reaching implications for the industry.

Ongoing research and development in XAI will likely address current challenges and contribute to the standardisation of practices in healthcare. The future holds promise for XAI to revolutionise healthcare practices, ensuring that the benefits of AI are harnessed responsibly and ethically for the betterment of patient care.

Conclusion:

Explainable Artificial Intelligence is on the verge of transforming the landscape of healthcare. From enhancing the interpretability of medical diagnoses and drug discovery processes to improving patient outcomes and addressing ethical concerns, XAI stands as a beacon of transparency in the realm of AI applications.

The integration of XAI in healthcare promises a future where AI systems work seamlessly with healthcare professionals, leading to more accurate diagnoses, efficient drug development, and ultimately, improved healthcare for all. As the journey of XAI in healthcare progresses, it is essential to continue refining its implementation, ensuring that transparency and accountability remain at the forefront of technological advancements in the pursuit of a healthier future.

References:

Göllner, S. (2022, August 11). State of the art of Explainable AI in Healthcare in 2022. Medium.

State of the art of Explainable AI in Healthcare in 2022 | by Sabrina Göllner | Medium

Zitnik, M. (n.d.). Research directions. Zitnik Lab.

https://zitniklab.hms.harvard.edu/research

Giuste, F. et al. Explainable artificial intelligence methods in combating pandemics: A systematic review. In IEEE Rev. Biomed. Eng. 16, 5–21. 

https://doi.org/10.1109/RBME.2022.3185953 (2022).

Jin, W., Li, X., Fatehi, M., & Hamarneh, G. (2023). Guidelines and evaluation of clinical explainable AI in medical image analysis. Medical Image Analysis, 84, 102684.

https://doi.org/10.1016/j.media.2022.102684

Most asked questions

What is the role of XAI in medical diagnosis?

XAI steps in by generating explanations for the decisions made by AI algorithms, enabling medical professionals to verify and understand the reasoning behind the proposed diagnoses.

What are the key benefits of integrating XAI into healthcare practices?

Integrating XAI fosters trust and collaboration, leading to accurate diagnoses, streamlined drug development and better patient outcomes.

Most searched queries

Magnetic Resonance Imaging (MRI)

Computed Tomography (CT) scan

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Med Res

Precision Revolution: CRISPR, AI and the Future of Biotechnology and Pharmaceuticals

drug

Written by : Oswald Yap Tingzhe on Digilah (Student Research)

My Bio

I am a 2nd year student at Nanyang Technological University. The inspiration for this research article is the rapid and advanced development of AI and CRISPR technology in the medical field. AI’s rapid data analysis transforms drug development, while CRISPR precision revolutionizes genetic modification. Together, these two incredible technologies result in groundbreaking developments, shaping the future of healthcare with innovative treatments and personalized medicine.

Read my article below:

Section 1: CRISPR Technology

CRISPR technology is derived from bacterial immune systems to facilitate precise gene modification. It is made up of short repetitive DNA sequences that contain “spacer” sequences, which contain viral genetic information.

By utilizing Cas9 enzyme as molecular scissors, RNA molecules can guide CRISPR to target and edits specific genes precisely. With this capability, scientists can introduce new genes or modify existing sequences accurately.

CRISPR has a profound impact on treating genetic disease, because it can modify faulty genes that are responsible for these hereditary conditions. Therefore, CRISPR can treat diseases that cannot be treated through therapeutic interventions.

This makes CRISPR a revolutionary tool in the pursuit of precise and effective treatments for many genetic diseases. This is due to its precision, versatility, and transformative impact.

A group of scientists from China has demonstrated that CRISPR can eliminate or inactivate carcinogenic viral infection (cancers are caused by gene mutations). They have proven that CRISPR can treat cancer when it is applied to human viruses, such as hepatitis B virus and HPV16.

For example, HPV16 and HPV18 viruses can induce cervical cancers by Papillomavirus E6 and E7 viral proteins. Bacterial CRISPR/Cas RNA guided endonuclease can be reprogrammed to HPV-transformed cells to knockout (delete) E6 and E7 genes.

One of the clinical challenges faced is the off-target effects even though CRISPR can edit and modify gene precisely. Many researchers have reported that the CRISPR-Cas9 technology cause gene modification in other undesired genomic loci. As a result, this will reduce the efficacy of gene modification.

To reduce the off-target effects of CRISPR-Cas9, a scientist from Harvard university has modified the Cas9 protein to enhance the recognition of target DNA. Hence, it can improve the on-target specificity and efficiency of CRISPR-Cas9 technology.

Section 2: AI accelerates drug development

The advanced algorithms of artificial intelligence (AI) can revolutionize the development of drug by analyzing extensive dataset with speed and accuracy. 

These algorithms can identify potential drug candidates more efficiently than traditional methods. Due to its ability to identify intricate pattern and relationships within the extensive and diverse data, it can lead to more informed decision-making.

The complex algorithm in AI can also reduce the time and resources required for early-stage research. Hence, this innovative application of AI marks a paradigm shift, creating the hope of streamlining drug discovery to bring novel and effective treatments to patients more swiftly.

AI has emerged as a possible solution to the problems caused by chemical space of atoms in the pharmaceutical industry. 

The AI algorithms have been increased in computer-aided drug design (CADD) due to the development of technologies and high-performance computer.

The two most common methods of CADD are structure-based drug design and ligand-based drug design. The structure-based drug design analyzes the three-dimensional of proteins, while the ligand-based CADD uses the information of studied active and inactive molecules.

Machine learning computational algorithms, such as support vector machine (SVM), has ensured to improve the activity of bioactive components. 

The combined methods of both deep-learning and machine-learning has increased the ability, strength, and standard of the evolved products.

In the field of orthopedics, the large amount of data with the inclusion of ML has helped orthopedic surgeons in many aspects of the application. 

For example, the advances in this field to assess the impact on the musculoskeletal system of human beings. This is done to provide value-based healthcare and serving the patients in a better manner.

Section 3: Synergy of CRISPR and AI

The integration of CRISPR and AI has led to a new era of unprecedented advancements in the development of drug discovery. 

As a result, many critical challenges can be addressed. There will be more novel solutions and the pace of scientific breakthroughs will be accelerated.

The synergy of CRISPR and AI potential drug targets to be identified rapidly in the process of drug discovery and the assessment of their therapeutic viability. 

It would be impossible for human researchers to decipher the massive genomic information. AI algorithms make it possible due to its ability to analyze vast data sets, identifying patterns and relationships.

Furthermore, AI has an important role in optimizing the process of predicting the outcomes of genetic editing in CRISPR experiments. 

This is because the algorithms can anticipate the effects of specific gene edits after studying the previous CRISPR data. In this way, it can learn from past experiments.

The ability of AI to predict results in experiments not only speeds up the experiment, but also reduce risks to increase accuracy. Therefore, the synergy of CRISPR and AI can revolutionize the landscape of biotechnology.

For example, machine-learning (ML) models are trained using existing datasets and can be used to predict the on/off-target effects of the testing datasets (genomic information). 

The current ML models are based on regression-based methods, classification-based methods, and ensemble-based methods.

The advanced ML models enable deep-learning (DL) methods to be applied in the CRISPR-Cas9 system. The models in CIRSPR-Cas9 system consists of multiple layers of interconnected compute units.

The algorithm takes the encoded gRNA-DNA sequence in length 23 in the matrix as input. The convolution layer applies various filters of different sizes to the input matrix.

The next layer performs batch normalization to the output of convolution layer to boost learning and prevent over-fitting.

The last layer (pooling layer) further filters the normalized data from the previous layer. The output of this layer is then passed through multiple layers of deep learning neural network.

The last layer of this network passes the result to the stop layer that will predict whether the input is off-target or on-target.

Conclusion

In conclusion, the combination of CRISPR and AI has led to a revolutionary era in biotechnology. The coupling of the precision of CRISPR in genetic modification and AI in drug development has resulted in a groundbreaking development in drug discovery.

This showcases the transformative potential of this dynamic collaboration.

References:

https://sci-hub.se/https://doi.org/10.1093/bfgp/elaa001

https://link.springer.com/article/10.1007/s11030-021-10217-3

https://www.sciencedirect.com/topics/pharmacology-toxicology-and-pharmaceutical-science/computer-aided-drug-design

https://link.springer.com/article/10.1186/s12967-022-03765-1#Sec14

https://www.sciencedirect.com/science/article/pii/B9780323911726000200?ref=pdf_download&fr=RR-2&rr=8464441bdb385ffa

Most asked questions

Can AI be more efficient than traditional methods in predicting diseases?

How AI can be used to decipher DNA?

Most searched queries

Machine learning

Genome

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