AI for life: Trends in artificial intelligence for biotechnology

Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools

Antibodies play a crucial role in defending the human body against diseases, including life-threatening conditions like cancer. They mediate immune responses against foreign antigens and, in some cases, self-antigens. Over time, antibody-based technologies have evolved from monoclonal antibodies (mAbs) to chimeric antigen receptor T cells (CAR-T cells), significantly impacting biotechnology, diagnostics, and therapeutics. Although these advancements have enhanced therapeutic interventions, the integration of artificial intelligence (AI) is revolutionizing antibody design and optimization. This review explores recent AI advancements, including large language models (LLMs), diffusion models, and generative AI-based applications, which have transformed antibody discovery by accelerating de novo generation, enhancing immune response precision, and optimizing therapeutic efficacy. Through advanced data analysis, AI enables the prediction and design of antibody sequences, 3D structures, complementarity-determining regions (CDRs), paratopes, epitopes, and antigen-antibody interactions. These AI-powered innovations address longstanding challenges in antibody development, significantly improving speed, specificity, and accuracy in therapeutic design. By integrating computational advancements with biomedical applications, AI is driving next-generation cancer therapies, transforming precision medicine, and enhancing patient outcomes.

Recent advances in multimodal mechanoluminescent sensors enabled by nanostructure design

Multiple modes of perception have evolved in creatures to help them survive in a highly complex world under different harsh environments. Inspired by this, multimodal sensing materials have been created as one of the most crucial elements to bridge artificial intelligence with reality. The well-organized integration of multiple independent stimuli in a single material rather than simple integration, is expected to increase the accuracy and multifunctional applications of sensing devices. However, achieving multifunction coupling through elaborate nanostructure and supramolecular design, still remains a challenge that attracts great attention. Under the framework of nanostructural design for a multimodal response, the coupling of mechanoluminescence ability and advanced stimulus-response, has been reported to realize comprehensive perception and multifunctional applications for more complex scenarios. Herein, this mini review briefly provides an overview on the latest advances of multimodal mechanoluminescent sensors, concentrating on the nanostructure design strategy for multifunctional coupling, including triboelectric compositing, supramolecular interfacial connection, and band structure modulation; as well as emphatically discussing the advantages of mechanoluminescence coupling with self-powered sensing, piezoresistive response, temperature/chemical detection, and the corresponding advanced tools for heterogeneous output decoupling. Finally, the conclusions and outlook of multimodal mechanoluminescent sensors are presented.

A review on recent advancements in pharmaceutical technology transfer of tablets from an Indian perspective

OBJECTIVE

The healthcare sector is a paramount and rapidly expanding industry in India. The pharmaceutical field in India has experienced substantial growth and transformation in recent times, making significant contributions to the global healthcare market. This comprehensive review delves into the most recent innovations in pharmaceutical technology transfer (TT), particularly in the context of tablet formulations from an Indian standpoint.

SIGNIFICANCE

The pharmaceutical sector has grappled with various challenging issues, including the escalating costs of medications and the demand for patient-friendly products.

METHODS

In this technological progress era, various cutting-edge pharmaceutical technologies, such as artificial intelligence (AI), and 3D and 4D printing, play pivotal roles in drug development. Tablets, the most promising and widely utilized dosage form worldwide, require a sophisticated approach to TT. Achieving a successful TT necessitates a dedicated team with well-defined objectives, improved documentation, and effective communication.

RESULTS

The Indian Pharmaceutical Industry (IPI) possesses the potential to make significant contributions to the global healthcare sector. Moreover, we delve into the various phases of TT, highlighting the pivotal role of formulation development and process optimization in ensuring product quality, efficiency, and cost-effectiveness along with different models of TT. Additionally, we examine the challenges associated with TT and potential solutions, as well as the initiatives of the Indian government to bolster the Indian pharmaceutical sector's position as the "Pharmacy of the World".

CONCLUSION

It is concluded that there is a need to contextualize and institutionalize the tech transfer policies for successful implementation for the benefit of the global population.

New advances in biological preservation technology for aquatic products

Aquatic products, characterized by their high moisture content, abundant nutrients, and neutral pH, create an optimal environment for the rapid proliferation of spoilage organisms, lipid oxidation, and autolytic degradation. These factors collectively expedite the spoilage and deterioration of aquatic products during storage and transportation within the supply chain. To maintain the quality and extend the shelf-life of aquatic products, appropriate preservation methods must be implemented. The growing consumer preference for bio-preservatives, is primarily driven by consumer demands for naturalness and concerns about environmental sustainability. The present review discusses commonly employed bio-preservatives derived from plants, animals, and microorganisms and their utilization in the preservation of aquatic products. Moreover, the preservation mechanisms of bio-preservatives, including antioxidant activity, inhibition of spoilage bacteria and enzyme activity, and the formation of protective films are reviewed. Integration of bio-preservation techniques with other methods, such as nanotechnology, ozone technology, and coating technology that enhance the fresh-keeping effect are discussed. Importantly, the principal issues in the application of bio-preservation technology for aquatic products and their countermeasures are presented. Further studies and the identification of new bio-preservatives that preserve the safety and quality of aquatic products should continue.

Bioimaging and the future of whole-organismal developmental physiology

While omics has transformed the study of biology, concomitant advances made at the level of the whole organism, i.e. the phenome, have arguably not kept pace with lower levels of biological organisation. In this personal commentary we evaluate the importance of imaging as a means of measuring whole organismal developmental physiology. Image acquisition, while an important process itself, has become secondary to image analysis as a bottleneck to the use of imaging in research. Here, we explore the significant potential for increasingly sophisticated approaches to image analysis, including deep learning, to advance our understanding of how developing animals grow and function. Furthermore, unlike many species-specific methodologies, tools and technologies, we explore how computer vision has the potential to be transferable between species, life stages, experiments and even taxa in which embryonic development can be imaged. We identify what we consider are six of the key challenges and opportunities in the application of computer vision to developmental physiology carried out in our lab, and more generally. We reflect on the tangibility of transferrable computer vision models capable of measuring the integrative physiology of a broad range of developing organisms, and thereby driving the adoption of phenomics for developmental physiology. We are at an exciting time of witnessing the move from computer vision as a replacement for manual observation, or manual image analysis, to it enabling a fundamentally more powerful approach to exploring and understanding the complex biology of developing organisms, the quantification of which has long posed a challenge to researchers.

Explainable AI chatbots towards XAI ChatGPT: A review

Advances in artificial intelligence (AI) have had a major impact on natural language processing (NLP), even more so with the emergence of large-scale language models like ChatGPT. This paper aims to provide a critical review of explainable AI (XAI) methodologies for AI chatbots, with a particular focus on ChatGPT. Its main objectives are to investigate the applied methods that improve the explainability of AI chatbots, identify the challenges and limitations within them, and explore future research directions. Such goals emphasize the need for transparency and interpretability of AI systems to build trust with users and allow for accountability. While integrating such interdisciplinary methods, such as hybrid methods combining knowledge graphs with ChatGPT, enhancing explainability, they also highlight industry needs for explainability and user-centred design. This will be followed by a discussion of the balance between explainability and performance, then the role of human judgement, and finally the future of verifiable AI. These are the avenues through which insights can be used to guide the development of transparent, reliable and efficient AI chatbots.

Advances in cyclotide research: bioactivity to cyclotide-based therapeutics

Cyclotides are a class of plant-derived cyclic peptides having a distinctive structure with a cyclic cystine knot (CCK) motif. They are stable molecules that naturally play a role in plant defense. Till date, more than 750 cyclotides have been reported among diverse plant taxa belonging to Cucurbitaceae, Violaceae, Rubiaceae, Solanaceae, and Fabaceae. These native cyclotides exhibit several bioactivities, such as anti-bacterial, anti-HIV, anti-fungal, pesticidal, cytotoxic, and hemolytic activities which have immense significance in agriculture and therapeutics. The general mode of action of cyclotides is related to their structure, where their hydrophobic face penetrates the cell membrane and disrupts it to exhibit anti-microbial, cytotoxic, or hemolytic activities. Thus, the structure-activity relationship is of significance in cyclotides. Further, owing to their, small size, stability, and potential to interact and cross the membrane barrier of cells, they make promising choices for developing peptide-based biologics. However, challenges, such as production complexity, pharmacokinetic limitations, and off-target effects hinder their development. Advancements in cyclotide engineering, such as peptide grafting, ligand conjugation, and nanocarrier integration, heterologous production along with computational design optimization, can help overcome these challenges. Given the potential of these cyclic peptides, the present review focuses on the diversity, bioactivities, and structure-activity relationships of cyclotides, and advancements in cyclotides engineering emphasizing their unique attributes for diverse medical and biotechnological applications.

Process simulation and evaluation of scaled-up biocatalytic systems: Advances, challenges, and future prospects

With the increased demand for bio-based products and the rapid development of biomanufacturing technologies, biocatalytic reactions including microorganisms and enzyme based, have become promising approaches. Prior to the scale-up of production process, environmental and economic feasibility analysis are essential for the development of a sustainable and intelligent bioeconomy in the context of industry 4.0. To achieve these goals, process simulation supports system optimization, improves energy and resource utilization efficiencies, and supports digital bioprocessing. However, due to the insufficient understanding of cellular metabolism and interaction mechanisms, there is still a lack of rational and transparent simulation tools to efficiently simulate, control, and optimize microbial/enzymatic reaction processes. Therefore, there is an urgent need to develop frameworks that integrate kinetic modeling, process simulation, and sustainability analysis for bioreaction simulations and their optimization. This review summarizes and compares the advantages and disadvantages of different process simulation software and models in simulating biocatalytic processes, identifies the limitations of traditional reaction kinetics models, and proposes the requirement of simulations close to real reactions. In addition, we explore the current state of kinetic modeling at the microscopic scale and how process simulation can be linked to kinetic models of cellular metabolism and computational fluid dynamics modeling. Finally, this review discusses the requirement of sensitivity analysis and how machine learning can assist with optimization of simulations to improve energy efficiency and product yields from techno-economic and life cycle assessment perspectives.

A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology

Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.

Concordance of ChatGPT With American Burn Association Guidelines on Acute Burns

ABSTRACT

Burn injuries often require immediate assistance and specialized care for optimal management and outcomes. The emergence of accessible artificial intelligence technology has just recently started being applied to healthcare decision making and patient education. However, its role in clinical recommendations is still under scrutiny. This study aims to evaluate ChatGPT's outputs and the appropriateness of its responses to commonly asked questions regarding acute burn care when compared to the American Burn Association Guidelines. Twelve commonly asked questions were formulated by a fellowship-trained burn surgeon to address the American Burn Association's recommendations on burn injuries, management, and patient referral. These questions were prompted into ChatGPT, and each response was compared with the aforementioned guidelines, the gold standard for accurate and evidence-based burn care recommendations. Three burn surgeons independently evaluated the appropriateness and comprehensiveness of each ChatGPT response based on the guidelines according to the modified Global Quality Score scale. The average score for ChatGPT-generated responses was 4.56 ± 0.65, indicating the responses were exceptional quality with the most important topics covered and in high concordance with the guidelines. This initial comparison of ChatGPT-generated responses and the American Burn Association guidelines demonstrates that ChatGPT can accurately and comprehensibly describe appropriate treatment and management plans for acute burn injuries. We foresee that ChatGPT may play a role as a complementary tool in medical decision making and patient education, having a profound impact on clinical practice, research, and education.

Transformative insights: Image-based breast cancer detection and severity assessment through advanced AI techniques

In the realm of image-based breast cancer detection and severity assessment, this study delves into the revolutionary potential of sophisticated artificial intelligence (AI) techniques. By investigating image processing, machine learning (ML), and deep learning (DL), the research illuminates their combined impact on transforming breast cancer diagnosis. This integration offers insights into early identification and precise characterization of cancers. With a foundation in 125 research articles, this article presents a comprehensive overview of the current state of image-based breast cancer detection. Synthesizing the transformative role of AI, including image processing, ML, and DL, the review explores how these technologies collectively reshape the landscape of breast cancer diagnosis and severity assessment. An essential aspect highlighted is the synergy between advanced image processing methods and ML algorithms. This combination facilitates the automated examination of medical images, which is crucial for detecting minute anomalies indicative of breast cancer. The utilization of complex neural networks for feature extraction and pattern recognition in DL models further enhances diagnostic precision. Beyond diagnostic improvements, the abstract underscores the substantial influence of AI-driven methods on breast cancer treatment. The integration of AI not only increases diagnostic precision but also opens avenues for individualized treatment planning, marking a paradigm shift toward personalized medicine in breast cancer care. However, challenges persist, with issues related to data quality and interpretability requiring continued research efforts. Looking forward, the abstract envisions future directions for breast cancer identification and diagnosis, emphasizing the adoption of explainable AI techniques and global collaboration for data sharing. These initiatives promise to propel the field into a new era characterized by enhanced efficiency and precision in breast cancer care.

Quantum-level machine learning calculations of Levodopa

Many drug molecules contain functional groups, resulting in a torsional barrier corresponding to rotation around the bond linking the fragments. In medicinal chemistry and pharmaceutical sciences, inclusive of drug design studies, the exact calculation of the potential energy surface (PES) of these molecular torsions is extremely important and precious. Machine learning (ML), including deep learning (DL), is currently one of the most rapidly evolving tools in computer-aided drug discovery and molecular simulations. In this work, we used ANI-1x neural network potential as a quantum-level ML to predict the PESs of the L-3,4-dihydroxyphenylalanine (Levodopa) antiparkinsonian drug molecule. The electronic energies and structural parameters calculated by density functional theory (DFT) using the wB97X method and all possible Pople's basis sets indicated the 6-31G(d) basis set, when used with the wB97X functional, exhibits behavior similar to that of the ANI-1x model. The vibrational frequencies investigation showed a linear correlation between DFT and ML data. All ANI-1x calculations were completed quickly in a very short computing time. From this perspective, we expect the ANI-1x dataset applied in this work to be appreciably efficient and effective in computational structure-based drug design studies.

ChatGPT versus a customized AI chatbot (Anatbuddy) for anatomy education: A comparative pilot study

Large Language Models (LLMs) have the potential to improve education by personalizing learning. However, ChatGPT-generated content has been criticized for sometimes producing false, biased, and/or hallucinatory information. To evaluate AI's ability to return clear and accurate anatomy information, this study generated a custom interactive and intelligent chatbot (Anatbuddy) through an Open AI Application Programming Interface (API) that enables seamless AI-driven interactions within a secured cloud infrastructure. Anatbuddy was programmed through a Retrieval Augmented Generation (RAG) method to provide context-aware responses to user queries based on a predetermined knowledge base. To compare their outputs, various queries (i.e., prompts) on thoracic anatomy (n = 18) were fed into Anatbuddy and ChatGPT 3.5. A panel comprising three experienced anatomists evaluated both tools' responses for factual accuracy, relevance, completeness, coherence, and fluency on a 5-point Likert scale. These ratings were reviewed by a third party blinded to the study, who revised and finalized scores as needed. Anatbuddy's factual accuracy (mean ± SD = 4.78/5.00 ± 0.43; median = 5.00) was rated significantly higher (U = 84, p = 0.01) than ChatGPT's accuracy (4.11 ± 0.83; median = 4.00). No statistically significant differences were detected between the chatbots for the other variables. Given ChatGPT's current content knowledge limitations, we strongly recommend the anatomy profession develop a custom AI chatbot for anatomy education utilizing a carefully curated knowledge base to ensure accuracy. Further research is needed to determine students' acceptance of custom chatbots for anatomy education and their influence on learning experiences and outcomes.

Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence

The field of computational protein engineering has been transformed by recent advancements in machine learning, artificial intelligence, and molecular modeling, enabling the design of proteins with unprecedented precision and functionality. Computational methods now play a crucial role in enhancing the stability, activity, and specificity of proteins for diverse applications in biotechnology and medicine. Techniques such as deep learning, reinforcement learning, and transfer learning have dramatically improved protein structure prediction, optimization of binding affinities, and enzyme design. These innovations have streamlined the process of protein engineering by allowing the rapid generation of targeted libraries, reducing experimental sampling, and enabling the rational design of proteins with tailored properties. Furthermore, the integration of computational approaches with high-throughput experimental techniques has facilitated the development of multifunctional proteins and novel therapeutics. However, challenges remain in bridging the gap between computational predictions and experimental validation and in addressing ethical concerns related to AI-driven protein design. This review provides a comprehensive overview of the current state and future directions of computational methods in protein engineering, emphasizing their transformative potential in creating next-generation biologics and advancing synthetic biology.

Recent Advances in the Biosynthesis of Mid- and Long-Chain Dicarboxylic Acids Using Terminally Oxidizing Unconventional Yeasts

As high-performance monomers for the manufacture of polyamide materials, mid- and long-chain dicarboxylic acids (DCAi, i ≥ 6) have received extensive attention from researchers. Biosynthesis is gradually replacing chemical synthesis due to its outstanding advantages in the industrial production of mid- and long-chain dicarboxylic acids, which is mostly achieved by using the strong terminal oxidation ability of nonmodel microorganisms such as Candida tropicalis to oxidize hydrophobic substrates such as alkanes. Here, we first summarize the metabolic pathways of oxidative alkane conversion into dicarboxylic acid by terminally oxidizing unconventional yeasts and the corresponding metabolic engineering strategies. Then, we summarize the research progress on new dicarboxylic acid production processes. Finally, the future development directions in the biosynthesis of mid- and long-chain dicarboxylic acids are prospected from synthetic biology and bioprocess engineering, which can also provide a reference for the synthesis of other biobased chemicals and biomaterials.

From Industry 5.0 to Forestry 5.0: Bridging the gap with Human-Centered Artificial Intelligence

Purpose of the Review

Recent technological innovations in Artificial Intelligence (AI) have successfully revolutionized many industrial processes, enhancing productivity and sustainability, under the paradigm of Industry 5.0. It offers opportunities for the forestry sector such as predictive analytics, automation, and precision management, which could transform traditional forest operations into smart, effective, and sustainable practices. The paper sets forth to outline the evolution from Industry 5.0 and its promising transition into Forestry 5.0. The purpose is to elucidate the status of these developments, identify enabling technologies, particularly AI, and uncover the challenges hindering the efficient adoption of these techniques in forestry by presenting a framework.

Recent Findings

However, the gap between potential and practical implementation is primarily due to logistical, infrastructural, and environmental challenges unique to the forestry sector. The solution lies in Human-Centered AI, which, unlike the Industry 4.0 paradigm, aims to integrate humans into the loop rather than replace them, thereby fostering safe, secure, and trustworthy Human-AI interactions.

Summary

The paper concludes by highlighting the need for Human-Centered AI development for the successful transition to Forestry 5.0 - where the goal is to support the human workers rather than substituting them. A multidisciplinary approach involving technologists, ecologists, policymakers, and forestry practitioners is essential to navigate these challenges, leading to a sustainable and technologically advanced future for the forestry sector. In this transformation, our focus remains on ensuring a balance between increased productivity, nature conservation and social licence, worker safety and satisfaction.

Panoramic view of artificial fruit ripening agents sensing technologies and the exigency of developing smart, rapid, and portable detection devices: A review

Recently, the availability of point-of-care sensor systems has led to the rapid development of smart and portable devices for the detection of hazardous analytes. The rapid flow of artificially ripened fruits into the market is associated with an elevated risk to human life, agriculture, and the ecosystem due to the use of artificial fruit ripening agents (AFRAs). Accordingly, there is a need for the development of "Point-of-care Sensors" to detect AFRAs due to several advantages, such as simple operation, promising detection mechanism, higher selectivity and sensitivity, compact, and portable. Traditional detection approaches are time-consuming and inappropriate for on-the-spot analyses. Presented comprehensive review aimed to reveal how such technology has systematically evolved over time (through conventional, advanced, and portable smart techniques) detection detect AFRA, till date. Moreover, focuses and highlights a framework of initiatives undertaken for technological advancements in the development of smart the portable detection techniques (kits) for the onsite detection of AFRAs in fruits with in-depth discussion over sensing mechanism and analytical performance of the sensing technology. Notably, colorimetric detection methods have the greatest potential for real-time monitoring of AFRA and its residues because they are easy to assemble, have a high level of selectivity and sensitivity, and can be read by the human eye independently. This study sought to differentiate between traditional credible strategies by presenting new prospects, perceptions, and challenges related to portable devices. This review provides systematic framework of advances in portable field recognition strategies for the on-spot AFRA detection in fruits and critical information for development of new paper-based portable sensors for fruit diagnostic sectors.

Recent advances in artificial intelligent strategies for tissue engineering and regenerative medicine

BACKGROUND

Tissue engineering and regenerative medicine (TERM) aim to repair or replace damaged or lost tissues or organs due to accidents, diseases, or aging, by applying different sciences. For this purpose, an essential part of TERM is the designing, manufacturing, and evaluating of scaffolds, cells, tissues, and organs. Artificial intelligence (AI) or the intelligence of machines or software can be effective in all areas where computers play a role.

METHODS

The "artificial intelligence," "machine learning," "tissue engineering," "clinical evaluation," and "scaffold" keywords used for searching in various databases and articles published from 2000 to 2024 were evaluated.

RESULTS

The combination of tissue engineering and AI has created a new generation of technological advancement in the biomedical industry. Experience in TERM has been refined using advanced design and manufacturing techniques. Advances in AI, particularly deep learning, offer an opportunity to improve scientific understanding and clinical outcomes in TERM.

CONCLUSION

The findings of this research show the high potential of AI, machine learning, and robots in the selection, design, and fabrication of scaffolds, cells, tissues, or organs, and their analysis, characterization, and evaluation after their implantation. AI can be a tool to accelerate the introduction of tissue engineering products to the bedside.

HIGHLIGHTS

The capabilities of artificial intelligence (AI) can be used in different ways in all the different stages of TERM and not only solve the existing limitations, but also accelerate the processes, increase efficiency and precision, reduce costs, and complications after transplantation. ML predicts which technologies have the most efficient and easiest path to enter the market and clinic. The use of AI along with these imaging techniques can lead to the improvement of diagnostic information, the reduction of operator errors when reading images, and the improvement of image analysis (such as classification, localization, regression, and segmentation).

ChatGPT in medicine: A cross-disciplinary systematic review of ChatGPT's (artificial intelligence) role in research, clinical practice, education, and patient interaction

BACKGROUND

ChatGPT, a powerful AI language model, has gained increasing prominence in medicine, offering potential applications in healthcare, clinical decision support, patient communication, and medical research. This systematic review aims to comprehensively assess the applications of ChatGPT in healthcare education, research, writing, patient communication, and practice while also delineating potential limitations and areas for improvement.

METHOD

Our comprehensive database search retrieved relevant papers from PubMed, Medline and Scopus. After the screening process, 83 studies met the inclusion criteria. This review includes original studies comprising case reports, analytical studies, and editorials with original findings.

RESULT

ChatGPT is useful for scientific research and academic writing, and assists with grammar, clarity, and coherence. This helps non-English speakers and improves accessibility by breaking down linguistic barriers. However, its limitations include probable inaccuracy and ethical issues, such as bias and plagiarism. ChatGPT streamlines workflows and offers diagnostic and educational potential in healthcare but exhibits biases and lacks emotional sensitivity. It is useful in inpatient communication, but requires up-to-date data and faces concerns about the accuracy of information and hallucinatory responses.

CONCLUSION

Given the potential for ChatGPT to transform healthcare education, research, and practice, it is essential to approach its adoption in these areas with caution due to its inherent limitations.

Advancing plant biology through deep learning-powered natural language processing

The application of deep learning methods, specifically the utilization of Large Language Models (LLMs), in the field of plant biology holds significant promise for generating novel knowledge on plant cell systems. The LLM framework exhibits exceptional potential, particularly with the development of Protein Language Models (PLMs), allowing for in-depth analyses of nucleic acid and protein sequences. This analytical capacity facilitates the discernment of intricate patterns and relationships within biological data, encompassing multi-scale information within DNA or protein sequences. The contribution of PLMs extends beyond mere sequence patterns and structure--function recognition; it also supports advancements in genetic improvements for agriculture. The integration of deep learning approaches into the domain of plant sciences offers opportunities for major breakthroughs in basic research across multi-scale plant traits. Consequently, the strategic application of deep learning methodologies, particularly leveraging the potential of LLMs, will undoubtedly play a pivotal role in advancing plant sciences, plant production, plant uses and propelling the trajectory toward sustainable agroecological and agro-food transitions.

Learning vs. understanding: When does artificial intelligence outperform process-based modeling in soil organic carbon prediction?

In recent years, machine learning (ML) algorithms have gained substantial recognition for ecological modeling across various temporal and spatial scales. However, little evaluation has been conducted for the prediction of soil organic carbon (SOC) on small data sets commonly inherent to long-term soil ecological research. In this context, the performance of ML algorithms for SOC prediction has never been tested against traditional process-based modeling approaches. Here, we compare ML algorithms, calibrated and uncalibrated process-based models as well as multiple ensembles on their performance in predicting SOC using data from five long-term experimental sites (comprising 256 independent data points) in Austria. Using all available data, the ML-based approaches using Random forest and Support vector machines with a polynomial kernel were superior to all process-based models. However, the ML algorithms performed similar or worse when the number of training samples was reduced or when a leave-one-site-out cross validation was applied. This emphasizes that the performance of ML algorithms is strongly dependent on the data-size related quality of learning information following the well-known curse of dimensionality phenomenon, while the accuracy of process-based models significantly relies on proper calibration and combination of different modeling approaches. Our study thus suggests a superiority of ML-based SOC prediction at scales where larger datasets are available, while process-based models are superior tools when targeting the exploration of underlying biophysical and biochemical mechanisms of SOC dynamics in soils. Therefore, we recommend applying ensembles of ML algorithms with process-based models to combine advantages inherent to both approaches.

Avoiding basic mistakes when programming the use of artificial intelligence in soil and environmental science research

In this discussion text (proposed as an opinionated exposition on a relevant scientific issue, with the aim of stimulating further discussion in a broader scientific forum) the author comments on mistakes that should be avoided when trying to use artificial intelligence (AI) in research, with special focus on soil science and environmental sciences. The author indicates aspects of research where it would not be reasonable (and/or correct) to use AI, while showing other aspects where an appropriate use of AI tools could be of real help for researchers in these fields and the whole society. The use of AI in investigation is a cutting-edge theme needing reflection and proposals to extract its best without causing an inappropriate deviation of resources, as well as a waste of time for people involved in direct research tasks or assessment, and without provoking undesirable side effects.

Progress in membrane distillation processes for dye wastewater treatment: A review

Textile and cosmetic industries generate large amounts of dye effluents requiring treatment before discharge. This wastewater contains high levels of reactive dyes, low to none-biodegradable materials and chemical residues. Technically, dye wastewater is characterised by high chemical and biological oxygen demand. Biological, physical and pressure-driven membrane processes have been extensively used in textile wastewater treatment plants. However, these technologies are characterised by process complexity and are often costly. Also, process efficiency is not achieved in cost-effective biochemical and physical treatment processes. Membrane distillation (MD) emerged as a promising technology harnessing challenges faced by pressure-driven membrane processes. To ensure high cost-effectiveness, the MD can be operated by solar energy or low-grade waste heat. Herein, the MD purification of dye wastewater is comprehensively and yet concisely discussed. This involved research advancement in MD processes towards removal of dyes from industrial effluents. Also, challenges faced by this process with a specific focus on fouling are reviewed. Current literature mainly tested MD setups in the laboratory scale suggesting a deep need of further optimization of membrane and module designs in near future, especially for textile wastewater treatment. There is a need to deliver customized high-porosity hydrophobic membrane design with the appropriate thickness and module configuration to reduce concentration and temperature polarization (CP and TP). Also, energy loss should be minimized while increasing dye rejection and permeate flux. Although laboratory experiments remain pivotal in optimizing the MD process for treating dye wastewater, the nature of their time intensity poses a challenge. Given the multitude of parameters involved in MD process optimization, artificial intelligence (AI) methodologies present a promising avenue for assistance. Thus, AI-driven algorithms have the potential to enhance overall process efficiency, cutting down on time, fine-tuning parameters, and driving cost reductions. However, achieving an optimal balance between efficiency enhancements and financial outlays is a complex process. Finally, this paper suggests a research direction for the development of effective synthetic and natural dye removal from industrially discharged wastewater.

The Millennia-Long Development of Drugs Associated with the 80-Year-Old Artificial Intelligence Story: The Therapeutic Big Bang?

The journey of drug discovery (DD) has evolved from ancient practices to modern technology-driven approaches, with Artificial Intelligence (AI) emerging as a pivotal force in streamlining and accelerating the process. Despite the vital importance of DD, it faces challenges such as high costs and lengthy timelines. This review examines the historical progression and current market of DD alongside the development and integration of AI technologies. We analyse the challenges encountered in applying AI to DD, focusing on drug design and protein-protein interactions. The discussion is enriched by presenting models that put forward the application of AI in DD. Three case studies are highlighted to demonstrate the successful application of AI in DD, including the discovery of a novel class of antibiotics and a small-molecule inhibitor that has progressed to phase II clinical trials. These cases underscore the potential of AI to identify new drug candidates and optimise the development process. The convergence of DD and AI embodies a transformative shift in the field, offering a path to overcome traditional obstacles. By leveraging AI, the future of DD promises enhanced efficiency and novel breakthroughs, heralding a new era of medical innovation even though there is still a long way to go.

ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine

Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, medical education, and research. In this narrative review, we evaluate the myriad applications of ChatGPT in diverse healthcare domains. We discuss its potential role in clinical decision-making, exploring how it can assist physicians by providing rapid, data-driven insights for diagnosis and treatment. We review the benefits of ChatGPT in personalized patient care, particularly in geriatric care, medication management, weight loss and nutrition, and physical activity guidance. We further delve into its potential to enhance medical research, through the analysis of large datasets, and the development of novel methodologies. In the realm of medical education, we investigate the utility of ChatGPT as an information retrieval tool and personalized learning resource for medical students and professionals. There are numerous promising applications of ChatGPT that will likely induce paradigm shifts in healthcare practice, education, and research. The use of ChatGPT may come with several benefits in areas such as clinical decision making, geriatric care, medication management, weight loss and nutrition, physical fitness, scientific research, and medical education. Nevertheless, it is important to note that issues surrounding ethics, data privacy, transparency, inaccuracy, and inadequacy persist. Prior to widespread use in medicine, it is imperative to objectively evaluate the impact of ChatGPT in a real-world setting using a risk-based approach.

The Impact of Performance Expectancy, Workload, Risk, and Satisfaction on Trust in ChatGPT: Cross-Sectional Survey Analysis

BACKGROUND

ChatGPT (OpenAI) is a powerful tool for a wide range of tasks, from entertainment and creativity to health care queries. There are potential risks and benefits associated with this technology. In the discourse concerning the deployment of ChatGPT and similar large language models, it is sensible to recommend their use primarily for tasks a human user can execute accurately. As we transition into the subsequent phase of ChatGPT deployment, establishing realistic performance expectations and understanding users' perceptions of risk associated with its use are crucial in determining the successful integration of this artificial intelligence (AI) technology.

OBJECTIVE

The aim of the study is to explore how perceived workload, satisfaction, performance expectancy, and risk-benefit perception influence users' trust in ChatGPT.

METHODS

A semistructured, web-based survey was conducted with 607 adults in the United States who actively use ChatGPT. The survey questions were adapted from constructs used in various models and theories such as the technology acceptance model, the theory of planned behavior, the unified theory of acceptance and use of technology, and research on trust and security in digital environments. To test our hypotheses and structural model, we used the partial least squares structural equation modeling method, a widely used approach for multivariate analysis.

RESULTS

A total of 607 people responded to our survey. A significant portion of the participants held at least a high school diploma (n=204, 33.6%), and the majority had a bachelor's degree (n=262, 43.1%). The primary motivations for participants to use ChatGPT were for acquiring information (n=219, 36.1%), amusement (n=203, 33.4%), and addressing problems (n=135, 22.2%). Some participants used it for health-related inquiries (n=44, 7.2%), while a few others (n=6, 1%) used it for miscellaneous activities such as brainstorming, grammar verification, and blog content creation. Our model explained 64.6% of the variance in trust. Our analysis indicated a significant relationship between (1) workload and satisfaction, (2) trust and satisfaction, (3) performance expectations and trust, and (4) risk-benefit perception and trust.

CONCLUSIONS

The findings underscore the importance of ensuring user-friendly design and functionality in AI-based applications to reduce workload and enhance user satisfaction, thereby increasing user trust. Future research should further explore the relationship between risk-benefit perception and trust in the context of AI chatbots.

Protein coding regions prediction by fusing DNA shape features

Exons crucial for coding are often hidden within introns, and the two tend to vary greatly in length, which results in deep learning-based protein coding region prediction methods often performing poorly when applied to more structurally complex biological genomes. DNA shape information also plays a role in revealing the underlying logic of gene expression, yet current methods ignore the influence of DNA shape features when distinguishing coding and non-coding regions. We propose a method to predict protein-coding regions using the CNNS-BRNN model, which incorporates DNA shape features and improves the model's ability to distinguish between intronic and exonic features. We use a fusion coding technique that combines DNA shape features and traditional sequence features. Experiments show that this method outperforms the baseline method in metrics such as AUC and F1 by 2.3% and 5.3%, respectively, and the fusion coding method that introduces DNA shape features has a significant improvement in model performance.

Peptide-based drug discovery through artificial intelligence: towards an autonomous design of therapeutic peptides

With their diverse biological activities, peptides are promising candidates for therapeutic applications, showing antimicrobial, antitumour and hormonal signalling capabilities. Despite their advantages, therapeutic peptides face challenges such as short half-life, limited oral bioavailability and susceptibility to plasma degradation. The rise of computational tools and artificial intelligence (AI) in peptide research has spurred the development of advanced methodologies and databases that are pivotal in the exploration of these complex macromolecules. This perspective delves into integrating AI in peptide development, encompassing classifier methods, predictive systems and the avant-garde design facilitated by deep-generative models like generative adversarial networks and variational autoencoders. There are still challenges, such as the need for processing optimization and careful validation of predictive models. This work outlines traditional strategies for machine learning model construction and training techniques and proposes a comprehensive AI-assisted peptide design and validation pipeline. The evolving landscape of peptide design using AI is emphasized, showcasing the practicality of these methods in expediting the development and discovery of novel peptides within the context of peptide-based drug discovery.

ChatGPT: Literate or intelligent about UN sustainable development goals?

Generative AI tools, such as ChatGPT, are progressively transforming numerous sectors, demonstrating a capacity to impact human life dramatically. This research seeks to evaluate the UN Sustainable Development Goals (SDGs) literacy of ChatGPT, which is crucial for diverse stakeholders involved in SDG-related policies. Experimental outcomes from two widely used Sustainability Assessment tests-the UN SDG Fitness Test and Sustainability Literacy Test (SULITEST) - suggest that ChatGPT exhibits high SDG literacy, yet its comprehensive SDG intelligence needs further exploration. The Fitness Test gauges eight vital competencies across introductory, intermediate, and advanced levels. Accurate mapping of these to the test questions is essential for partial evaluation of SDG intelligence. To assess SDG intelligence, the questions from both tests were mapped to 17 SDGs and eight cross-cutting SDG core competencies, but both test questionnaires were found to be insufficient. SULITEST could satisfactorily map only 5 out of 8 competencies, whereas the Fitness Test managed to map 6 out of 8. Regarding the coverage of the Fitness Test and SULITEST, their mapping to the 17 SDGs, both tests fell short. Most SDGs were underrepresented in both instruments, with certain SDGs not represented at all. Consequently, both tools proved ineffective in assessing SDG intelligence through SDG coverage. The study recommends future versions of ChatGPT to enhance competencies such as collaboration, critical thinking, systems thinking, and others to achieve the SDGs. It concludes that while AI models like ChatGPT hold considerable potential in sustainable development, their usage must be approached carefully, considering current limitations and ethical implications.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

Plant genome information facilitates plant functional genomics

MAIN CONCLUSION

In this review, we give an overview of plant sequencing efforts and how this impacts plant functional genomics research. Plant genome sequence information greatly facilitates the studies of plant biology, functional genomics, evolution of genomes and genes, domestication processes, phylogenetic relationships, among many others. More than two decades of sequencing efforts have boosted the number of available sequenced plant genomes. The first plant genome, of Arabidopsis, was published in the year 2000 and currently, 4604 plant genomes from 1482 plant species have been published. Various large sequence initiatives are running, which are planning to produce tens of thousands of sequenced plant genomes in the near future. In this review, we give an overview on the status of sequenced plant genomes and on the use of genome information in different research areas.

Evaluating insomnia queries from an artificial intelligence chatbot for patient education

STUDY OBJECTIVES

We evaluated the accuracy of ChatGPT in addressing insomnia-related queries for patient education and assessed ChatGPT's ability to provide varied responses based on differing prompting scenarios.

METHODS

Four identical sets of 20 insomnia-related queries were posed to ChatGPT. Each set differed by the context in which ChatGPT was prompted: no prompt, patient-centered, physician-centered, and with references and statistics. Responses were reviewed by 2 academic sleep surgeons, 1 academic sleep medicine physician, and 2 sleep medicine fellows across 4 domains: clinical accuracy, prompt adherence, referencing, and statistical precision, using a binary grading system. Flesch-Kincaid grade-level scores were calculated to estimate the grade level of the responses, with statistical differences between prompts analyzed via analysis of variance and Tukey's test. Interrater reliability was calculated using Fleiss's kappa.

RESULTS

The study revealed significant variations in the Flesch-Kincaid grade-level scores across 4 prompts: unprompted (13.2 ± 2.2), patient-centered (8.1 ± 1.9), physician-centered (15.4 ± 2.8), and with references and statistics (17.3 ± 2.3, P < .001). Despite poor Fleiss kappa scores, indicating low interrater reliability for clinical accuracy and relevance, all evaluators agreed that the majority of ChatGPT's responses were clinically accurate, with the highest variability on Form 4. The responses were also uniformly relevant to the given prompts (100% agreement). Eighty percent of the references ChatGPT cited were verified as both real and relevant, and only 25% of cited statistics were corroborated within referenced articles.

CONCLUSIONS

ChatGPT can be used to generate clinically accurate responses to insomnia-related inquiries.

CITATION

Alapati R, Campbell D, Molin N, et al. Evaluating insomnia queries from an artificial intelligence chatbot for patient education. J Clin Sleep Med. 2024;20(4):583-594.

ChatGPT fails challenging the recent ESCMID brain abscess guideline

BACKGROUND

With artificial intelligence (AI) on the rise, it remains unclear if AI is able to professionally evaluate medical research and give scientifically valid recommendations.

AIM

This study aimed to assess the accuracy of ChatGPT's responses to ten key questions on brain abscess diagnostics and treatment in comparison to the guideline recently published by the European Society for Clinical Microbiology and Infectious Diseases (ESCMID).

METHODS

All ten PECO (Population, Exposure, Comparator, Outcome) questions which had been developed during the guideline process were presented directly to ChatGPT. Next, ChatGPT was additionally fed with data from studies selected for each PECO question by the ESCMID committee. AI's responses were subsequently compared with the recommendations of the ESCMID guideline.

RESULTS

For 17 out of 20 challenges, ChatGPT was able to give recommendations on the management of patients with brain abscess, including grade of evidence and strength of recommendation. Without data prompting, 70% of questions were answered very similar to the guideline recommendation. In the answers that differed from the guideline recommendations, no patient hazard was present. Data input slightly improved the clarity of ChatGPT's recommendations, but, however, led to less correct answers including two recommendations that directly contradicted the guideline, being associated with the possibility of a hazard to the patient.

CONCLUSION

ChatGPT seems to be able to rapidly gather information on brain abscesses and give recommendations on key questions about their management in most cases. Nevertheless, single responses could possibly harm the patients. Thus, the expertise of an expert committee remains inevitable.

Artificial intelligence / machine-learning tool for post-market surveillance of in vitro diagnostic assays

The study compares an artificial intelligence technology with traditional manual search of literature databases to assess the accuracy and efficiency of retrieving relevant articles for post-market surveillance of in vitro diagnostic and medical devices under the Medical Device Regulation and In Vitro Diagnostic Medical Device Regulation. Over a 3-year period, literature searches and technical assessment searches were performed manually or using the Huma.AI platform to retrieve relevant articles related to the safety and performance of selected in vitro diagnostic and medical devices. The manual search involved refined keyword searches, screening of titles/abstracts / full text, and extraction of relevant information. The Huma.AI search utilized advanced caching techniques and a natural language processing system to identify relevant reports. Searches were conducted on PubMed and PubMed Central. The number of identified relevant reports, precision rates, and time requirements for each approach were analyzed. The Huma.AI system outperformed the manual search in terms of the number of identified relevant articles in almost all cases. The average precision rates per year were significantly higher and more consistent with the Huma.AI search compared with the manual search. The Huma.AI system also took significantly less time to perform the searches and analyze the outputs than the manual search. The study demonstrated that the Huma.AI platform was more effective and efficient in identifying relevant articles compared with the manual approach.

ChatGPT in healthcare: A taxonomy and systematic review

The recent release of ChatGPT, a chat bot research project/product of natural language processing (NLP) by OpenAI, stirs up a sensation among both the general public and medical professionals, amassing a phenomenally large user base in a short time. This is a typical example of the 'productization' of cutting-edge technologies, which allows the general public without a technical background to gain firsthand experience in artificial intelligence (AI), similar to the AI hype created by AlphaGo (DeepMind Technologies, UK) and self-driving cars (Google, Tesla, etc.). However, it is crucial, especially for healthcare researchers, to remain prudent amidst the hype. This work provides a systematic review of existing publications on the use of ChatGPT in healthcare, elucidating the 'status quo' of ChatGPT in medical applications, for general readers, healthcare professionals as well as NLP scientists. The large biomedical literature database PubMed is used to retrieve published works on this topic using the keyword 'ChatGPT'. An inclusion criterion and a taxonomy are further proposed to filter the search results and categorize the selected publications, respectively. It is found through the review that the current release of ChatGPT has achieved only moderate or 'passing' performance in a variety of tests, and is unreliable for actual clinical deployment, since it is not intended for clinical applications by design. We conclude that specialized NLP models trained on (bio)medical datasets still represent the right direction to pursue for critical clinical applications.

Exploring the Feasibility of Using ChatGPT to Create Just-in-Time Adaptive Physical Activity mHealth Intervention Content: Case Study

BACKGROUND

Achieving physical activity (PA) guidelines' recommendation of 150 minutes of moderate-to-vigorous PA per week has been shown to reduce the risk of many chronic conditions. Despite the overwhelming evidence in this field, PA levels remain low globally. By creating engaging mobile health (mHealth) interventions through strategies such as just-in-time adaptive interventions (JITAIs) that are tailored to an individual's dynamic state, there is potential to increase PA levels. However, generating personalized content can take a long time due to various versions of content required for the personalization algorithms. ChatGPT presents an incredible opportunity to rapidly produce tailored content; however, there is a lack of studies exploring its feasibility.

OBJECTIVE

This study aimed to (1) explore the feasibility of using ChatGPT to create content for a PA JITAI mobile app and (2) describe lessons learned and future recommendations for using ChatGPT in the development of mHealth JITAI content.

METHODS

During phase 1, we used Pathverse, a no-code app builder, and ChatGPT to develop a JITAI app to help parents support their child's PA levels. The intervention was developed based on the Multi-Process Action Control (M-PAC) framework, and the necessary behavior change techniques targeting the M-PAC constructs were implemented in the app design to help parents support their child's PA. The acceptability of using ChatGPT for this purpose was discussed to determine its feasibility. In phase 2, we summarized the lessons we learned during the JITAI content development process using ChatGPT and generated recommendations to inform future similar use cases.

RESULTS

In phase 1, by using specific prompts, we efficiently generated content for 13 lessons relating to increasing parental support for their child's PA following the M-PAC framework. It was determined that using ChatGPT for this case study to develop PA content for a JITAI was acceptable. In phase 2, we summarized our recommendations into the following six steps when using ChatGPT to create content for mHealth behavior interventions: (1) determine target behavior, (2) ground the intervention in behavior change theory, (3) design the intervention structure, (4) input intervention structure and behavior change constructs into ChatGPT, (5) revise the ChatGPT response, and (6) customize the response to be used in the intervention.

CONCLUSIONS

ChatGPT offers a remarkable opportunity for rapid content creation in the context of an mHealth JITAI. Although our case study demonstrated that ChatGPT was acceptable, it is essential to approach its use, along with other language models, with caution. Before delivering content to population groups, expert review is crucial to ensure accuracy and relevancy. Future research and application of these guidelines are imperative as we deepen our understanding of ChatGPT and its interactions with human input.

The whack-a-mole governance challenge for AI-enabled synthetic biology: literature review and emerging frameworks

AI-enabled synthetic biology has tremendous potential but also significantly increases biorisks and brings about a new set of dual use concerns. The picture is complicated given the vast innovations envisioned to emerge by combining emerging technologies, as AI-enabled synthetic biology potentially scales up bioengineering into industrial biomanufacturing. However, the literature review indicates that goals such as maintaining a reasonable scope for innovation, or more ambitiously to foster a huge bioeconomy do not necessarily contrast with biosafety, but need to go hand in hand. This paper presents a literature review of the issues and describes emerging frameworks for policy and practice that transverse the options of command-and-control, stewardship, bottom-up, and laissez-faire governance. How to achieve early warning systems that enable prevention and mitigation of future AI-enabled biohazards from the lab, from deliberate misuse, or from the public realm, will constantly need to evolve, and adaptive, interactive approaches should emerge. Although biorisk is subject to an established governance regime, and scientists generally adhere to biosafety protocols, even experimental, but legitimate use by scientists could lead to unexpected developments. Recent advances in chatbots enabled by generative AI have revived fears that advanced biological insight can more easily get into the hands of malignant individuals or organizations. Given these sets of issues, society needs to rethink how AI-enabled synthetic biology should be governed. The suggested way to visualize the challenge at hand is whack-a-mole governance, although the emerging solutions are perhaps not so different either.

Ethical Concerns About ChatGPT in Healthcare: A Useful Tool or the Tombstone of Original and Reflective Thinking?

Artificial intelligence (AI), the uprising technology of computer science aiming to create digital systems with human behavior and intelligence, seems to have invaded almost every field of modern life. Launched in November 2022, ChatGPT (Chat Generative Pre-trained Transformer) is a textual AI application capable of creating human-like responses characterized by original language and high coherence. Although AI-based language models have demonstrated impressive capabilities in healthcare, ChatGPT has received controversial annotations from the scientific and academic communities. This chatbot already appears to have a massive impact as an educational tool for healthcare professionals and transformative potential for clinical practice and could lead to dramatic changes in scientific research. Nevertheless, rational concerns were raised regarding whether the pre-trained, AI-generated text would be a menace not only for original thinking and new scientific ideas but also for academic and research integrity, as it gets more and more difficult to distinguish its AI origin due to the coherence and fluency of the produced text. This short review aims to summarize the potential applications and the consequential implications of ChatGPT in the three critical pillars of medicine: education, research, and clinical practice. In addition, this paper discusses whether the current use of this chatbot is in compliance with the ethical principles for the safe use of AI in healthcare, as determined by the World Health Organization. Finally, this review highlights the need for an updated ethical framework and the increased vigilance of healthcare stakeholders to harvest the potential benefits and limit the imminent dangers of this new innovative technology.

Sensors for Digital Transformation in Smart Forestry

Smart forestry, an innovative approach leveraging artificial intelligence (AI), aims to enhance forest management while minimizing the environmental impact. The efficacy of AI in this domain is contingent upon the availability of extensive, high-quality data, underscoring the pivotal role of sensor-based data acquisition in the digital transformation of forestry. However, the complexity and challenging conditions of forest environments often impede data collection efforts. Achieving the full potential of smart forestry necessitates a comprehensive integration of sensor technologies throughout the process chain, ensuring the production of standardized, high-quality data essential for AI applications. This paper highlights the symbiotic relationship between human expertise and the digital transformation in forestry, particularly under challenging conditions. We emphasize the human-in-the-loop approach, which allows experts to directly influence data generation, enhancing adaptability and effectiveness in diverse scenarios. A critical aspect of this integration is the deployment of autonomous robotic systems in forests, functioning both as data collectors and processing hubs. These systems are instrumental in facilitating sensor integration and generating substantial volumes of quality data. We present our universal sensor platform, detailing our experiences and the critical importance of the initial phase in digital transformation-the generation of comprehensive, high-quality data. The selection of appropriate sensors is a key factor in this process, and our findings underscore its significance in advancing smart forestry.

ChatGPT sits the DFPH exam: large language model performance and potential to support public health learning

BACKGROUND

Artificial intelligence-based large language models, like ChatGPT, have been rapidly assessed for both risks and potential in health-related assessment and learning. However, their applications in public health professional exams have not yet been studied. We evaluated the performance of ChatGPT in part of the Faculty of Public Health's Diplomat exam (DFPH).

METHODS

ChatGPT was provided with a bank of 119 publicly available DFPH question parts from past papers. Its performance was assessed by two active DFPH examiners. The degree of insight and level of understanding apparently displayed by ChatGPT was also assessed.

RESULTS

ChatGPT passed 3 of 4 papers, surpassing the current pass rate. It performed best on questions relating to research methods. Its answers had a high floor. Examiners identified ChatGPT answers with 73.6% accuracy and human answers with 28.6% accuracy. ChatGPT provided a mean of 3.6 unique insights per question and appeared to demonstrate a required level of learning on 71.4% of occasions.

CONCLUSIONS

Large language models have rapidly increasing potential as a learning tool in public health education. However, their factual fallibility and the difficulty of distinguishing their responses from that of humans pose potential threats to teaching and learning.

Unlocking biomedical data sharing: A structured approach with digital twins and artificial intelligence (AI) for open health sciences

Objective

Data sharing promotes the scientific progress. However, not all data can be shared freely due to privacy issues. This work is intended to foster FAIR sharing of sensitive data exemplary in the biomedical domain, via an integrated computational approach for utilizing and enriching individual datasets by scientists without coding experience.

Methods

We present an in silico pipeline for openly sharing controlled materials by generating synthetic data. Additionally, it addresses the issue of inexperience to computational methods in a non-IT-affine domain by making use of a cyberinfrastructure that runs and enables sharing of computational notebooks without the need of local software installation. The use of a digital twin based on cancer datasets serves as exemplary use case for making biomedical data openly available. Quantitative and qualitative validation of model output as well as a study on user experience are conducted.

Results

The metadata approach describes generalizable descriptors for computational models, and outlines how to profit from existing data resources for validating computational models. The use of a virtual lab book cooperatively developed using a cloud-based data management and analysis system functions as showcase enabling easy interaction between users. Qualitative testing revealed a necessity for comprehensive guidelines furthering acceptance by various users.

Conclusion

The introduced framework presents an integrated approach for data generation and interpolating incomplete data, promoting Open Science through reproducibility of results and methods. The system can be expanded from the biomedical to any other domain while future studies integrating an enhanced graphical user interface could increase interdisciplinary applicability.

Explainable deep learning enhances robust and reliable real-time monitoring of a chromatographic protein A capture step

The application of model-based real-time monitoring in biopharmaceutical production is a major step toward quality-by-design and the fundament for model predictive control. Data-driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state-of-the-art modeling algorithms are employed, and (iii) the input-output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data-driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high-quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion-based methods can be used to gain an understanding of dependencies learned by one of the most complex data-driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data-driven models in biopharmaceutical manufacturing.

Darwinian evolution has become dogma; AI can rescue what is salvageable

Artificial Intelligence (AI), as an academic discipline, is traceable to the mid-1950s but it is currently exploding in applications with successes and concerns. AI can be defined as intelligence demonstrated by computers, with intelligence difficult to define but it must include concepts of ability to learn, reason, and generalize from a vast amount of information and, we propose, to infer meaning. The type of AI known as general AI, has strong, but unrealized potential both for assessing and also for solving major problems with the scientific theory of Darwinian evolution, including its modern variants and for origin of life studies. Specifically, AI should be applied first to evaluate the strengths and weaknesses of the assumptions and empirical information underpinning theories of the origin of life and probability of its evolution. AI should then be applied to assess the scientific validity of the theory of how abundant life came to be on earth.

Shedding light on the black box of a neural network used to detect prostate cancer in whole slide images by occlusion-based explainability

Diagnostic histopathology faces increasing demands due to aging populations and expanding healthcare programs. Semi-automated diagnostic systems employing deep learning methods are one approach to alleviate this pressure. The learning models for histopathology are inherently complex and opaque from the user's perspective. Hence different methods have been developed to interpret their behavior. However, relatively limited attention has been devoted to the connection between interpretation methods and the knowledge of experienced pathologists. The main contribution of this paper is a method for comparing morphological patterns used by expert pathologists to detect cancer with the patterns identified as important for inference of learning models. Given the patch-based nature of processing large-scale histopathological imaging, we have been able to show statistically that the VGG16 model could utilize all the structures that are observable by the pathologist, given the patch size and scan resolution. The results show that the neural network approach to recognizing prostatic cancer is similar to that of a pathologist at medium optical resolution. The saliency maps identified several prevailing histomorphological features characterizing carcinoma, e.g., single-layered epithelium, small lumina, and hyperchromatic nuclei with halo. A convincing finding was the recognition of their mimickers in non-neoplastic tissue. The method can also identify differences, i.e., standard patterns not used by the learning models and new patterns not yet used by pathologists. Saliency maps provide added value for automated digital pathology to analyze and fine-tune deep learning systems and improve trust in computer-based decisions.

The opportunities and challenges of adopting ChatGPT in medical research

Purpose

This study aims to investigate the opportunities and challenges of adopting ChatGPT in medical research.

Methods

A qualitative approach with focus groups is adopted in this study. A total of 62 participants including academic researchers from different streams in medicine and eHealth, participated in this study.

Results

A total of five themes with 16 sub-themes related to the opportunities; and a total of five themes with 12 sub-themes related to the challenges were identified. The major opportunities include improved data collection and analysis, improved communication and accessibility, and support for researchers in multiple streams of medical research. The major challenges identified were limitations of training data leading to bias, ethical issues, technical limitations, and limitations in data collection and analysis.

Conclusion

Although ChatGPT can be used as a potential tool in medical research, there is a need for further evidence to generalize its impact on the different research activities.

Bridging artificial intelligence and fucoxanthin for the recovery and quantification from microalgae

Fucoxanthin is a carotenoid that possesses various beneficial medicinal properties for human well-being. However, the current extraction technologies and quantification techniques are still lacking in terms of cost validation, high energy consumption, long extraction time, and low yield production. To date, artificial intelligence (AI) models can assist and improvise the bottleneck of fucoxanthin extraction and quantification process by establishing new technologies and processes which involve big data, digitalization, and automation for efficiency fucoxanthin production. This review highlights the application of AI models such as artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS), capable of learning patterns and relationships from large datasets, capturing non-linearity, and predicting optimal conditions that significantly impact the fucoxanthin extraction yield. On top of that, combining metaheuristic algorithm such as genetic algorithm (GA) can further improve the parameter space and discovery of optimal conditions of ANN and ANFIS models, which results in high R2 accuracy ranging from 98.28% to 99.60% after optimization. Besides, AI models such as support vector machine (SVM), convolutional neural networks (CNNs), and ANN have been leveraged for the quantification of fucoxanthin, either computer vision based on color space of images or regression analysis based on statistical data. The findings are reliable when modeling for the concentration of pigments with high R2 accuracy ranging from 66.0% - 99.2%. This review paper has reviewed the feasibility and potential of AI for the extraction and quantification purposes, which can reduce the cost, accelerate the fucoxanthin yields, and development of fucoxanthin-based products.

An overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity

The artificial intelligence (AI) tool ChatGPT, which is based on a large language model (LLM), is gaining popularity in academic institutions, notably in the medical field. This article provides a brief overview of the capabilities of ChatGPT for medical writing and its implications for academic integrity. It provides a list of AI generative tools, common use of AI generative tools for medical writing, and provides a list of AI generative text detection tools. It also provides recommendations for policymakers, information professionals, and medical faculty for the constructive use of AI generative tools and related technology. It also highlights the role of health sciences librarians and educators in protecting students from generating text through ChatGPT in their academic work.