THE NATIONAL ARTIFICIAL INTELLIGENCE ROADMAP, also known as AI-Rmap, was released during the Covid-19 pandemic in 2021. It came at a time when millions of companies worldwide were pushed to re-strategise in response to unprecedented challenges, accelerating digital transformation and technology adoption throughout the country. As such, the AI-Rmap seemed to fit with the timeline and the times.

In mid-2023, Science, Technology and Innovation Minister Chang Lih Kang explained that the government was working on a regulatory framework “as a guide in developing the artificial intelligence ecosystem in the country” while aiming to create 10,000 positions in the country and growing the economy by 30%. Most recently, in Budget 2024, Prime Minister Anwar Ibrahim announced the establishment of the country’s first artificial intelligence study centre, the Faculty of Artificial Intelligence (AI) at Universiti Teknologi Malaysia (UTM), with an initial allocation of RM20mil.

While these leaders navigate AI’s promotion and regulation, I spoke to a few people from three different industries to see how their institutions stay adaptable in a rapidly evolving technology landscape.

AI in Medicine

Dr. Annamalar Muthu Interventional Cardiologist, Gleneagles Hospital Penang.

1. Tell us how Gleneagles has incorporated AI into various treatments? 

Gleneagles has been actively incorporating AI and advanced technology into our medical services to improve patient care and operational efficiency. AI initiatives are evolving, and we are also evolving to adapt AI into our practices. It is a work in progress. AI systems are often used to assist medical professionals in the diagnostic process. It can analyse medical images such as echocardiography images to detect abnormalities and provide more accurate and faster diagnoses.

2. What specific areas of patient treatment and care benefit most from AI, and what are the key advantages of using AI in these contexts?

AI has made significant inroads, and the medical areas that benefit include medical imaging, drug discovery and development, personalised medical care, electronic health record processing, telemedicine and virtual health assistants. AI is transforming healthcare in various ways, improving the accuracy, speed and efficiency of patient treatment and care.

These advancements can lead to earlier diagnoses, more personalised treatment plans, better patient outcomes, and a more streamlined healthcare system. We have not fully incorporated AI into our daily practice. However, it is essential to balance these advantages with considerations regarding data privacy, the ethical use of AI, and the need for human oversight in critical healthcare decisions.

3. How do you envision the future of AI in patient care, and what potential advancements or breakthroughs do you anticipate in the coming years?

The future of AI in patient care holds great promise, with numerous potential advancements and breakthroughs anticipated in the coming years. AI can analyse vast amounts of patient data, including genetic information, medical history and real-time health data, leading to more effective and tailored care for individuals.

Trained AI algorithms can identify subtle patterns and anomalies in medical imaging, detecting diseases like cancer, which can significantly improve patient outcomes. Wearable devices and sensors connected to AI systems can continuously monitor patients’ health and provide real-time feedback to healthcare providers. This is especially valuable for chronic disease management and elderly care.

The future of AI in patient care is likely to involve close collaboration between AI systems and healthcare professionals. Therefore, it is important to proceed with caution. Ethical, regulatory and security concerns must be addressed to ensure that the benefits of AI in healthcare are realised without compromising patient safety.

AI in Higher Education

Andrew Tan Kian Lam Head of DiGiT, WOU.

1. Ever since the advent of ChatGPT, there has been a lot of worry about students handing in AI-generated essays to the point where an application is created to detect AI-written essays. Where does WOU stand on this issue? 

Some higher education institutions globally have considered prohibiting the use of ChatGPT and other AI tools. At Wawasan Open University (WOU), we use AI detection tools like Turnitin to detect AI-written essays.

I think it is time to shift from traditional assessment methods to more comprehensive ones. For example, incorporating elements of in-person evaluation, encompassing written and oral, or both. Within the School of Digital Technology (DiGiT), we mandate that students include a short presentation as part of their final assessment, where they elucidate how they completed their projects and address inquiries from the lecturer and fellow students.

Other times, students critique the provided questions and answers, explaining their assessments and giving reasons behind their judgments.

2. In your opinion, how can higher education institutes reap the benefits of AI securely and safely?

Prohibiting and discouraging AI use for most assessments is counterproductive. AI technology can enhance productivity across all sectors, exemplified by the pioneer of modern computer science, Alan Turing, and his enigma code decryption. Without the assistance of computers, it would have taken him much longer to accomplish it. Clearly, restriction is not the solution; education on effective AI use is.

Failure to integrate AI technology into your workspace risks rapid obsolescence from the industry. For example, AI can help rapidly create digital messaging content with only minor inaccuracies. The draft can then be corrected before dissemination.

Rather than resisting its use, we should focus on responsible integration, education and continuous improvement. We can then adapt to the evolving landscape and ensure that AI serves as a valuable tool for the betterment of society.

3. How is WOU currently incorporating AI and machine learning into its academic programmes, if at all?

DiGiT has integrated AI and machine learning components into its Bachelor in Software Engineering programme. These elective courses include Data and AI Essentials, Statistics for Data Science and AI, R Programming, Machine Learning, Deep Learning and Reinforcement Learning.

Students pursuing a Bachelor of Digital Business also need to take Data and AI Essentials to graduate.

We are also excited to announce the launch of a new Master’s programme, Master of Data Science, set to be introduced next year. It offers a comprehensive curriculum that delves deeply into AI and machine learning. It equips students with advanced skills in data analysis and the ability to effectively manage and communicate complex data insights.

As technology advances, our educational methods must adapt. The key lies in striking a balance between leveraging AI tools for educational benefits and maintaining the integrity and authenticity of the learning experience.

AI in Fisheries

Dr. Alex Tilley Penang-based Senior Scientist, WorldFish.

1. Is AI currently used in fisheries and aquaculture in Malaysia or Penang specifically? If yes, how? If not, why?

The utilisation of AI in fisheries and aquaculture is a promising avenue to combat overfishing, ensure environmental sustainability, and meet the rising demand for seafood. AI has been applied in these sectors for several years now. In aquaculture facilities, AI can help monitor water quality, manage disease outbreaks and optimise feeding schedules.

In fisheries, industrial fleets use AI-driven models to improve the detection of fish schools based on various oceanographic factors to reduce the costs of searching and to make fishing operations more efficient. However, I believe such AI applications are not embraced widely in Malaysia, yet.

The emerging field of cybernetics offers a substantial promise for fisheries, as it allows new information to be continually fed back into models to refine predictions. The “intelligence” or machine learning of AI comes from identifying patterns in huge amounts of data. For Malaysia to harness this potential, the processes and infrastructure to collect bid data in real-time data will be necessary investments. Thankfully, low-cost, open-source data collection, even for small-scale fisheries, has become achievable, as demonstrated by the canoe-based fisheries of Timor-Leste (www.timor.peskas.org).

2. What are the primary challenges or issues in the fisheries and aquaculture industry that you think AI can aid in, and can you project how it can help with food security?

By leveraging AI, fisheries and aquaculture stakeholders can drastically accelerate the process of turning data into information for decision-making. Alarming patterns can be identified quickly, as can the response to them, thereby improving the chances of avoiding harmful effects on ecosystems and food production sectors based on natural resources. In Penang, for example, we can use high-resolution vessel tracking and catch data to model the impact of land reclamation projects on small-scale fisheries’ livelihoods and local food security in the future.

In combating overfishing and resource depletion, AI can analyse historical and real-time catch data and satellite imagery to predict fish stock fluctuations and suggest sustainable fishing levels. Additionally, AI can integrate climate information to suggest adaptations to fisheries and aquaculture approaches, given certain scenarios, thereby improving the resilience of farmers and fishers to changing local conditions.

Furthermore, AI can streamline seafood supply chains, reducing food waste and loss during processing and transportation, and ensuring that fresher, safer products reach consumers.

3. Does WorldFish have a vision that constitutes a future of AI in fisheries and aquaculture? What potential innovations or advancements are expected to emerge in this sector?

At WorldFish, we recognise AI’s pivotal role in shaping fisheries and aquaculture’s future. Our work entails developing AI-driven decision support systems for small-scale fishers, producing ontologies to improve AI structuring of public aquatic data, deploying AI-powered aquaculture management tools, and integrating AI into sustainable aquaculture practices. These advancements aim to provide fishers, farmers and managers with information that leads to the increased well-being of people and aquatic ecosystems.