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The Star
2 days ago
- Health
- The Star
Here's the AI diagnosis: How the tech is being leveraged in M'sia's public healthcare system
A man in his 60s showed up for a routine health screening at a private hospital in Klang Valley last year. Despite being a smoker, he wasn't displaying any symptoms that might have indicated a health issue. On that day, he also underwent a chest X-ray augmented by artificial intelligence (AI) software, which was designed to detect subtle abnormalities that the human eye may sometimes overlook. Prof Anand Sachithanandan, a consultant cardiothoracic surgeon, recalls how the software detected a small shadow in the upper zone of the man's left lung. 'It was something that would most likely be easily missed or overlooked by conventional X-ray imaging,' he says in a statement to LifestyleTech. Prof Anand adds that further investigations revealed an elevated tumour marker, which could be a sign of cancer in the body. While this may sound alarming, additional tests are needed to confirm the diagnosis. 'He was swiftly investigated with CT thorax scan, PET scan and biopsy – confirming an early-stage primary lung cancer,' he says. Prof Anand adds that they performed a surgery to resect (remove) the tumour. Three days later, the man recovered well and went home. AI has the potential to sift through voluminous data very quickly and will pave the way for precision diagnostics and personalised treatments in the future. — Prof Anand Sachithanandan It was the first known case of AI helping to detect lung cancer in Malaysia, where Prof Anand says the technology involved cloud-based software powered by deep learning algorithms. 'The case demonstrated the potential to screen for lung cancer with a chest X-ray and how the technology can aid radiologists. 'It also highlighted the quick turnaround time which contributed in part to the patient completing definitive treatment in less than two weeks,' he adds. AI and early detection Malaysia began incorporating AI screening as early as 2020, when private healthcare centres launched initiatives to use AI for detection of retinal diseases. The adoption of AI is now broadening to public healthcare with the Health Ministry driving initiatives to use AI to detect diseases. Last year, the Health Ministry announced the roll-out of Dr Mata, an AI-driven software solution to detect and diagnose diabetic retinopathy, an eye disease caused by diabetes, in a pilot project involving around 140 government clinics. According to Health Ministry deputy director-general for research and technical support Datuk Dr Nor Fariza Ngah in a report, the technology can help to produce eye test results at a faster rate and would lead to better outcomes for patients. Then in May, the Health Ministry announced that an AI-powered lung cancer screening initiative will be rolled out at seven health clinics nationwide – including in Kelantan, Pahang, and Kedah – starting this year. 'With this AI capability, the incident detection rate is significantly improved,' Bernama quoted Health Minister Datuk Seri Dr Dzulkefly Ahmad as saying during a National Lung Health Initiative 2025-2030 briefing in May. Based on the latest available National Cancer Registry Report (2017-2021), Prof Anand says lung cancer remains one of the leading cancers in Malaysia, with reported cases on the rise: 'It now makes up 10% of all cancer cases, ranks as the second most common cancer among men, and is the third most common among women.' He adds that the Health Ministry's initiative to prioritise lung cancer screening is timely, as identifying the disease at an earlier stage offers a significantly better chance of successful treatment. 'As the Health Ministry remains the largest healthcare service provider, adoption of AI-enabling screening in government clinics has the potential to create a significant impact in terms of meaningful stage shift to detect more cases at an early-stage when the cancer is more amenable to curative-intent treatments like surgery,' he adds. While AI can be scaled and deployed in rural areas to assist clinic staff without a radiologist or doctor on-site, Prof Anand points out that strong communication networks are essential for it to work effectively. — Image by freepik Prof Yeong Chai Hong from the School of Medicine, Faculty of Health & Medical Sciences, Taylor's University, says AI-driven diagnostics screening at government clinics reflects the country's agenda of moving towards preventive data-driven healthcare. 'The efforts can also be seen as an attempt to demonstrate Malaysia's commitment towards bridging the healthcare gap between urban and rural communities through scalable, data-driven solutions. 'It signals a shift from reactive to proactive care, where early detection can lead to timely interventions, improved outcomes, and reduced burden on tertiary care facilities,' Prof Yeong says in a statement to LifestyleTech. Prof Anand says AI is ubiquitous and its impact is now visible in all sectors of people's personal and professional lives. 'The government has been swift to recognise its potential and been proactive in the early adoption of various innovative AI-driven programmes,' he adds. He also believes social media has helped to normalise the broad concept of AI that 'it seems less daunting and more acceptable' to most people. He adds there is also a growing body of scientific evidence pointing towards AI improving diagnostics accuracy and enhancing the delivery of precision and personalised medical care: 'Collectively, this may have contributed to the wider acceptance,' he says. Dealing with data Despite its potential, the use of AI in disease detection still comes with some limitations that need to be addressed, says Prof Yeong. AI should remain a support tool, while clinicians must continue to play the central role in decision-making. — Prof Yeong Chai Hong 'One major concern is algorithmic bias. Many AI models are trained on datasets from specific populations, which may not generalise well to Malaysia's diverse demographic landscape without proper local validation,' she explains. She also warns of the risk of over-reliance on AI outputs, which could compromise clinical judgment if not carefully managed. 'AI should remain a support tool, while clinicians must continue to play the central role in decision-making. This ensures patient safety and maintains professional accountability,' she says. Another key challenge lies in data governance. Prof Yeong stresses that the safe, ethical, and transparent use of patient data requires strong regulatory frameworks to protect privacy, ensure security, and build public trust in AI-powered healthcare systems. To ensure responsible and ethical use of AI in the public health sector, she calls for a multi-faceted approach underpinned by clear regulatory oversight and well-defined standards. 'Authorities such as the Health Ministry should establish national guidelines to govern the validation, certification, and post-deployment monitoring of AI tools in clinical settings,' she says. She adds that robust measures are needed to protect data privacy and promote transparency. 'This includes building secure digital infrastructures and ensuring that AI models are developed, trained, and tested with full transparency especially regarding the origin and representativeness of the datasets used.' She also highlights the need for continuous monitoring. 'AI tools must be regularly assessed and updated to ensure they remain accurate in real-world conditions, are properly calibrated, and do not produce unequal outcomes across different patient groups especially in today's rapidly evolving healthcare landscape.' While AI can be scaled and deployed in rural areas to assist clinic staff without a radiologist or doctor on-site, Prof Anand points out that strong communication networks are essential for it to work effectively. 'The cost of installing the software, along with maintaining and upgrading server networks – such as 5G infrastructure – can be a limiting factor,' he adds. Dzulkefly has said in the Bernama report that the cost of deploying AI lung screening at the selected government clinics is relatively modest – just RM70,000 – and worth the investment for the benefits of early disease detection. 'Cautiously hopeful' As the founding president of the non-governmental organisation Lung Cancer Network Malaysia (LCNM), Prof Anand has been a key advocate for the use of AI in lung cancer screening. In 2021, LCNM launched a free screening initiative using AI-enhanced chest X-rays at health clinics across the Klang Valley, reaching over 10,000 participants. 'I am excited and cautiously hopeful with the more widespread adoption of AI screening,' he shares. Due to high disease burden and poor outcomes due to most cases being detected at the late stage, he says the time is now ripe for a national level lung screening programme. He adds that other countries like the United Kingdom initiated a national level lung screening programme back in 2019, while Australia is about to start their initiative in July. Ultimately, Prof Anand explains that screening is not a one-off test but part of a longer process that involves follow-up scans, biopsies, and treatment – requiring proper funding and workforce planning. 'There must be a well coordinated pathway for anyone with an abnormal finding on an AI-enabled chest X-ray to be followed up and quickly further investigated,' he adds. For AI tools to be integrated effectively into public healthcare, Prof Yeong says digital infrastructure at hospitals, clinics and screening centres must be strengthened. 'This includes expanding electronic health record (EHR) systems, securing medical data storage and improving network connectivity, particularly in rural and underserved areas where digital access may be limited,' she adds. There is also a need to provide healthcare professionals with digital literacy training and essential knowledge in data science. She also says there should be a structured pathway to expand and scale up successful pilot projects into 'sustainable, nationwide AI-powered programmes'. Beyond health screenings The potential for AI-driven solutions in Malaysia's public sector is vast, according to Prof Yeong. Beyond screening, she sees AI playing a significant role in patient management with tools like AskCPG. 'It can help streamline the implementation of national Clinical Practice Guidelines (CPGs) and optimise healthcare workflows by providing real-time recommendations based on patient data,' she adds. For hospital operations, she says that AI can also help to optimise administrative workflows by predicting patient volumes, managing bed occupancy and even automating appointment scheduling. 'Beyond clinical care, AI is increasingly used in drug discovery and clinical trials. It can optimise drug design tailored to specific populations or individuals, accelerate the identification of new drug candidates, and match patients to appropriate clinical trials based on eligibility criteria,' she adds. Prof Anand also sees the potential for AI to shape the future of health screenings in Malaysia. 'AI has the potential to sift through voluminous data very quickly and will pave the way for precision diagnostics and personalised treatments in the future,' he says, adding that there's the possibility of AI predicting and spotting early warning signs on a chest X-ray or CT scan – even before a tumour fully forms – allowing doctors to closely monitor high-risk patients and take action much earlier. While early intervention can significantly improve a patient's chances of survival, recovery also depends on the individual's own commitment to their health. Prof Anand says the man in his 60s who was diagnosed with lung cancer – through the AI-assisted screening last year – still requires regular follow-ups for the next three to five years. 'He completed his post-surgery adjuvant chemotherapy (as per international protocol) and thankfully remains well and cancer-free. His previously elevated tumour marker levels are now normalising. He has also quit smoking!'
The Star
16-06-2025
- Business
- The Star
AI takes on food waste
You have your safety belt on. About an hour into the flight, hunger kicks in. You reach for the inflight menu, and a pack of nasi lemak catches your eye. Maybe it's the sambal, or it's the idea of enjoying a familiar local favourite at 30,000 feet in the air. Either way, it sounds perfect. You make a request. Then the flight attendant breaks your heart: 'Sorry, we've run out.' Such a situation is not unfamiliar to most airline passengers. When it comes to inflight meal planning, Catherine Goh – the CEO behind Santan, official inflight caterer for AirAsia – explains that there are a lot of factors to consider. Teams behind inflight meal planning relied on experience, historical data and rough estimates to not only decide how much nasi lemak to prepare but also estimate how many passengers would likely choose vegetarian options or prefer to have coffee instead of tea. 'There was a lot of guesswork around passenger preferences, last-minute cancellations, and unexpected bookings. This approach often led to challenges such as overstocking – resulting in waste – or understocking, which left some passengers disappointed,' Goh says in an interview with LifestyleTech . She adds that flight delays or changes also added more complexity to an already delicate balancing act. 'On top of that, we had to consider the stock required for every single flight, accounting for different routes, turnaround times, and return legs – all of which influence the catering load,' she says. According to Goh, Santan is currently testing an AI-powered demand planning tool built on a robust datasheet consisting of historical flight-level sales and loading data – capturing both pre-Covid trends and recent post-Covid recovery behaviours. — SANTAN With over 1,200 flights daily in five regions, Goh says managing inflight food waste is one of their biggest challenges. According to cabin waste audits commissioned by the International Air Transport Association (IATA) published in May, 34% of waste generated on flights comes from untouched food and beverages. The sector is estimated to be incinerating resources worth US$6bil (RM24.40bil) annually. To address this, airlines and catering providers are being urged to improve planning and logistics to reduce cabin waste and contribute to the Sustainable Development Goal of halving global food waste by 2030. Goh concedes that forecasting demand based on static or generic data is simply not sustainable. 'That's what drove us to explore AI-powered demand planning. Unlike traditional methods, AI allows us to factor in a wide range of dynamic variables – such as passenger demographics, travel and booking patterns, historical food preferences by route, meal-time segments, and even cultural events like festivities or the fasting month,' she adds. Connecting the dots with data According to Goh, Santan is currently testing an AI-powered demand planning tool built on a robust datasheet consisting of historical flight-level sales and loading data – capturing both pre-Covid trends and recent post-Covid recovery behaviours. 'Specifically, it has been trained using over three years of historical data, enhanced with the latest six months of operational insights and up to 12 months of forward-looking pre-booking forecasts,' she says, adding that it gives the model more historical depth and real-time relevance. Goh shares that there is a broad spectrum of variables to be analysed including passenger numbers, routes and seasonability trends, nationalities, meal time segments and flight departure times. 'For instance, it can identify how meal preferences shift not just by destination, but also based on time of day or passenger mix – insights that are nearly impossible to act on through traditional planning,' she adds. By looking at the system's recommendations for menu mixes, Goh says it has led to the company being able to offer meals that better match their passengers' expectations during peak travel times. Even with changes like flight delays or cancellations or a spike in last-minute bookings, Goh says the AI can quickly recalculate expected demand. Airlines and hotels are increasingly turning to artificial intelligence to better predict meal consumption patterns. Could this be the key to tackling food waste? — Image by freepik 'It is designed with real-world operational flexibility. This agility allows our supply chain and cabin crew teams to make timely adjustments – whether it's modifying loading volumes or ensuring we reserve popular items that are likely to be in demand,' she adds. Since the system was implemented, Goh says it has been 'showing promising results', with forecast accuracy improving to over 95%. This has led to a noticeable drop in both overstocking and understocking of inflight food items. 'Inflight food waste has dropped by 20% over the past year – a clear win for both efficiency and operational performance. We've also seen better alignment between forecasted and actual demand, enabling more informed decision-making across our supply chain,' she says. The AI system was developed in-house using the airline's central data infrastructure. Goh says all data is encrypted and access is strictly governed through its group-level data governance framework to ensure compliance and protection across all touchpoints. 'Developing the tool internally has also allowed us to fine-tune the system closely to operational realities. It has already delivered encouraging outcomes in live environments, and we're now preparing to scale it across the fleet to unlock greater precision and efficiency in our meal planning processes,' she adds. Dining with data Turning data into actionable insights that could translate to better ways to manage food waste isn't new. Back in 2020, Etihad Airways announced that it was partnering with Singapore food tech startup Lumitics to trial the use of computer vision and machine learning to track uneaten economy class meals. The goal was to highlight food consumption and wastage patterns across the network. With the integration of AI into broader systems, its capabilities have steadily advanced. A 2024 review published in the peer-reviewed journal Food Chemistry: X highlights how AI-powered tools such as deep learning and advanced robotics can greatly enhance food safety, improve quality, and boost efficiency throughout the supply chain. It highlighted the potential of AI to enhance food waste management through 'predictive algorithms' that could help to minimise overproduction and spoilage. In Malaysia, Hilton Hotels announced that it adopted Winnow's AI-powered solution to help reduce wastage during Ramadan. — Hilton Hotels An example cited in the article is the system by Winnow Solutions, featuring smart scales and image recognition technology to help kitchens reduce waste by pointing towards the source and adjusting portion sizes. In Malaysia, Hilton Hotels announced that it adopted Winnow's AI-powered solution to help reduce wastage during Ramadan – a period when hotel buffets are typically more extensive and prone to excess. The system generated reports providing details on the most wasted ingredients, waste patterns and guest demographics to help kitchen teams make precise adjustments during food preparation. The company claimed that last year, the same AI-powered initiative led to a 64% reduction in food waste at two hotels in Kuala Lumpur and Selangor. Other companies that have adopted AI to monitor food waste have also started sharing some interesting insights. Last year, Air New Zealand chief customer and sales officer Leanne Geraghty shared in an interview that AI was used to analyse 30,000 photos of food trays on flights coming from Los Angeles and Hong Kong. She told that findings revealed that passengers didn't like beetroot hummus as an entree and blue cheese. The next step is to take measures to remove unpopular ingredients and deliver products that customers want, adding the AI-driven insights are helpful to reduce food waste. A few obstacles remain Despite its promise, widespread adoption of AI to reduce waste in the food industry faces several hurdles. The same review published in the Food Chemistry: X journal cited implementation costs, data security concerns, and the complexity of integrating with legacy systems as major barriers. There are also ethical concerns, including privacy and fairness in algorithmic decision-making, that need to be carefully addressed. For AI to play a broader role in curbing food waste, clearer regulations, greater transparency, and more affordable solutions to enable smaller players are essential, according to the report. Some companies are using computer vision to monitor leftover food, providing data that helps kitchens identify which ingredients to cut back on. — Image by freepik Rolling out a new tech-driven system also takes more than just software – it requires people to trust that the technology will deliver real results. Goh says change management was key to successful adoption. 'We conducted hands-on workshops with our demand planners, supply chain and operations teams to walk them through how the model works and why it's reliable,' she adds. Visual dashboards were introduced to compare forecasts with actual outcomes over time. 'Seeing the model's accuracy in action helped build trust organically and empowered our teams to make data-driven decisions with greater confidence,' Goh says. The CEO also believes there is more potential for the system beyond inflight meals, including ground-based food services and group-level catering operations. As the company continues to evolve, Goh expects technology to drive ongoing green initiatives. 'AI empowers us to make smarter, faster decisions that reduce waste and boost efficiency, which are key pillars of our environmental responsibility efforts. We're integrating predictive analytics with procurement and eco-friendly packaging choices to further lower our carbon footprint,' she says.
The Star
07-05-2025
- Business
- The Star
Shopee to raise SPayLater seller fee to 4.5% starting May 8
The e-commerce platform previously revised seller transaction fees from 2.0% to 3.5% last August. — Shopee PETALING JAYA: Shopee has announced via its Seller Education Hub that it will raise the SPayLater seller fee from 3.5% to 4.5% for all completed orders starting on May 8. The fee applies to all SPayLater payment plans, which range from one month to two years. SPayLater is automatically enabled by default for eligible sellers on the platform, with no option to opt out. It is also subject to 8% Sales and Service Tax (SST). According to Shopee, the payment method increases the total value of merchandise sold by a business on the platform while providing customers more flexibility in making payments. A power tool seller, who asked to be quoted only as Loh, said that compounding costs, such as the increased SPayLater fee, are a key reason his business has shifted away from the platform. 'My business has to contend with more and more extra fees, and I'd need to hire someone just to operate and manage the sales channel. That's pushed my business away from focusing too much effort towards selling on Shopee,' he said. The e-commerce platform previously revised seller transaction fees from 2.0% to 3.5% last August, which was similarly met with a negative reaction from sellers online. Others have responded similarly to the fee change, with members of a Facebook group for Shopee sellers questioning why sellers are charged when it is buyers who choose to use the SPayLater payment method. Some sellers even suggested raising product prices to offset the increased fee. LifestyleTech has reached out to Shopee for a media statement on the fee increase, but has not received a response as of publication time.
